U.S. patent application number 17/828132 was filed with the patent office on 2022-09-15 for dynamic chronometry data orientation.
The applicant listed for this patent is ThoughtSpot, Inc.. Invention is credited to Archit Bansal, Donko Donjerkovic, Simranjyot Singh Gill, Sanchit Gupta, Rakesh Kothari, Tushar Mahale, Vishwas Sharma, Siva Singaram.
Application Number | 20220292094 17/828132 |
Document ID | / |
Family ID | 1000006364552 |
Filed Date | 2022-09-15 |
United States Patent
Application |
20220292094 |
Kind Code |
A1 |
Mahale; Tushar ; et
al. |
September 15, 2022 |
Dynamic Chronometry Data Orientation
Abstract
Operating a low-latency data access and analysis system using
domain-specific chronometry may include obtaining, in the
low-latency data access and analysis system, data expressing usage
intent with respect to the low-latency data access and analysis
system, in response to obtaining the data expressing usage intent,
obtaining ontological data for a chronometric object in the
low-latency data access and analysis system indicated by the data
expressing usage intent, identifying a chronometry dataset from a
plurality of chronometry datasets, wherein the plurality of
chronometry datasets includes a domain-specific chronometry dataset
and a canonical chronometry dataset, obtaining results data in
accordance with the chronometry dataset and the chronometric
object, generating output data representing the results data in
accordance with the chronometry dataset, and outputting the output
data for presentation via a user interface.
Inventors: |
Mahale; Tushar; (Milpitas,
CA) ; Singaram; Siva; (San Jose, CA) ; Sharma;
Vishwas; (Foster City, CA) ; Donjerkovic; Donko;
(San Mateo, CA) ; Gill; Simranjyot Singh; (San
Jose, CA) ; Bansal; Archit; (Cupertino, CA) ;
Kothari; Rakesh; (San Jose, CA) ; Gupta; Sanchit;
(Sunnyvale, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ThoughtSpot, Inc. |
San Jose |
CA |
US |
|
|
Family ID: |
1000006364552 |
Appl. No.: |
17/828132 |
Filed: |
May 31, 2022 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16834163 |
Mar 30, 2020 |
11372872 |
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17828132 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/2477 20190101;
G06F 16/2228 20190101; G06F 16/248 20190101; G06F 16/221
20190101 |
International
Class: |
G06F 16/2458 20060101
G06F016/2458; G06F 16/22 20060101 G06F016/22; G06F 16/248 20060101
G06F016/248 |
Claims
1. A method for use in a low-latency data access and analysis
system, the method comprising: obtaining, by the low-latency data
access and analysis system, data expressing usage intent with
respect to the low-latency data access and analysis system; in
response to obtaining the data expressing usage intent, obtaining
ontological data for a chronometric object in the low-latency data
access and analysis system indicated by the data expressing usage
intent; identifying a chronometry dataset from a plurality of
chronometry datasets, wherein the plurality of chronometry datasets
includes a domain-specific chronometry dataset and a canonical
chronometry dataset; obtaining results data in accordance with the
chronometry dataset and the chronometric object; generating output
data representing the results data in accordance with the
chronometry dataset; and outputting the output data for
presentation via a user interface.
2. The method of claim 1, wherein: the canonical chronometry
dataset describes a Gregorian calendar; and the domain-specific
chronometry dataset describes a chronometric unit such that a
temporal location expressed with reference to the chronometric unit
and indicative of an epoch value differs from a temporal location
indicative of the epoch value and expressed in accordance with the
canonical chronometry dataset.
3. The method of claim 1, wherein identifying the chronometry
dataset includes: determining whether the data expressing usage
intent includes data indicating an expressly identified chronometry
dataset; and in response to a determination that the data
expressing usage intent includes data indicating an expressly
identified chronometry dataset, identifying the expressly
identified chronometry dataset as the chronometry dataset.
4. The method of claim 3, wherein determining whether the data
expressing usage intent includes the data indicating the expressly
identified chronometry dataset includes: traversing a token index
based on the data expressing usage intent.
5. The method of claim 4, wherein obtaining the results data
includes: obtaining a probable chronometric object by traversing a
finite state machine representing the chronometry dataset, wherein
the data expressing usage intent omits data expressly indicating
the probable chronometric object; and including data indicating the
probable chronometric object in the results data.
6. The method of claim 5, wherein obtaining the probable
chronometric object includes: in response identifying token
ambiguity for the data expressing usage intent: identifying a
candidate token resolution token using the chronometry dataset; and
omitting identifying a candidate token resolution token using a
chronometry dataset other than the chronometry dataset.
7. The method of claim 5, wherein obtaining the probable
chronometric object includes: in response identifying phrase
ambiguity for the data expressing usage intent, identifying a
candidate phrase completion token agnostic of the chronometry
dataset.
8. The method of claim 3, wherein obtaining the ontological data
for the chronometric object includes: in response to a
determination that the data expressing usage intent includes an
analytical object identifier, obtaining the ontological data based
on the analytical object identifier; and in response to a
determination that the data expressing usage intent omits the
analytical object identifier: identifying a chronometric token by
traversing a token index based on the data expressing usage intent,
the chronometric token representing the chronometric object; and
obtaining the ontological data based on the chronometric token.
9. The method of claim 8, wherein obtaining the results data
includes: in response to the determination that the data expressing
usage intent includes the analytical object identifier, obtaining
ontological data for an analytical object identified by the
analytical object identifier; and in response to the determination
that the data expressing usage intent omits the analytical object
identifier, generating an analytical object representing the data
expressing usage intent, wherein generating the analytical object
includes generating ontological data representing the analytical
object.
10. The method of claim 9, wherein generating the ontological data
representing the analytical object includes: in response to a
determination that the data expressing usage intent includes data
indicating an expressly identified chronometry dataset, including
data identifying the expressly identified chronometry dataset as
the chronometry dataset in the ontological data representing the
analytical object; and in response to a determination that the data
expressing usage intent omits data indicating an expressly
identified chronometry dataset, omitting data identifying the
chronometry dataset from the ontological data representing the
analytical object.
11. The method of claim 10, wherein obtaining the results data
includes: generating a data-query based on the ontological data for
the analytical object and the chronometry dataset; issuing the
data-query to a database of the low-latency data access and
analysis system; and obtaining the results data in accordance with
the data-query, wherein obtaining the results data in accordance
with the data-query include obtaining the results data in response
to query execution by the database in accordance with the
data-query.
12. The method of claim 11, wherein generating the data-query
includes: determining whether the ontological data representing the
analytical object includes data identifying the chronometry
dataset; and in response to a determination that the ontological
data representing the analytical object omits the data identifying
the chronometry dataset: determining whether data indicating an
assigned chronometry dataset for the chronometric object is
available, wherein the plurality of chronometry datasets includes
the assigned chronometry dataset; in response to a determination
that the data indicating the assigned chronometry dataset is
available, including data identifying the assigned chronometry
dataset as the chronometry dataset in the ontological data
representing the analytical object; and in response to a
determination that the data indicating the assigned chronometry
dataset is unavailable: determining whether data indicating a
primary chronometry dataset is available, wherein the plurality of
chronometry datasets includes the primary chronometry dataset; in
response to a determination that the data indicating the primary
chronometry dataset is available, including data identifying the
primary chronometry dataset as the chronometry dataset in the
ontological data representing the analytical object; and in
response to a determination that the data indicating the primary
chronometry dataset is unavailable, including data identifying the
canonical chronometry dataset as the chronometry dataset in the
ontological data representing the analytical object.
13. The method of claim 12, wherein determining whether the data
indicating the assigned chronometry dataset is available includes:
determining whether the ontological data for the chronometric
object includes data indicating that the assigned chronometry
dataset is assigned to the chronometric object; in response to a
determination that the ontological data for the chronometric object
includes the data indicating that the assigned chronometry dataset
is assigned to the chronometric object, determining that the data
indicating the assigned chronometry dataset is available; and in
response to a determination that the ontological data for the
chronometric object omits the data indicating that the assigned
chronometry dataset is assigned to the chronometric object,
determining that the data indicating the assigned chronometry
dataset is unavailable.
14. The method of claim 12, wherein generating the data-query
includes: generating the data-query such that obtaining the results
data in accordance with the data-query includes identifying an
epoch value from a column in the database indicated by the
chronometric object as included in data associated with an instance
of a chronometric unit that is determined in accordance with the
chronometry dataset.
15. The method of claim 12, wherein generating the data-query
includes: generating the data-query such that obtaining the results
data in accordance with the data-query includes obtaining: a value
of an instance of a chronometric unit that is identified in
accordance with the chronometry dataset; and a descriptor for the
instance of the chronometric unit that is identified in accordance
with the chronometry dataset.
16. The method of claim 12, further comprising: in response to the
determination that the ontological data representing the analytical
object omits the data identifying the chronometry dataset,
obtaining ontological data for a second chronometric object in the
low-latency data access and analysis system indicated by the data
expressing usage intent.
17. The method of claim 16, wherein generating the data-query
includes: determining whether data indicating a second assigned
chronometry dataset for the second chronometric object is
available, wherein the plurality of chronometry datasets includes
the second assigned chronometry dataset; in response to a
determination that the data indicating the second assigned
chronometry dataset is available, including data identifying the
second assigned chronometry dataset as a chronometry dataset for
the second chronometric object in the ontological data representing
the analytical object; and in response to a determination that the
data indicating the second assigned chronometry dataset is
unavailable: determining whether the data indicating the primary
chronometry dataset is available; in response to a determination
that the data indicating the primary chronometry dataset is
available, including data identifying the primary chronometry
dataset as the chronometry dataset for the second chronometric
object in the ontological data representing the analytical object;
and in response to a determination that the data indicating the
primary chronometry dataset is unavailable, including data
identifying the canonical chronometry dataset as the chronometry
dataset for the second chronometric object in the ontological data
representing the analytical object.
18. The method of claim 17, wherein: generating the data-query
includes: generating a first portion of the data-query in
accordance with the chronometry dataset and the chronometric
object; and generating a second portion of the data-query in
accordance with the chronometry dataset for the second chronometric
object and the second chronometric object; and generating the
output data includes: generating a first portion of the output data
in accordance with the chronometry dataset; and generating a second
portion of the output data in accordance with the chronometry
dataset for the second chronometric object.
19. A method for use in a low-latency data access and analysis
system, the method comprising: obtaining, in the low-latency data
access and analysis system, data expressing usage intent with
respect to the low-latency data access and analysis system; in
response to obtaining the data expressing usage intent, obtaining a
resolved-request corresponding to the data expressing usage intent,
wherein obtaining the resolved-request includes: obtaining
ontological data for a chronometric object in the low-latency data
access and analysis system indicated by the data expressing usage
intent; and identifying a chronometry dataset from a plurality of
chronometry datasets, wherein the plurality of chronometry datasets
includes a domain-specific chronometry dataset and a canonical
chronometry dataset, wherein: the canonical chronometry dataset
describes a Gregorian calendar; and the domain-specific chronometry
dataset describes a chronometric unit such that a temporal location
expressed with reference to the chronometric unit and indicative of
an epoch value differs from a temporal location indicative of the
epoch value and expressed in accordance with the canonical
chronometry dataset; in response to obtaining the resolved-request,
obtaining results data in accordance with the resolved-request; in
response to obtaining the results data, generating output data
representing the results data in accordance with the chronometry
dataset; and outputting the output data for presentation via a user
interface.
20. A method for use in a low-latency data access and analysis
system, the method comprising: obtaining, in the low-latency data
access and analysis system, data expressing usage intent with
respect to the low-latency data access and analysis system;
obtaining a resolved-request corresponding to the data expressing
usage intent in accordance with a chronometry dataset, wherein
obtaining the resolved-request includes identifying the chronometry
dataset from a plurality of chronometry datasets, wherein the
plurality of chronometry datasets includes a domain-specific
chronometry dataset and a canonical chronometry dataset, wherein:
the canonical chronometry dataset describes a Gregorian calendar;
and the domain-specific chronometry dataset describes a
chronometric unit such that a temporal location expressed with
reference to the chronometric unit and indicative of an epoch value
differs from a temporal location indicative of the epoch value and
expressed in accordance with the canonical chronometry dataset;
obtaining results data responsive to the resolved-request and in
accordance with the chronometry dataset; generating output data
representing the results data in accordance with the chronometry
dataset; and outputting the output data for presentation via a user
interface.
Description
CROSS-REFERENCE TO RELATED APPLICATION(S)
[0001] This application claims priority to and the benefit of U.S.
application patent Ser. No. 16/834,163, filed Mar. 30, 2020, the
entire disclosure of which is hereby incorporated by reference.
BACKGROUND
[0002] Advances in computer storage and database technology have
led to exponential growth of the amount of data being created.
Businesses are overwhelmed by the volume of the data stored in
their computer systems. Existing database analytic tools are
inefficient, costly to utilize, and/or require substantial
configuration and training.
SUMMARY
[0003] Disclosed herein are implementations of dynamic chronometry
data orientation in a low-latency data access and analysis
system.
[0004] An aspect of the disclosure is a method of dynamic
chronometry data orientation for use in a low-latency data access
and analysis system. The method includes obtaining, in the
low-latency data access and analysis system, data expressing usage
intent with respect to the low-latency data access and analysis
system, in response to obtaining the data expressing usage intent,
obtaining ontological data for a chronometric object in the
low-latency data access and analysis system indicated by the data
expressing usage intent, identifying a chronometry dataset from a
plurality of chronometry datasets, wherein the plurality of
chronometry datasets includes a domain-specific chronometry dataset
and a canonical chronometry dataset, obtaining results data in
accordance with the chronometry dataset and the chronometric
object, generating output data representing the results data in
accordance with the chronometry dataset, and outputting the output
data for presentation via a user interface.
[0005] Another aspect of the disclosure is a method of dynamic
chronometry data orientation for use in a low-latency data access
and analysis system. The method includes obtaining, in the
low-latency data access and analysis system, data expressing usage
intent with respect to the low-latency data access and analysis
system, in response to obtaining the data expressing usage intent,
obtaining a resolved-request corresponding to the data expressing
usage intent,
[0006] Obtaining the resolved-request includes obtaining
ontological data for a chronometric object in the low-latency data
access and analysis system indicated by the data expressing usage
intent, and identifying a chronometry dataset from a plurality of
chronometry datasets, wherein the plurality of chronometry datasets
includes a domain-specific chronometry dataset and a canonical
chronometry dataset. The canonical chronometry dataset describes a
Gregorian calendar and the domain-specific chronometry dataset
describes a chronometric unit such that a temporal location
expressed with reference to the chronometric unit and indicative of
an epoch value differs from a temporal location indicative of the
epoch value and expressed in accordance with the canonical
chronometry dataset. The method includes, in response to obtaining
the resolved-request, obtaining results data in accordance with the
resolved-request. The method includes, in response to obtaining the
results data, generating output data representing the results data
in accordance with the chronometry dataset. The method includes
outputting the output data for presentation via a user
interface
[0007] Another aspect of the disclosure is a method of dynamic
chronometry data orientation for use in a low-latency data access
and analysis system. The method includes obtaining, in the
low-latency data access and analysis system, data expressing usage
intent with respect to the low-latency data access and analysis
system, obtaining a resolved-request corresponding to the data
expressing usage intent in accordance with a chronometry dataset,
wherein obtaining the resolved-request includes identifying the
chronometry dataset from a plurality of chronometry datasets,
wherein the plurality of chronometry datasets includes a
domain-specific chronometry dataset and a canonical chronometry
dataset, obtaining results data responsive to the resolved-request
and in accordance with the chronometry dataset, generating output
data representing the results data in accordance with the
chronometry dataset, and outputting the output data for
presentation via a user interface. The canonical chronometry
dataset describes a Gregorian calendar and the domain-specific
chronometry dataset describes a chronometric unit such that a
temporal location expressed with reference to the chronometric unit
and indicative of an epoch value differs from a temporal location
indicative of the epoch value and expressed in accordance with the
canonical chronometry dataset.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] The disclosure is best understood from the following
detailed description when read in conjunction with the accompanying
drawings. It is emphasized that, according to common practice, the
various features of the drawings are not to-scale. On the contrary,
the dimensions of the various features are arbitrarily expanded or
reduced for clarity.
[0009] FIG. 1 is a block diagram of an example of a computing
device.
[0010] FIG. 2 is a block diagram of an example of a computing
system.
[0011] FIG. 3 is a block diagram of an example of a low-latency
data access and analysis system.
[0012] FIG. 4 is a flow diagram of a method of analytical data
access with system-defined chronometry in a low-latency data access
and analysis system.
[0013] FIG. 5 is a flow diagram of a method of analytical data
access with domain-specific chronometry in a low-latency data
access and analysis system.
[0014] FIG. 6 is a flow diagram of a method of obtaining
domain-specific chronometry in a low-latency data access and
analysis system.
[0015] FIG. 7 is a flow diagram of a method of chronometry
determination for data-query generation for analytical data access
with available domain-specific chronometry in a low-latency data
access and analysis system.
DETAILED DESCRIPTION
[0016] Businesses and other organizations store large amounts of
data, such as business records, transaction records, and the like,
in data storage systems, such as relational database systems that
store data as records, or rows, having values, or fields,
corresponding to respective columns in tables that can be
interrelated using key values. Databases structures are often
normalized or otherwise organized to maximize data density and to
maximize transactional data operations at the expense of increased
complexity and reduced accessibility for analysis. Individual
records and tables may have little or no utility without
substantial correlation, interpretation, and analysis. The
complexity of these data structures and the large volumes of data
that can be stored therein limit the accessibility of the data and
require substantial skilled human resources to code procedures and
tools that allow business users to access useful data. The tools
that are available for accessing these systems are limited to
outputting data expressly requested by the users and lack the
capability to identify and prioritize data other than the data
expressly requested. Useful data, such as data aggregations,
patterns, and statistical anomalies that would not be available in
smaller data sets (e.g., 10,000 rows of data), and may not be
apparent to human users, may be derivable using the large volume of
data (e.g., millions or billions of rows) stored in complex data
storage systems, such as relational database systems, and may be
inaccessible due to the complexity and limitations of the data
storage systems.
[0017] Some analytical data access systems may be based on the use
of a system-defined chronometry for representing temporal data,
such as time and date data. For example, the system-defined
chronometry may correspond with a Gregorian calendar. The
system-defined chronometry may be implemented as program
instructions, which may include aspects that are defined by the
operating environment of the analytical data access, or low-latency
database analysis, system, such as by a hardware component, an
operating system, or a combination thereof. Analytical data access
systems based on the use of a system-defined chronometry may omit
support for representing data using chronometry or calendaring
systems that vary from the system-defined, e.g., Gregorian,
chronometry. Including support other chronometry, such as a
domain-specific or user-defined chronometry may be unavailable or
may be based on inefficient and expensive manual coding.
[0018] Analytical data access systems including dynamic chronometry
data orientation may include the use of domain-specific
chronometry, such as instead of, in addition to, or in combination
with, the use of system-defined chronometry. Analytical data access
systems including dynamic chronometry data orientation may obtain
chronometry configuration data defining or describing a
domain-specific chronometry. For example, analytical data access
systems including dynamic chronometry data orientation may include
an interface, such as a user interface, for defining or describing
the domain-specific chronometry configuration data. Analytical data
access systems including dynamic chronometry data orientation may
generate a domain-specific chronometry dataset in accordance with
the chronometry configuration data. The domain-specific chronometry
dataset may be stored in the analytical data access, or low-latency
database analysis, system. Analytical data access systems including
dynamic chronometry data orientation may represent temporal data,
such as time and date data, using domain-specific chronometry.
Analytical data access systems including dynamic chronometry data
orientation may improve the efficiency, accuracy, and utility of
analytical data access systems by dynamically representing temporal
data, such as time and date data, with respect to domain-specific,
or user-defined, chronometry.
[0019] FIG. 1 is a block diagram of an example of a computing
device 1000. One or more aspects of this disclosure may be
implemented using the computing device 1000. The computing device
1000 includes a processor 1100, static memory 1200, low-latency
memory 1300, an electronic communication unit 1400, a user
interface 1500, a bus 1600, and a power source 1700. Although shown
as a single unit, any one or more element of the computing device
1000 may be integrated into any number of separate physical units.
For example, the low-latency memory 1300 and the processor 1100 may
be integrated in a first physical unit and the user interface 1500
may be integrated in a second physical unit. Although not shown in
FIG. 1, the computing device 1000 may include other aspects, such
as an enclosure or one or more sensors.
[0020] The computing device 1000 may be a stationary computing
device, such as a personal computer (PC), a server, a workstation,
a minicomputer, or a mainframe computer; or a mobile computing
device, such as a mobile telephone, a personal digital assistant
(PDA), a laptop, or a tablet PC.
[0021] The processor 1100 may include any device or combination of
devices capable of manipulating or processing a signal or other
information, including optical processors, quantum processors,
molecular processors, or a combination thereof. The processor 1100
may be a central processing unit (CPU), such as a microprocessor,
and may include one or more processing units, which may
respectively include one or more processing cores. The processor
1100 may include multiple interconnected processors. For example,
the multiple processors may be hardwired or networked, including
wirelessly networked. In some implementations, the operations of
the processor 1100 may be distributed across multiple physical
devices or units that may be coupled directly or across a network.
In some implementations, the processor 1100 may include a cache, or
cache memory, for internal storage of operating data or
instructions. The processor 1100 may include one or more special
purpose processors, one or more digital signal processor (DSP), one
or more microprocessors, one or more controllers, one or more
microcontrollers, one or more integrated circuits, one or more an
Application Specific Integrated Circuits, one or more Field
Programmable Gate Array, one or more programmable logic arrays, one
or more programmable logic controllers, firmware, one or more state
machines, or any combination thereof.
[0022] The processor 1100 may be operatively coupled with the
static memory 1200, the low-latency memory 1300, the electronic
communication unit 1400, the user interface 1500, the bus 1600, the
power source 1700, or any combination thereof. The processor may
execute, which may include controlling, such as by sending
electronic signals to, receiving electronic signals from, or both,
the static memory 1200, the low-latency memory 1300, the electronic
communication unit 1400, the user interface 1500, the bus 1600, the
power source 1700, or any combination thereof to execute,
instructions, programs, code, applications, or the like, which may
include executing one or more aspects of an operating system, and
which may include executing one or more instructions to perform one
or more aspects described herein, alone or in combination with one
or more other processors.
[0023] The static memory 1200 is coupled to the processor 1100 via
the bus 1600 and may include non-volatile memory, such as a disk
drive, or any form of non-volatile memory capable of persistent
electronic information storage, such as in the absence of an active
power supply. Although shown as a single block in FIG. 1, the
static memory 1200 may be implemented as multiple logical or
physical units.
[0024] The static memory 1200 may store executable instructions or
data, such as application data, an operating system, or a
combination thereof, for access by the processor 1100. The
executable instructions may be organized into programmable modules
or algorithms, functional programs, codes, code segments, or
combinations thereof to perform one or more aspects, features, or
elements described herein. The application data may include, for
example, user files, database catalogs, configuration information,
or a combination thereof. The operating system may be, for example,
a desktop or laptop operating system; an operating system for a
mobile device, such as a smartphone or tablet device; or an
operating system for a large device, such as a mainframe
computer.
[0025] The low-latency memory 1300 is coupled to the processor 1100
via the bus 1600 and may include any storage medium with
low-latency data access including, for example, DRAM modules such
as DDR SDRAM, Phase-Change Memory (PCM), flash memory, or a
solid-state drive. Although shown as a single block in FIG. 1, the
low-latency memory 1300 may be implemented as multiple logical or
physical units. Other configurations may be used. For example,
low-latency memory 1300, or a portion thereof, and processor 1100
may be combined, such as by using a system on a chip design.
[0026] The low-latency memory 1300 may store executable
instructions or data, such as application data for low-latency
access by the processor 1100. The executable instructions may
include, for example, one or more application programs, that may be
executed by the processor 1100. The executable instructions may be
organized into programmable modules or algorithms, functional
programs, codes, code segments, and/or combinations thereof to
perform various functions described herein.
[0027] The low-latency memory 1300 may be used to store data that
is analyzed or processed using the systems or methods described
herein. For example, storage of some or all data in low-latency
memory 1300 instead of static memory 1200 may improve the execution
speed of the systems and methods described herein by permitting
access to data more quickly by an order of magnitude or greater
(e.g., nanoseconds instead of microseconds).
[0028] The electronic communication unit 1400 is coupled to the
processor 1100 via the bus 1600. The electronic communication unit
1400 may include one or more transceivers. The electronic
communication unit 1400 may, for example, provide a connection or
link to a network via a network interface. The network interface
may be a wired network interface, such as Ethernet, or a wireless
network interface. For example, the computing device 1000 may
communicate with other devices via the electronic communication
unit 1400 and the network interface using one or more network
protocols, such as Ethernet, Transmission Control Protocol/Internet
Protocol (TCP/IP), power line communication (PLC), Wi-Fi, infrared,
ultra violet (UV), visible light, fiber optic, wire line, general
packet radio service (GPRS), Global System for Mobile
communications (GSM), code-division multiple access (CDMA),
Long-Term Evolution (LTE), or other suitable protocols.
[0029] The user interface 1500 may include any unit capable of
interfacing with a human user, such as a virtual or physical
keypad, a touchpad, a display, a touch display, a speaker, a
microphone, a video camera, a sensor, a printer, or any combination
thereof. For example, a keypad can convert physical input of force
applied to a key to an electrical signal that can be interpreted by
computing device 1000. In another example, a display can convert
electrical signals output by computing device 1000 to light. The
purpose of such devices may be to permit interaction with a human
user, for example by accepting input from the human user and
providing output back to the human user. The user interface 1500
may include a display; a positional input device, such as a mouse,
touchpad, touchscreen, or the like; a keyboard; or any other human
and machine interface device. The user interface 1500 may be
coupled to the processor 1100 via the bus 1600. In some
implementations, the user interface 1500 can include a display,
which can be a liquid crystal display (LCD), a cathode-ray tube
(CRT), a light emitting diode (LED) display, an organic light
emitting diode (OLED) display, an active matrix organic light
emitting diode (AMOLED), or other suitable display. In some
implementations, the user interface 1500, or a portion thereof, may
be part of another computing device (not shown). For example, a
physical user interface, or a portion thereof, may be omitted from
the computing device 1000 and a remote or virtual interface may be
used, such as via the electronic communication unit 1400.
[0030] The bus 1600 is coupled to the static memory 1200, the
low-latency memory 1300, the electronic communication unit 1400,
the user interface 1500, and the power source 1700. Although a
single bus is shown in FIG. 1, the bus 1600 may include multiple
buses, which may be connected, such as via bridges, controllers, or
adapters.
[0031] The power source 1700 provides energy to operate the
computing device 1000. The power source 1700 may be a
general-purpose alternating-current (AC) electric power supply, or
power supply interface, such as an interface to a household power
source. In some implementations, the power source 1700 may be a
single use battery or a rechargeable battery to allow the computing
device 1000 to operate independently of an external power
distribution system. For example, the power source 1700 may include
a wired power source; one or more dry cell batteries, such as
nickel-cadmium (NiCad), nickel-zinc (NiZn), nickel metal hydride
(NiMH), lithium-ion (Li-ion); solar cells; fuel cells; or any other
device capable of powering the computing device 1000.
[0032] FIG. 2 is a block diagram of an example of a computing
system 2000. As shown, the computing system 2000 includes an
external data source portion 2100, an internal database analysis
portion 2200, and a system interface portion 2300. The computing
system 2000 may include other elements not shown in FIG. 2, such as
computer network elements.
[0033] The external data source portion 2100 may be associated
with, such as controlled by, an external person, entity, or
organization (second-party). The internal database analysis portion
2200 may be associated with, such as created by or controlled by, a
person, entity, or organization (first-party). The system interface
portion 2300 may be associated with, such as created by or
controlled by, the first-party and may be accessed by the
first-party, the second-party, third-parties, or a combination
thereof, such as in accordance with access and authorization
permissions and procedures.
[0034] The external data source portion 2100 is shown as including
external database servers 2120 and external application servers
2140. The external data source portion 2100 may include other
elements not shown in FIG. 2. The external data source portion 2100
may include external computing devices, such as the computing
device 1000 shown in FIG. 1, which may be used by or accessible to
the external person, entity, or organization (second-party)
associated with the external data source portion 2100, including
but not limited to external database servers 2120 and external
application servers 2140. The external computing devices may
include data regarding the operation of the external person,
entity, or organization (second-party) associated with the external
data source portion 2100.
[0035] The external database servers 2120 may be one or more
computing devices configured to store data in a format and schema
determined externally from the internal database analysis portion
2200, such as by a second-party associated with the external data
source portion 2100, or a third party. For example, the external
database server 2120 may use a relational database and may include
a database catalog with a schema. In some embodiments, the external
database server 2120 may include a non-database data storage
structure, such as a text-based data structure, such as a comma
separated variable structure or an extensible markup language
formatted structure or file. For example, the external database
servers 2120 can include data regarding the production of materials
by the external person, entity, or organization (second-party)
associated with the external data source portion 2100,
communications between the external person, entity, or organization
(second-party) associated with the external data source portion
2100 and third parties, or a combination thereof. Other data may be
included. The external database may be a structured database
system, such as a relational database operating in a relational
database management system (RDBMS), which may be an enterprise
database. In some embodiments, the external database may be an
unstructured data source. The external data may include data or
content, such as sales data, revenue data, profit data, tax data,
shipping data, safety data, sports data, health data, weather data,
or the like, or any other data, or combination of data, that may be
generated by or associated with a user, an organization, or an
enterprise and stored in a database system. For simplicity and
clarity, data stored in or received from the external data source
portion 2100 may be referred to herein as enterprise data.
[0036] The external application server 2140 may include application
software, such as application software used by the external person,
entity, or organization (second-party) associated with the external
data source portion 2100. The external application server 2140 may
include data or metadata relating to the application software.
[0037] The external database servers 2120, the external application
servers 2140, or both, shown in FIG. 2 may represent logical units
or devices that may be implemented on one or more physical units or
devices, which may be controlled or operated by the first party,
the second party, or a third party.
[0038] The external data source portion 2100, or aspects thereof,
such as the external database servers 2120, the external
application servers 2140, or both, may communicate with the
internal database analysis portion 2200, or an aspect thereof, such
as one or more of the servers 2220, 2240, 2260, and 2280, via an
electronic communication medium, which may be a wired or wireless
electronic communication medium. For example, the electronic
communication medium may include a local area network (LAN), a wide
area network (WAN), a fiber channel network, the Internet, or a
combination thereof.
[0039] The internal database analysis portion 2200 is shown as
including servers 2220, 2240, 2260, and 2280. The servers 2220,
2240, 2260, and 2280 may be computing devices, such as the
computing device 1000 shown in FIG. 1. Although four servers 2220,
2240, 2260, and 2280 are shown in FIG. 2, other numbers, or
cardinalities, of servers may be used. For example, the number of
computing devices may be determined based on the capability of
individual computing devices, the amount of data to be processed,
the complexity of the data to be processed, or a combination
thereof. Other metrics may be used for determining the number of
computing devices.
[0040] The internal database analysis portion 2200 may store data,
process data, or store and process data. The internal database
analysis portion 2200 may include a distributed cluster (not
expressly shown) which may include two or more of the servers 2220,
2240, 2260, and 2280. The operation of distributed cluster, such as
the operation of the servers 2220, 2240, 2260, and 2280
individually, in combination, or both, may be managed by a
distributed cluster manager. For example, the server 2220 may be
the distributed cluster manager. In another example, the
distributed cluster manager may be implemented on another computing
device (not shown). The data and processing of the distributed
cluster may be distributed among the servers 2220, 2240, 2260, and
2280, such as by the distributed cluster manager.
[0041] Enterprise data from the external data source portion 2100,
such as from the external database server 2120, the external
application server 2140, or both may be imported into the internal
database analysis portion 2200. The external database server 2120,
the external application server 2140, or both may be one or more
computing devices and may communicate with the internal database
analysis portion 2200 via electronic communication. The imported
data may be distributed among, processed by, stored on, or a
combination thereof, one or more of the servers 2220, 2240, 2260,
and 2280. Importing the enterprise data may include importing or
accessing the data structures of the enterprise data. Importing the
enterprise data may include generating internal data, internal data
structures, or both, based on the enterprise data. The internal
data, internal data structures, or both may accurately represent
and may differ from the enterprise data, the data structures of the
enterprise data, or both. In some implementations, enterprise data
from multiple external data sources may be imported into the
internal database analysis portion 2200. For simplicity and
clarity, data stored or used in the internal database analysis
portion 2200 may be referred to herein as internal data. For
example, the internal data, or a portion thereof, may represent,
and may be distinct from, enterprise data imported into or accessed
by the internal database analysis portion 2200.
[0042] The system interface portion 2300 may include one or more
client devices 2320, 2340. The client devices 2320, 2340 may be
computing devices, such as the computing device 1000 shown in FIG.
1. For example, one of the client devices 2320, 2340 may be a
desktop or laptop computer and the other of the client devices
2320, 2340 may be a mobile device, smartphone, or tablet. One or
more of the client devices 2320, 2340 may access the internal
database analysis portion 2200. For example, the internal database
analysis portion 2200 may provide one or more services, application
interfaces, or other electronic computer communication interfaces,
such as a web site, and the client devices 2320, 2340 may access
the interfaces provided by the internal database analysis portion
2200, which may include accessing the internal data stored in the
internal database analysis portion 2200.
[0043] In an example, one or more of the client devices 2320, 2340
may send a message or signal indicating a request for data, which
may include a request for data analysis, to the internal database
analysis portion 2200. The internal database analysis portion 2200
may receive and process the request, which may include distributing
the processing among one or more of the servers 2220, 2240, 2260,
and 2280, may generate a response to the request, which may include
generating or modifying internal data, internal data structures, or
both, and may output the response to the client device 2320, 2340
that sent the request. Processing the request may include accessing
one or more internal data indexes, an internal database, or a
combination thereof. The client device 2320, 2340 may receive the
response, including the response data or a portion thereof, and may
store, output, or both, the response or a representation thereof,
such as a representation of the response data, or a portion
thereof, which may include presenting the representation via a user
interface on a presentation device of the client device 2320, 2340,
such as to a user of the client device 2320, 2340.
[0044] The system interface portion 2300, or aspects thereof, such
as one or more of the client devices 2320, 2340, may communicate
with the internal database analysis portion 2200, or an aspect
thereof, such as one or more of the servers 2220, 2240, 2260, and
2280, via an electronic communication medium, which may be a wired
or wireless electronic communication medium. For example, the
electronic communication medium may include a local area network
(LAN), a wide area network (WAN), a fiber channel network, the
Internet, or a combination thereof.
[0045] FIG. 3 is a block diagram of an example of a low-latency
data access and analysis system 3000. The low-latency data access
and analysis system 3000, or aspects thereof, may be similar to the
internal database analysis portion 2200 shown in FIG. 2, except as
described herein or otherwise clear from context. The low-latency
data access and analysis system 3000, or aspects thereof, may be
implemented on one or more computing devices, such as servers 2220,
2240, 2260, and 2280 shown in FIG. 2, which may be in a clustered
or distributed computing configuration.
[0046] The low-latency data access and analysis system 3000 may
store and maintain the internal data, or a portion thereof, such as
low-latency data, in a low-latency memory device, such as the
low-latency memory 1300 shown in FIG. 1, or any other type of data
storage medium or combination of data storage devices with
relatively fast (low-latency) data access, organized in a
low-latency data structure. In some embodiments, the low-latency
data access and analysis system 3000 may be implemented as one or
more logical devices in a cloud-based configuration optimized for
automatic database analysis.
[0047] As shown, the low-latency data access and analysis system
3000 includes a distributed cluster manager 3100, a security and
governance unit 3200, a distributed in-memory database 3300, an
enterprise data interface unit 3400, a distributed in-memory
ontology unit 3500, a semantic interface unit 3600, a relational
search unit 3700, a natural language processing unit 3710, a data
utility unit 3720, an insight unit 3730, an object search unit
3800, an object utility unit 3810, a system configuration unit
3820, a user customization unit 3830, a system access interface
unit 3900, a real-time collaboration unit 3910, a third-party
integration unit 3920, and a persistent storage unit 3930, which
may be collectively referred to as the components of the
low-latency data access and analysis system 3000.
[0048] Although not expressly shown in FIG. 3, one or more of the
components of the low-latency data access and analysis system 3000
may be implemented on one or more operatively connected physical or
logical computing devices, such as in a distributed cluster
computing configuration, such as the internal database analysis
portion 2200 shown in FIG. 2. Although shown separately in FIG. 3,
one or more of the components of the low-latency data access and
analysis system 3000, or respective aspects thereof, may be
combined or otherwise organized.
[0049] The low-latency data access and analysis system 3000 may
include different, fewer, or additional components not shown in
FIG. 3. The aspects or components implemented in an instance of the
low-latency data access and analysis system 3000 may be
configurable. For example, the insight unit 3730 may be omitted or
disabled. One or more of the components of the low-latency data
access and analysis system 3000 may be implemented in a manner such
that aspects thereof are divided or combined into various
executable modules or libraries in a manner which may differ from
that described herein.
[0050] The low-latency data access and analysis system 3000 may
implement an application programming interface (API), which may
monitor, receive, or both, input signals or messages from external
devices and systems, client systems, process received signals or
messages, transmit corresponding signals or messages to one or more
of the components of the low-latency data access and analysis
system 3000, and output, such as transmit or send, output messages
or signals to respective external devices or systems. The
low-latency data access and analysis system 3000 may be implemented
in a distributed computing configuration.
[0051] The distributed cluster manager 3100 manages the operative
configuration of the low-latency data access and analysis system
3000. Managing the operative configuration of the low-latency data
access and analysis system 3000 may include controlling the
implementation of and distribution of processing and storage across
one or more logical devices operating on one or more physical
devices, such as the servers 2220, 2240, 2260, and 2280 shown in
FIG. 2. The distributed cluster manager 3100 may generate and
maintain configuration data for the low-latency data access and
analysis system 3000, such as in one or more tables, identifying
the operative configuration of the low-latency data access and
analysis system 3000. For example, the distributed cluster manager
3100 may automatically update the low-latency data access and
analysis system configuration data in response to an operative
configuration event, such as a change in availability or
performance for a physical or logical unit of the low-latency data
access and analysis system 3000. One or more of the component units
of low-latency data access and analysis system 3000 may access the
database analysis system configuration data, such as to identify
intercommunication parameters or paths.
[0052] The security and governance unit 3200 may describe,
implement, enforce, or a combination thereof, rules and procedures
for controlling access to aspects of the low-latency data access
and analysis system 3000, such as the internal data of the
low-latency data access and analysis system 3000 and the features
and interfaces of the low-latency data access and analysis system
3000. The security and governance unit 3200 may apply security at
an ontological level to control or limit access to the internal
data of the low-latency data access and analysis system 3000, such
as to columns, tables, rows, or fields, which may include using row
level security.
[0053] Although shown as a single unit in FIG. 3, the distributed
in-memory database 3300 may be implemented in a distributed
configuration, such as distributed among the servers 2220, 2240,
2260, and 2280 shown in FIG. 2, which may include multiple
in-memory database instances. Each in-memory database instance may
utilize one or more distinct resources, such as processing or
low-latency memory resources, that differ from the resources
utilized by the other in-memory database instances. In some
embodiments, the in-memory database instances may utilize one or
more shared resources, such as resources utilized by two or more
in-memory database instances.
[0054] The distributed in-memory database 3300 may generate,
maintain, or both, a low-latency data structure and data stored or
maintained therein (low-latency data). The low-latency data may
include principal data, which may represent enterprise data, such
as enterprise data imported from an external enterprise data
source, such as the external data source portion 2100 shown in FIG.
2. In some implementations, the distributed in-memory database 3300
may include system internal data representing one or more aspects,
features, or configurations of the low-latency data access and
analysis system 3000. The distributed in-memory database 3300 and
the low-latency data stored therein, or a portion thereof, may be
accessed using commands, messages, or signals in accordance with a
defined structured query language associated with the distributed
in-memory database 3300.
[0055] The low-latency data, or a portion thereof, may be organized
as tables in the distributed in-memory database 3300. A table may
be a data structure to organize or group the data or a portion
thereof, such as related or similar data. A table may have a
defined structure. For example, each table may define or describe a
respective set of one or more columns.
[0056] A column may define or describe the characteristics of a
discrete aspect of the data in the table. For example, the
definition or description of a column may include an identifier,
such as a name, for the column within the table, and one or more
constraints, such as a data type, for the data corresponding to the
column in the table. The definition or description of a column may
include other information, such as a description of the column. The
data in a table may be accessible or partitionable on a per-column
basis. The set of tables, including the column definitions therein,
and information describing relationships between elements, such as
tables and columns, of the database may be defined or described by
a database schema or design. The cardinality of columns of a table,
and the definition and organization of the columns, may be defined
by the database schema or design. Adding, deleting, or modifying a
table, a column, the definition thereof, or a relationship or
constraint thereon, may be a modification of the database design,
schema, model, or structure.
[0057] The low-latency data, or a portion thereof, may be stored in
the database as one or more rows or records in respective tables.
Each record or row of a table may include a respective field or
cell corresponding to each column of the table. A field may store a
discrete data value. The cardinality of rows of a table, and the
values stored therein, may be variable based on the data. Adding,
deleting, or modifying rows, or the data stored therein may omit
modification of the database design, schema, or structure. The data
stored in respective columns may be identified or defined as a
measure data, attribute data, or enterprise ontology data (e.g.,
metadata).
[0058] Measure data, or measure values, may include quantifiable or
additive numeric values, such as integer or floating-point values,
which may include numeric values indicating sizes, amounts,
degrees, or the like. A column defined as representing measure
values may be referred to herein as a measure or fact. A measure
may be a property on which quantitative operations (e.g., sum,
count, average, minimum, maximum) may be performed to calculate or
determine a result or output.
[0059] Attribute data, or attribute values, may include
non-quantifiable values, such as text or image data, which may
indicate names and descriptions, quantifiable values designated,
defined, or identified as attribute data, such as numeric unit
identifiers, or a combination thereof. A column defined as
including attribute values may be referred to herein as an
attribute or dimension. For example, attributes may include text,
identifiers, timestamps, or the like.
[0060] Enterprise ontology data may include data that defines or
describes one or more aspects of the database, such as data that
describes one or more aspects of the attributes, measures, rows,
columns, tables, relationships, or other aspects of the data or
database schema. For example, a portion of the database design,
model, or schema may be represented as enterprise ontology data in
one or more tables in the database.
[0061] Distinctly identifiable data in the low-latency data may be
referred to herein as a data portion. For example, the low-latency
data stored in the distributed in-memory database 3300 may be
referred to herein as a data portion, a table from the low-latency
data may be referred to herein as a data portion, a column from the
low-latency data may be referred to herein as a data portion, a row
or record from the low-latency data may be referred to herein as a
data portion, a value from the low-latency data may be referred to
herein as a data portion, a relationship defined in the low-latency
data may be referred to herein as a data portion, enterprise
ontology data describing the low-latency data may be referred to
herein as a data portion, or any other distinctly identifiable
data, or combination thereof, from the low-latency data may be
referred to herein as a data portion.
[0062] The distributed in-memory database 3300 may create or add
one or more data portions, such as a table, may read from or access
one or more data portions, may update or modify one or more data
portions, may remove or delete one or more data portions, or a
combination thereof. Adding, modifying, or removing data portions
may include changes to the data model of the low-latency data.
Changing the data model of the low-latency data may include
notifying one or more other components of the low-latency data
access and analysis system 3000, such as by sending, or otherwise
making available, a message or signal indicating the change. For
example, the distributed in-memory database 3300 may create or add
a table to the low-latency data and may transmit or send a message
or signal indicating the change to the semantic interface unit
3600.
[0063] In some implementations, a portion of the low-latency data
may represent a data model of an external enterprise database and
may omit the data stored in the external enterprise database, or a
portion thereof. For example, prioritized data may be cached in the
distributed in-memory database 3300 and the other data may be
omitted from storage in the distributed in-memory database 3300,
which may be stored in the external enterprise database. In some
implementations, requesting data from the distributed in-memory
database 3300 may include requesting the data, or a portion
thereof, from the external enterprise database.
[0064] The distributed in-memory database 3300 may receive one or
more messages or signals indicating respective data-queries for the
low-latency data, or a portion thereof, which may include
data-queries for modified, generated, or aggregated data generated
based on the low-latency data, or a portion thereof. For example,
the distributed in-memory database 3300 may receive a data-query
from the semantic interface unit 3600, such as in accordance with a
request for data. The data-queries received by the distributed
in-memory database 3300 may be agnostic to the distributed
configuration of the distributed in-memory database 3300. A
data-query, or a portion thereof, may be expressed in accordance
with the defined structured query language implemented by the
distributed in-memory database 3300. In some implementations, a
data-query may be included, such as stored or communicated, in a
data-query data structure or container.
[0065] The distributed in-memory database 3300 may execute or
perform one or more queries to generate or obtain response data
responsive to the data-query based on the low-latency data.
[0066] The distributed in-memory database 3300 may interpret,
evaluate, or otherwise process a data-query to generate one or more
distributed-queries, which maybe expressed in accordance with the
defined structured query language. For example, an in-memory
database instance of the distributed in-memory database 3300 may be
identified as a query coordinator. The query coordinator may
generate a query plan, which may include generating one or more
distributed-queries, based on the received data-query. The query
plan may include query execution instructions for executing one or
more queries, or one or more portions thereof, based on the
received data-query by the one or more of the in-memory database
instances. Generating the query plan may include optimizing the
query plan. The query coordinator may distribute, or otherwise make
available, the respective portions of the query plan, as query
execution instructions, to the corresponding in-memory database
instances.
[0067] The respective in-memory database instances may receive the
corresponding query execution instructions from the query
coordinator. The respective in-memory database instances may
execute the corresponding query execution instructions to obtain,
process, or both, data (intermediate results data) from the
low-latency data. The respective in-memory database instances may
output, or otherwise make available, the intermediate results data,
such as to the query coordinator.
[0068] The query coordinator may execute a respective portion of
query execution instructions (allocated to the query coordinator)
to obtain, process, or both, data (intermediate results data) from
the low-latency data. The query coordinator may receive, or
otherwise access, the intermediate results data from the respective
in-memory database instances. The query coordinator may combine,
aggregate, or otherwise process, the intermediate results data to
obtain results data.
[0069] In some embodiments, obtaining the intermediate results data
by one or more of the in-memory database instances may include
outputting the intermediate results data to, or obtaining
intermediate results data from, one or more other in-memory
database instances, in addition to, or instead of, obtaining the
intermediate results data from the low-latency data.
[0070] The distributed in-memory database 3300 may output, or
otherwise make available, the results data to the semantic
interface unit 3600.
[0071] The enterprise data interface unit 3400 may interface with,
or communicate with, an external enterprise data system. For
example, the enterprise data interface unit 3400 may receive or
access enterprise data from or in an external system, such as an
external database. The enterprise data interface unit 3400 may
import, evaluate, or otherwise process the enterprise data to
populate, create, or modify data stored in the low-latency data
access and analysis system 3000. The enterprise data interface unit
3400 may receive, or otherwise access, the enterprise data from one
or more external data sources, such as the external data source
portion 2100 shown in FIG. 2, and may represent the enterprise data
in the low-latency data access and analysis system 3000 by
importing, loading, or populating the enterprise data as principal
data in the distributed in-memory database 3300, such as in one or
more low-latency data structures. The enterprise data interface
unit 3400 may implement one or more data connectors, which may
transfer data between, for example, the external data source and
the distributed in-memory database 3300, which may include
altering, formatting, evaluating, or manipulating the data.
[0072] The enterprise data interface unit 3400 may receive, access,
or generate metadata that identifies one or more parameters or
relationships for the principal data, such as based on the
enterprise data, and may include the generated metadata in the
low-latency data stored in the distributed in-memory database 3300.
For example, the enterprise data interface unit 3400 may identify
characteristics of the principal data such as, attributes,
measures, values, unique identifiers, tags, links, keys, or the
like, and may include metadata representing the identified
characteristics in the low-latency data stored in the distributed
in-memory database 3300. The characteristics of the data can be
automatically determined by receiving, accessing, processing,
evaluating, or interpreting the schema in which the enterprise data
is stored, which may include automatically identifying links or
relationships between columns, classifying columns (e.g., using
column names), and analyzing or evaluating the data.
[0073] Distinctly identifiable operative data units or structures
representing one or more data portions, one or more entities,
users, groups, or organizations represented in the internal data,
or one or more aggregations, collections, relations, analytical
results, visualizations, or groupings thereof, may be represented
in the low-latency data access and analysis system 3000 as objects.
An object may include a unique identifier for the object, such as a
fully qualified name. An object may include a name, such as a
displayable value, for the object.
[0074] For example, an object may represent a user, a group, an
entity, an organization, a privilege, a role, a table, a column, a
data relationship, a worksheet, a view, a context, an answer, an
insight, a pinboard, a tag, a comment, a trigger, a defined
variable, a data source, an object-level security rule, a row-level
security rule, or any other data capable of being distinctly
identified and stored or otherwise obtained in the low-latency data
access and analysis system 3000. An object may represent or
correspond with a logical entity. Data describing an object may
include data operatively or uniquely identifying data corresponding
to, or represented by, the object in the low-latency data access
and analysis system. For example, a column in a table in a database
in the low-latency data access and analysis system may be
represented in the low-latency data access and analysis system as
an object and the data describing or defining the object may
include data operatively or uniquely identifying the column.
[0075] A worksheet (worksheet object), or worksheet table, may be a
logical table, or a definition thereof, which may be a collection,
a sub-set (such as a subset of columns from one or more tables), or
both, of data from one or more data sources, such as columns in one
or more tables, such as in the distributed in-memory database 3300.
A worksheet, or a definition thereof, may include one or more data
organization or manipulation definitions, such as join paths or
worksheet-column definitions, which may be user defined. A
worksheet may be a data structure that may contain one or more
rules or definitions that may define or describe how a respective
tabular set of data may be obtained, which may include defining one
or more sources of data, such as one or more columns from the
distributed in-memory database 3300. A worksheet may be a data
source. For example, a worksheet may include references to one or
more data sources, such as columns in one or more tables, such as
in the distributed in-memory database 3300, and a request for data
referencing the worksheet may access the data from the data sources
referenced in the worksheet. In some implementations, a worksheet
may omit aggregations of the data from the data sources referenced
in the worksheet.
[0076] An answer (answer object), or report, may be a defined, such
as previously generated, request for data, such as a
resolved-request. An answer may include information describing a
visualization of data responsive to the request for data.
[0077] A view (view object) may be a logical table, or a definition
thereof, which may be a collection, a sub-set, or both, of data
from one or more data sources, such as columns in one or more
tables, such as in the distributed in-memory database 3300. For
example, a view may be generated based on an answer, such as by
storing the answer as a view. A view may define or describe a data
aggregation. A view may be a data source. For example, a view may
include references to one or more data sources, such as columns in
one or more tables, such as in the distributed in-memory database
3300, which may include a definition or description of an
aggregation of the data from a respective data source, and a
request for data referencing the view may access the aggregated
data, the data from the unaggregated data sources referenced in the
worksheet, or a combination thereof. The unaggregated data from
data sources referenced in the view defined or described as
aggregated data in the view may be unavailable based on the view. A
view may be a materialized view or an unmaterialized view. A
request for data referencing a materialized view may obtain data
from a set of data previously obtained (view-materialization) in
accordance with the definition of the view and the request for
data. A request for data referencing an unmaterialized view may
obtain data from a set of data currently obtained in accordance
with the definition of the view and the request for data.
[0078] A pinboard (pinboard object), or dashboard, may be a defined
collection or grouping of objects, such as visualizations, answers,
or insights. Pinboard data for a pinboard may include information
associated with the pinboard, which may be associated with
respective objects included in the pinboard.
[0079] A context (context object) may be a set or collection of
data associated with a request for data or a discretely related
sequence or series of requests for data or other interactions with
the low-latency data access and analysis system 3000.
[0080] A definition may be a set of data describing the structure
or organization of a data portion. For example, in the distributed
in-memory database 3300, a column definition may define one or more
aspects of a column in a table, such as a name of the column, a
description of the column, a datatype for the column, or any other
information about the column that may be represented as discrete
data.
[0081] A data source object may represent a source or repository of
data accessible by the low-latency data access and analysis system
3000. A data source object may include data indicating an
electronic communication location, such as an address, of a data
source, connection information, such as protocol information,
authentication information, or a combination thereof, or any other
information about the data source that may be represented as
discrete data. For example, a data source object may represent a
table in the distributed in-memory database 3300 and include data
for accessing the table from the database, such as information
identifying the database, information identifying a schema within
the database, and information identifying the table within the
schema within the database. An external data source object may
represent an external data source. For example, an external data
source object may include data indicating an electronic
communication location, such as an address, of an external data
source, connection information, such as protocol information,
authentication information, or a combination thereof, or any other
information about the external data source that may be represented
as discrete data.
[0082] A sticker (sticker object) may be a description of a
classification, category, tag, subject area, or other information
that may be associated with one or more other objects such that
objects associated with a sticker may be grouped, sorted, filtered,
or otherwise identified based on the sticker. In the distributed
in-memory database 3300 a tag may be a discrete data portion that
may be associated with other data portions, such that data portions
associated with a tag may be grouped, sorted, filtered, or
otherwise identified based on the tag.
[0083] The distributed in-memory ontology unit 3500 generates,
maintains, or both, information (ontological data) defining or
describing the operative ontological structure of the objects
represented in the low-latency data access and analysis system
3000, such as in the low-latency data stored in the distributed
in-memory database 3300, which may include describing attributes,
properties, states, or other information about respective objects
and may include describing relationships among respective
objects.
[0084] Objects may be referred to herein as primary objects,
secondary objects, or tertiary objects. Other types of objects may
be used.
[0085] Primary objects may include objects representing distinctly
identifiable operative data units or structures representing one or
more data portions in the distributed in-memory database 3300, or
another data source in the low-latency data access and analysis
system 3000. For example, primary objects may be data source
objects, table objects, column objects, relationship objects, or
the like. Primary objects may include worksheets, views, filters,
such as row-level-security filters and table filters, variables, or
the like. Primary objects may be referred to herein as data-objects
or queryable-objects.
[0086] Secondary objects may be objects representing distinctly
identifiable operative data units or structures representing
analytical data aggregations, collections, analytical results,
visualizations, or groupings thereof, such as pinboard objects,
answer objects, insights, visualization objects, and the like.
Secondary objects may be referred to herein as
analytical-objects.
[0087] Tertiary objects may be objects representing distinctly
identifiable operative data units or structures representing
operational aspects of the low-latency data access and analysis
system 3000, such as one or more entities, users, groups, or
organizations represented in the internal data, such as user
objects, user-group objects, role objects, sticker objects, and the
like.
[0088] The distributed in-memory ontology unit 3500 may represent
the ontological structure, which may include the objects therein,
as a graph having nodes and edges. A node may be a representation
of an object in the graph structure of the distributed in-memory
ontology unit 3500. A node object can include one or more component
objects. Component objects may be versioned, such as on a
per-component object basis. For example, a node can include a
header object, a content object, or both. A header object may
include information about the node. A content may include the
content of the node. An edge may represent a relationship between
nodes, which may be directional.
[0089] In some implementations, the distributed in-memory ontology
unit 3500 graph may include one or more nodes, edges, or both,
representing one or more objects, relationships or both,
corresponding to a respective internal representation of enterprise
data stored in an external enterprise data storage unit, wherein a
portion of the data stored in the external enterprise data storage
unit represented in the distributed in-memory ontology unit 3500
graph is omitted from the distributed in-memory database 3300.
[0090] In some embodiments, the distributed in-memory ontology unit
3500 may generate, modify, or remove a portion of the ontology
graph in response to one or more messages, signals, or
notifications from one or more of the components of the low-latency
data access and analysis system 3000. For example, the distributed
in-memory ontology unit 3500 may generate, modify, or remove a
portion of the ontology graph in response to receiving one or more
messages, signals, or notifications from the distributed in-memory
database 3300 indicating a change to the low-latency data
structure. In another example, the distributed in-memory database
3300 may send one or more messages, signals, or notifications
indicating a change to the low-latency data structure to the
semantic interface unit 3600 and the semantic interface unit 3600
may send one or more messages, signals, or notifications indicating
the change to the low-latency data structure to the distributed
in-memory ontology unit 3500.
[0091] The distributed in-memory ontology unit 3500 may be
distributed, in-memory, multi-versioned, transactional, consistent,
durable, or a combination thereof. The distributed in-memory
ontology unit 3500 is transactional, which may include implementing
atomic concurrent, or substantially concurrent, updating of
multiple objects. The distributed in-memory ontology unit 3500 is
durable, which may include implementing a robust storage that
prevents data loss subsequent to or as a result of the completion
of an atomic operation. The distributed in-memory ontology unit
3500 is consistent, which may include performing operations
associated with a request for data with reference to or using a
discrete data set, which may mitigate or eliminate the risk
inconsistent results.
[0092] The distributed in-memory ontology unit 3500 may generate,
output, or both, one or more event notifications. For example, the
distributed in-memory ontology unit 3500 may generate, output, or
both, a notification, or notifications, in response to a change of
the distributed in-memory ontology. The distributed in-memory
ontology unit 3500 may identify a portion of the distributed
in-memory ontology (graph) associated with a change of the
distributed in-memory ontology, such as one or more nodes depending
from a changed node, and may generate, output, or both, a
notification, or notifications indicating the identified relevant
portion of the distributed in-memory ontology (graph). One or more
aspects of the low-latency data access and analysis system 3000 may
cache object data and may receive the notifications from the
distributed in-memory ontology unit 3500, which may reduce latency
and network traffic relative to systems that omit caching object
data or omit notifications relevant to changes to portions of the
distributed in-memory ontology (graph).
[0093] The distributed in-memory ontology unit 3500 may implement
prefetching. For example, the distributed in-memory ontology unit
3500 may predictively, such as based on determined probabilistic
utility, fetch one or more nodes, such as in response to access to
a related node by a component of the low-latency data access and
analysis system 3000.
[0094] The distributed in-memory ontology unit 3500 may implement a
multi-version concurrency control graph data storage unit. Each
node, object, or both, may be versioned. Changes to the distributed
in-memory ontology may be reversible. For example, the distributed
in-memory ontology may have a first state prior to a change to the
distributed in-memory ontology, the distributed in-memory ontology
may have a second state subsequent to the change, and the state of
the distributed in-memory ontology may be reverted to the first
state subsequent to the change, such as in response to the
identification of an error or failure associated with the second
state.
[0095] In some implementations, reverting a node, or a set of
nodes, may omit reverting one or more other nodes. In some
implementations, the distributed in-memory ontology unit 3500 may
maintain a change log indicating a sequential record of changes to
the distributed in-memory ontology (graph), such that a change to a
node or a set of nodes may be reverted and one or more other
changes subsequent to the reverted change may be reverted for
consistency.
[0096] The distributed in-memory ontology unit 3500 may implement
optimistic locking to reduce lock contention times. The use of
optimistic locking permits improved throughput of data through the
distributed in-memory ontology unit 3500.
[0097] The semantic interface unit 3600 may implement procedures
and functions to provide a semantic interface between the
distributed in-memory database 3300 and one or more of the other
components of the low-latency data access and analysis system
3000.
[0098] The semantic interface unit 3600 may implement ontological
data management, data-query generation, authentication and access
control, object statistical data collection, or a combination
thereof.
[0099] Ontological data management may include object lifecycle
management, object data persistence, ontological modifications, or
the like. Object lifecycle management may include creating one or
more objects, reading or otherwise accessing one or more objects,
updating or modifying one or more objects, deleting or removing one
or more objects, or a combination thereof. For example, the
semantic interface unit 3600 may interface or communicate with the
distributed in-memory ontology unit 3500, which may store the
ontological data, object data, or both, to perform object lifecycle
management, object data persistence, ontological modifications, or
the like.
[0100] For example, the semantic interface unit 3600 may receive,
or otherwise access, a message, signal, or notification, such as
from the distributed in-memory database 3300, indicating the
creation or addition of a data portion, such as a table, in the
low-latency data stored in the distributed in-memory database 3300,
and the semantic interface unit 3600 may communicate with the
distributed in-memory ontology unit 3500 to create an object in the
ontology representing the added data portion. The semantic
interface unit 3600 may transmit, send, or otherwise make
available, a notification, message, or signal to the relational
search unit 3700 indicating that the ontology has changed.
[0101] The semantic interface unit 3600 may receive, or otherwise
access, a request message or signal, such as from the relational
search unit 3700, indicating a request for information describing
changes to the ontology (ontological updates request). The semantic
interface unit 3600 may generate and send, or otherwise make
available, a response message or signal to the relational search
unit 3700 indicating the changes to the ontology (ontological
updates response). The semantic interface unit 3600 may identify
one or more data portions for indexing based on the changes to the
ontology. For example, the changes to the ontology may include
adding a table to the ontology, the table including multiple rows,
and the semantic interface unit 3600 may identify each row as a
data portion for indexing. The semantic interface unit 3600 may
include information describing the ontological changes in the
ontological updates response. The semantic interface unit 3600 may
include one or more data-query definitions, such as data-query
definitions for indexing data-queries, for each data portion
identified for indexing in the ontological updates response. For
example, the data-query definitions may include a sampling
data-query, which may be used to query the distributed in-memory
database 3300 for sample data from the added data portion, an
indexing data-query, which may be used to query the distributed
in-memory database 3300 for data from the added data portion, or
both.
[0102] The semantic interface unit 3600 may receive, or otherwise
access, internal signals or messages including data expressing
usage intent, such as data indicating requests to access or modify
the low-latency data stored in the distributed in-memory database
3300 (e.g., a request for data), wherein the data expressing usage
intent is current operative data obtained in response to an
interaction between a user interface of the low-latency data access
and analysis system and a user thereof. The request to access or
modify the low-latency data received by the semantic interface unit
3600 may include a resolved-request. The resolved-request, which
may be database and visualization agnostic, may be expressed or
communicated as an ordered sequence of tokens, which may represent
semantic data. For example, the relational search unit 3700 may
tokenize, identify semantics, or both, based on input data, such as
input data representing user input, to generate the
resolved-request. The resolved-request may include an ordered
sequence of tokens that represent the request for data
corresponding to the input data, and may transmit, send, or
otherwise make accessible, the resolved-request to the semantic
interface unit 3600. The semantic interface unit 3600 may process
or respond to a received resolved-request.
[0103] The semantic interface unit 3600 may process or transform
the received resolved-request, which may be, at least in part,
incompatible with the distributed in-memory database 3300, to
generate one or more corresponding data-queries that are compatible
with the distributed in-memory database 3300, which may include
generating a proto-query representing the resolved-request,
generating a pseudo-query representing the proto-query, and
generating the data-query representing the pseudo-query.
[0104] The semantic interface unit 3600 may generate a proto-query
based on the resolved-request. A proto-query, which may be database
agnostic, may be structured or formatted in a form, language, or
protocol that differs from the defined structured query language of
the distributed in-memory database 3300. Generating the proto-query
may include identifying visualization identification data, such as
an indication of a type of visualization, associated with the
request for data, and generating the proto-query based on the
resolved-request and the visualization identification data.
[0105] The semantic interface unit 3600 may transform the
proto-query to generate a pseudo-query. The pseudo-query, which may
be database agnostic, may be structured or formatted in a form,
language, or protocol that differs from the defined structured
query language of the distributed in-memory database 3300.
Generating a pseudo-query may include applying a defined
transformation, or an ordered sequence of transformations.
Generating a pseudo-query may include incorporating row-level
security filters in the pseudo-query.
[0106] The semantic interface unit 3600 may generate a data-query
based on the pseudo-query, such as by serializing the pseudo-query.
The data-query, or a portion thereof, may be structured or
formatted using the defined structured query language of the
distributed in-memory database 3300. In some implementations, a
data-query may be structured or formatted using a defined
structured query language of another database, which may differ
from the defined structured query language of the distributed
in-memory database 3300. Generating the data-query may include
using one or more defined rules for expressing respective the
structure and content of a pseudo-query in the respective defined
structured query language.
[0107] The semantic interface unit 3600 may communicate, or issue,
the data-query to the distributed in-memory database 3300. In some
implementations, processing or responding to a resolved-request may
include generating and issuing multiple data-queries to the
distributed in-memory database 3300.
[0108] The semantic interface unit 3600 may receive results data
from the distributed in-memory database 3300 responsive to one or
more resolved-requests. The semantic interface unit 3600 may
process, format, or transform the results data to obtain
visualization data. For example, the semantic interface unit 3600
may identify a visualization for representing or presenting the
results data, or a portion thereof, such as based on the results
data or a portion thereof. For example, the semantic interface unit
3600 may identifying a bar chart visualization for results data
including one measure and attribute.
[0109] Although not shown separately in FIG. 3, the semantic
interface unit 3600 may include a data visualization unit. In some
embodiments, the data visualization unit may be a distinct unit,
separate from the semantic interface unit 3600. In some
embodiments, the data visualization unit may be included in the
system access interface unit 3900. The data visualization unit, the
system access interface unit 3900, or a combination thereof, may
generate a user interface, or one or more portions thereof. For
example, data visualization unit, the system access interface unit
3900, or a combination thereof, may obtain the results data, such
as the visualization data, and may generate user interface elements
(visualizations) representing the results data.
[0110] The semantic interface unit 3600 may implement object-level
security, row-level security, or a combination thereof. Object
level security may include security associated with an object, such
as a table, a column, a worksheet, an answer, or a pinboard.
Row-level security may include user-based or group-based access
control of rows of data in the low-latency data, the indexes, or
both. The semantic interface unit 3600 may implement on or more
authentication procedures, access control procedures, or a
combination thereof.
[0111] The semantic interface unit 3600 may implement one or more
user-data integration features. For example, the semantic interface
unit 3600 may generate and output a user interface, or a portion
thereof, for inputting, uploading, or importing user data, may
receive user data, and may import the user data. For example, the
user data may be enterprise data.
[0112] The semantic interface unit 3600 may implement object
statistical data collection. Object statistical data may include,
for respective objects, temporal access information, access
frequency information, access recency information, access requester
information, or the like. For example, the semantic interface unit
3600 may obtain object statistical data as described with respect
to the data utility unit 3720, the object utility unit 3810, or
both. The semantic interface unit 3600 may send, transmit, or
otherwise make available, the object statistical data for
data-objects to the data utility unit 3720. The semantic interface
unit 3600 may send, transmit, or otherwise make available, the
object statistical data for analytical-objects to the object
utility unit 3810.
[0113] The semantic interface unit 3600 may implement or expose one
or more services or application programming interfaces. For
example, the semantic interface unit 3600 may implement one or more
services for access by the system access interface unit 3900. In
some implementations, one or more services or application
programming interfaces may be exposed to one or more external
devices or systems.
[0114] The semantic interface unit 3600 may generate and transmit,
send, or otherwise communicate, one or more external
communications, such as e-mail messages, such as periodically, in
response to one or more events, or both. For example, the semantic
interface unit 3600 may generate and transmit, send, or otherwise
communicate, one or more external communications including a
portable representation, such as a portable document format
representation of one or more pinboards in accordance with a
defined schedule, period, or interval. In another example, the
semantic interface unit 3600 may generate and transmit, send, or
otherwise communicate, one or more external communications in
response to input data indicating an express request for a
communication. In another example, the semantic interface unit 3600
may generate and transmit, send, or otherwise communicate, one or
more external communications in response to one or more defined
events, such as the expiration of a recency of access period for a
user.
[0115] Although shown as a single unit in FIG. 3, the relational
search unit 3700 may be implemented in a distributed configuration,
which may include a primary relational search unit instance and one
or more secondary relational search unit instances.
[0116] The relational search unit 3700 may generate, maintain,
operate, or a combination thereof, one or more indexes, such as one
or more of an ontological index, a constituent data index, a
control-word index, a numeral index, or a constant index, based on
the low-latency data stored in the distributed in-memory database
3300, the low-latency data access and analysis system 3000, or
both. An index may be a defined data structure, or combination of
data structures, for storing tokens, terms, or string keys,
representing a set of data from one or more defined data sources in
a form optimized for searching, data-analytics, or both. For
example, an index may be a collection of index shards. In some
implementations, an index may be segmented into index segments and
the index segments may be sharded into index shards. In some
implementations, an index may be partitioned into index partitions,
the index partitions may be segmented into index segments and the
index segments may be sharded into index shards.
[0117] Generating, or building, an index may be performed to create
or populate a previously unavailable index, which may be referred
to as indexing the corresponding data, and may include
regenerating, rebuilding, or reindexing to update or modify a
previously available index, such as in response to a change in the
indexed data (constituent data).
[0118] The ontological index may be an index of data (ontological
data) describing the ontological structure or schema of the
low-latency data access and analysis system 3000, the low-latency
data stored in the distributed in-memory database 3300, or a
combination thereof. For example, the ontological index may include
data representing the table and column structure of the distributed
in-memory database 3300. The relational search unit 3700 may
generate, maintain, or both, the ontological index by communicating
with, such as requesting ontological data from, the distributed
in-memory ontology unit 3500, the semantic interface unit 3600, or
both. Each record in the ontological index may correspond to a
respective ontological token, such as a token that identifies a
column by name.
[0119] The control-word index may be an index of a defined set of
control-word tokens. A control-word token may be a character, a
symbol, a word, or a defined ordered sequence of characters or
symbols, that is identified in one or more grammars of the
low-latency data access and analysis system 3000 as having one or
more defined grammatical functions, which may be contextual. For
example, the control-word index may include the control-word token
"sum", which may be identified in one or more grammars of the
low-latency data access and analysis system 3000 as indicating an
additive aggregation. In another example, the control-word index
may include the control-word token "top", which may be identified
in one or more grammars of the low-latency data access and analysis
system 3000 as indicating a maximal value from an ordered set. In
another example, the control-word index may include operator
tokens, such as the equality operator token ("="). The constant
index may be an index of constant tokens such as "100" or "true".
The numeral index may be an index of number word tokens (or named
numbers), such as number word tokens for the positive integers
between zero and one million, inclusive. For example, "one hundred
and twenty eight".
[0120] A token may be a word, phrase, character, sequence of
characters, symbol, combination of symbols, or the like. A token
may represent a data portion in the low-latency data stored in the
low-latency data structure. For example, the relational search unit
3700 may automatically generate respective tokens representing the
attributes, the measures, the tables, the columns, the values,
unique identifiers, tags, links, keys, or any other data portion,
or combination of data portions, or a portion thereof. The
relational search unit 3700 may classify the tokens, which may
include storing token classification data in association with the
tokens. For example, a token may be classified as an attribute
token, a measure token, a value token, or the like.
[0121] The constituent data index may be an index of the
constituent data values stored in the low-latency data access and
analysis system 3000, such as in the distributed in-memory database
3300. The relational search unit 3700 may generate, maintain, or
both, the constituent data index by communicating with, such as
requesting data from, the distributed in-memory database 3300. For
example, the relational search unit 3700 may send, or otherwise
communicate, a message or signal to the distributed in-memory
database 3300 indicating a request to perform an indexing
data-query, the relational search unit 3700 may receive response
data from the distributed in-memory database 3300 in response to
the requested indexing data-query, and the relational search unit
3700 may generate the constituent data index, or a portion thereof,
based on the response data. For example, the constituent data index
may index data-objects.
[0122] An index shard may be used for token searching, such as
exact match searching, prefix match searching, substring match
searching, or suffix match searching. Exact match searching may
include identifying tokens in the index shard that matches a
defined target value. Prefix match searching may include
identifying tokens in the index shard that include a prefix, or
begin with a value, such as a character or string, that matches a
defined target value. Substring match searching may include
identifying tokens in the index shard that include a value, such as
a character or string, that matches a defined target value. Suffix
match searching may include identifying tokens in the index shard
that include a suffix, or end with a value, such as a character or
string, that matches a defined target value. In some
implementations, an index shard may include multiple distinct index
data structures. For example, an index shard may include a first
index data structure optimized for exact match searching, prefix
match searching, and suffix match searching, and a second index
data structure optimized for substring match searching. Traversing,
or otherwise accessing, managing, or using, an index may include
identifying one or more of the index shards of the index and
traversing the respective index shards. In some implementations,
one or more indexes, or index shards, may be distributed, such as
replicated on multiple relational search unit instances. For
example, the ontological index may be replicated on each relational
search unit instance.
[0123] The relational search unit 3700 may receive a request for
data from the low-latency data access and analysis system 3000. For
example, the relational search unit 3700 may receive data
expressing usage intent indicating the request for data in response
to input, such as user input, obtained via a user interface, such
as a user interface generated, or partially generated, by the
system access interface unit 3900, which may be a user interface
operated on an external device, such as one of the client devices
2320, 2340 shown in FIG. 2. In some implementations, the relational
search unit 3700 may receive the data expressing usage intent from
the system access interface unit 3900 or from the semantic
interface unit 3600. For example, the relational search unit 3700
may receive or access the data expressing usage intent in a request
for data message or signal.
[0124] The relational search unit 3700 may process, parse, identify
semantics, tokenize, or a combination thereof, the request for data
to generate a resolved-request, which may include identifying a
database and visualization agnostic ordered sequence of tokens
based on the data expressing usage intent. The data expressing
usage intent, or request for data, may include request data, such
as resolved-request data, unresolved request data, or a combination
of resolved-request data and unresolved request data. The
relational search unit 3700 may identify the resolved-request data.
The relational search unit 3700 may identify the unresolved request
data and may tokenize the unresolved request data.
[0125] Resolved-request data may be request data identified in the
data expressing usage intent as resolved-request data. Each
resolved-request data portion may correspond with a respective
token in the low-latency data access and analysis system 3000. The
data expressing usage intent may include information identifying
one or more portions of the request data as resolved-request
data.
[0126] Unresolved request data may be request data identified in
the data expressing usage intent as unresolved request data, or
request data for which the data expressing usage intent omits
information identifying the request data as resolved-request data.
Unresolved request data may include text or string data, which may
include a character, sequence of characters, symbol, combination of
symbols, word, sequence of words, phrase, or the like, for which
information, such as tokenization binding data, identifying the
text or string data as resolved-request data is absent or omitted
from the request data. The data expressing usage intent may include
information identifying one or more portions of the request data as
unresolved request data. The data expressing usage intent may omit
information identifying whether one or more portions of the request
data are resolved-request data. The relational search unit 3700 may
identify one or more portions of the request data for which the
data expressing usage intent omits information identifying whether
the one or more portions of the request data are resolved-request
data as unresolved request data.
[0127] For example, the data expressing usage intent may include a
request string and one or more indications that one or more
portions of the request string are resolved-request data. One or
more portions of the request string that are not identified as
resolved-request data in the data expressing usage intent may be
identified as unresolved request data. For example, the data
expressing usage intent may include the request string "example
text"; the data expressing usage intent may include information
indicating that the first portion of the request string, "example",
is resolved-request data; and the data expressing usage intent may
omit information indicating that the second portion of the request
string, "text", is resolved-request data.
[0128] The information identifying one or more portions of the
request data as resolved-request data may include tokenization
binding data indicating a previously identified token corresponding
to the respective portion of the request data. The tokenization
binding data corresponding to a respective token may include, for
example, one or more of a column identifier indicating a column
corresponding to the respective token, a data type identifier
corresponding to the respective token, a table identifier
indicating a table corresponding to the respective token, an
indication of an aggregation corresponding to the respective token,
or an indication of a join path associated with the respective
token. Other tokenization binding data may be used. In some
implementations, the data expressing usage intent may omit the
tokenization binding data and may include an identifier that
identifies the tokenization binding data.
[0129] The relational search unit 3700 may implement or access one
or more grammar-specific tokenizers, such as a tokenizer for a
defined data-analytics grammar or a tokenizer for a
natural-language grammar. For example, the relational search unit
3700 may implement one or more of a formula tokenizer, a
row-level-security tokenizer, a data-analytics tokenizer, or a
natural language tokenizer. Other tokenizers may be used. In some
implementations, the relational search unit 3700 may implement one
or more of the grammar-specific tokenizers, or a portion thereof,
by accessing another component of the low-latency data access and
analysis system 3000 that implements the respective
grammar-specific tokenizer, or a portion thereof. For example, the
natural language processing unit 3710 may implement the natural
language tokenizer and the relational search unit 3700 may access
the natural language processing unit 3710 to implement natural
language tokenization.
[0130] A tokenizer, such as the data-analytics tokenizer, may parse
text or string data (request string), such as string data included
in a data expressing usage intent, in a defined read order, such as
from left to right, such as on a character-by-character or
symbol-by-symbol basis. For example, a request string may include a
single character, symbol, or letter, and tokenization may include
identifying one or more tokens matching, or partially matching, the
input character.
[0131] Tokenization may include parsing the request string to
identify one or more words or phrases. For example, the request
string may include a sequence of characters, symbols, or letters,
and tokenization may include parsing the sequence of characters in
a defined order, such as from left to right, to identify distinct
words or terms and identifying one or more tokens matching the
respective words. In some implementations, word or phrase parsing
may be based on one or more of a set of defined delimiters, such as
a whitespace character, a punctuation character, or a mathematical
operator.
[0132] The relational search unit 3700 may traverse one or more of
the indexes to identify one or more tokens corresponding to a
character, word, or phrase identified in request string.
Tokenization may include identifying multiple candidate tokens
matching a character, word, or phrase identified in request string.
Candidate tokens may be ranked or ordered, such as based on
probabilistic utility.
[0133] Tokenization may include match-length maximization.
Match-length maximization may include ranking or ordering candidate
matching tokens in descending magnitude order. For example, the
longest candidate token, having the largest cardinality of
characters or symbols, matching the request string, or a portion
thereof, may be the highest ranked candidate token. For example,
the request string may include a sequence of words or a semantic
phrase, and tokenization may include identifying one or more tokens
matching the input semantic phrase. In another example, the request
string may include a sequence of phrases, and tokenization may
include identifying one or more tokens matching the input word
sequence. In some implementations, tokenization may include
identifying the highest ranked candidate token for a portion of the
request string as a resolved token for the portion of the request
string.
[0134] The relational search unit 3700 may implement one or more
finite state machines. For example, tokenization may include using
one or more finite state machines. A finite state machine may model
or represent a defined set of states and a defined set of
transitions between the states. A state may represent a condition
of the system represented by the finite state machine at a defined
temporal point. A finite state machine may transition from a state
(current state) to a subsequent state in response to input (e.g.,
input to the finite state machine). A transition may define one or
more actions or operations that the relational search unit 3700 may
implement. One or more of the finite state machines may be
non-deterministic, such that the finite state machine may
transition from a state to zero or more subsequent states.
[0135] The relational search unit 3700 may generate, instantiate,
or operate a tokenization finite state machine, which may represent
the respective tokenization grammar. Generating, instantiating, or
operating a finite state machine may include operating a finite
state machine traverser for traversing the finite state machine.
Instantiating the tokenization finite state machine may include
entering an empty state, indicating the absence of received input.
The relational search unit 3700 may initiate or execute an
operation, such as an entry operation, corresponding to the empty
state in response to entering the empty state. Subsequently, the
relational search unit 3700 may receive input data, and the
tokenization finite state machine may transition from the empty
state to a state corresponding to the received input data. In some
embodiments, the relational search unit 3700 may initiate one or
more data-queries in response to transitioning to or from a
respective state of a finite state machine. In the tokenization
finite state machine, a state may represent a possible next token
in the request string. The tokenization finite state machine may
transition between states based on one or more defined transition
weights, which may indicate a probability of transiting from a
state to a subsequent state.
[0136] The tokenization finite state machine may determine
tokenization based on probabilistic path utility. Probabilistic
path utility may rank or order multiple candidate traversal paths
for traversing the tokenization finite state machine based on the
request string. The candidate paths may be ranked or ordered based
on one or more defined probabilistic path utility metrics, which
may be evaluated in a defined sequence. For example, the
tokenization finite state machine may determine probabilistic path
utility by evaluating the weights of the respective candidate
transition paths, the lengths of the respective candidate
transition paths, or a combination thereof. In some
implementations, the weights of the respective candidate transition
paths may be evaluated with high priority relative to the lengths
of the respective candidate transition paths.
[0137] In some implementations, one or more transition paths
evaluated by the tokenization finite state machine may include a
bound state such that the candidate tokens available for
tokenization of a portion of the request string may be limited
based on the tokenization of a previously tokenized portion of the
request string.
[0138] Tokenization may include matching a portion of the request
string to one or more token types, such as a constant token type, a
column name token type, a value token type, a control-word token
type, a date value token type, a string value token type, or any
other token type defined by the low-latency data access and
analysis system 3000. A constant token type may be a fixed, or
invariant, token type, such as a numeric value. A column name token
type may correspond with a name of a column in the data model. A
value token type may correspond with an indexed data value. A
control-word token type may correspond with a defined set of
control-words. A date value token type may be similar to a
control-word token type and may correspond with a defined set of
control-words for describing temporal information. A string value
token type may correspond with an unindexed value.
[0139] Token matching may include ordering or weighting candidate
token matches based on one or more token matching metrics. Token
matching metrics may include whether a candidate match is within a
defined data scope, such as a defined set of tables, wherein a
candidate match outside the defined data scope (out-of-scope) may
be ordered or weighted lower than a candidate match within the
define data scope (in-scope). Token matching metrics may include
whether, or the degree to which, a candidate match increases query
complexity, such as by spanning multiple roots, wherein a candidate
match that increases complexity may be ordered or weighted lower
than a candidate match that does not increase complexity or
increases complexity to a lesser extent. Token matching metrics may
include whether the candidate match is an exact match or a partial
match, wherein a candidate match that is a partial may be ordered
or weighted lower than a candidate match that is an exact match. In
some implementations, the cardinality of the set of partial matches
may be limited to a defined value.
[0140] Token matching metrics may include a token score
(TokenScore), wherein a candidate match with a relatively low token
score may be ordered or weighted lower than a candidate match with
a relatively high token score. The token score for a candidate
match may be determined based one or more token scoring metrics.
The token scoring metrics may include a finite state machine
transition weight metric (FSMScore), wherein a weight of
transitioning from a current state of the tokenization finite state
machine to a state indicating a candidate matching token is the
finite state machine transition weight metric. The token scoring
metrics may include a cardinality penalty metric
(CardinalityScore), wherein a cardinality of values (e.g., unique
values) corresponding to the candidate matching token is used as a
penalty metric (inverse cardinality), which may reduce the token
score. The token scoring metrics may include an index utility
metric (IndexScore), wherein a defined utility value, such as one,
associated with an object, such as a column wherein the matching
token represents the column or a value from the column, is the
index utility metric. In some implementations, the defined utility
values may be configured, such as in response to user input, on a
per object (e.g., per column) basis. The token scoring metrics may
include a usage metric (UBRScore). The usage metric may be
determined based on a usage based ranking index, one or more usage
ranking metrics, or a combination thereof. Determining the usage
metric (UBRScore) may include determining a usage boost value
(UBRBoost). The token score may be determined based on a defined
combination of token scoring metrics. For example, determining the
token score may be expressed as the following:
TokenScore=FSMScore*(IndexScore+UBRScore*UBRBoost)+Min
(CardinalityScore,1).
[0141] Token matching may include grouping candidate token matches
by match type, ranking or ordering on a per-match type basis based
on token score, and ranking or ordering the match types. For
example, the match types may include a first match type for exact
matches (having the highest match type priority order), a second
match type for prefix matches on ontological data (having a match
type priority order lower than the first match type), a third match
type for substring matches on ontological data and prefix matches
on data values (having a match type priority order lower than the
second match type), a fourth match type for substring matches on
data values (having a match type priority order lower than the
third match type), and a fifth match type for matches omitted from
the first through fourth match types (having a match type priority
order lower than the fourth match type). Other match types and
match type orders may be used.
[0142] Tokenization may include ambiguity resolution. Ambiguity
resolution may include token ambiguity resolution, join-path
ambiguity resolution, or both. In some implementations, ambiguity
resolution may cease tokenization in response to the identification
of an automatic ambiguity resolution error or failure.
[0143] Token ambiguity may correspond with identifying two or more
exactly matching candidate matching tokens. Token ambiguity
resolution may be based on one or more token ambiguity resolution
metrics. The token ambiguity resolution metrics may include using
available previously resolved token matching or binding data and
token ambiguity may be resolved in favor of available previously
resolved token matching or binding data, other relevant tokens
resolved from the request string, or both. The token ambiguity
resolution may include resolving token ambiguity in favor of
integer constants. The token ambiguity resolution may include
resolving token ambiguity in favor of control-words, such as for
tokens at the end of a request for data, such as last, that are not
being edited.
[0144] Join-path ambiguity may correspond with identifying matching
tokens having two or more candidate join paths. Join-path ambiguity
resolution may be based on one or more join-path ambiguity
resolution metrics. The join-path ambiguity resolution metrics may
include using available previously resolved join-path binding data
and join-path ambiguity may be resolved in favor of available
previously resolved join-paths. The join-path ambiguity resolution
may include favoring join paths that include in-scope objects over
join paths that include out-of-scope objects. The join-path
ambiguity resolution metrics may include a complexity minimization
metric, which may favor a join path that omits or avoids increasing
complexity over join paths that increase complexity, such as a join
path that may introduce a chasm trap.
[0145] The relational search unit 3700 may identify a
resolved-request based on the request string. The resolved-request,
which may be database and visualization agnostic, may be expressed
or communicated as an ordered sequence of tokens representing the
request for data indicated by the request string. The relational
search unit 3700 may instantiate, or generate, one or more
resolved-request objects. For example, the relational search unit
3700 may create or store a resolved-request object corresponding to
the resolved-request in the distributed in-memory ontology unit
3500. The relational search unit 3700 may transmit, send, or
otherwise make available, the resolved-request to the semantic
interface unit 3600.
[0146] In some implementations, the relational search unit 3700 may
transmit, send, or otherwise make available, one or more
resolved-requests, or portions thereof, to the semantic interface
unit 3600 in response to finite state machine transitions. For
example, the relational search unit 3700 may instantiate a search
object in response to a first transition of a finite state machine.
The relational search unit 3700 may include a first search object
instruction in the search object in response to a second transition
of the finite state machine. The relational search unit 3700 may
send the search object including the first search object
instruction to the semantic interface unit 3600 in response to the
second transition of the finite state machine. The relational
search unit 3700 may include a second search object instruction in
the search object in response to a third transition of the finite
state machine. The relational search unit 3700 may send the search
object including the search object instruction, or a combination of
the first search object instruction and the second search object
instruction, to the semantic interface unit 3600 in response to the
third transition of the finite state machine. The search object
instructions may be represented using any annotation, instruction,
text, message, list, pseudo-code, comment, or the like, or any
combination thereof that may be converted, transcoded, or
translated into structured search instructions for retrieving data
from the low-latency data.
[0147] The relational search unit 3700 may provide an interface to
permit the creation of user-defined syntax. For example, a user may
associate a string with one or more tokens. Accordingly, when the
string is entered, the pre-associated tokens are returned in lieu
of searching for tokens to match the input.
[0148] The relational search unit 3700 may include a localization
unit (not expressly shown). The localization, globalization,
regionalization, or internationalization, unit may obtain source
data expressed in accordance with a source expressive-form and may
output destination data representing the source data, or a portion
thereof, and expressed using a destination expressive-form. The
data expressive-forms, such as the source expressive-form and the
destination expressive-form, may include regional or customary
forms of expression, such as numeric expression, temporal
expression, currency expression, alphabets, natural-language
elements, measurements, or the like. For example, the source
expressive-form may be expressed using a canonical-form, which may
include using a natural-language, which may be based on English,
and the destination expressive-form may be expressed using a
locale-specific form, which may include using another
natural-language, which may be a natural-language that differs from
the canonical-language. In another example, the destination
expressive-form and the source expressive-form may be
locale-specific expressive-forms and outputting the destination
expressive-form representation of the source expressive-form data
may include obtaining a canonical-form representation of the source
expressive-form data and obtaining the destination expressive-form
representation based on the canonical-form representation.
Although, for simplicity and clarity, the grammars described
herein, such as the data-analytics grammar and the natural language
data-analytics grammar, are described with relation to the
canonical expressive-form, the implementation of the respective
grammars, or portions thereof, described herein may implement
locale-specific expressive-forms. For example, the data-analytics
tokenizer may include multiple locale-specific data-analytics
tokenizers.
[0149] The natural language processing unit 3710 may receive input
data including a natural language string, such as a natural
language string generated in accordance with user input. The
natural language string may represent a data request expressed in
an unrestricted natural language form, for which data identified or
obtained prior to, or in conjunction with, receiving the natural
language string by the natural language processing unit 3710
indicating the semantic structure, correlation to the low-latency
data access and analysis system 3000, or both, for at least a
portion of the natural language string is unavailable or
incomplete. Although not shown separately in FIG. 3, in some
implementations, the natural language string may be generated or
determined based on processing an analog signal, or a digital
representation thereof, such as an audio stream or recording or a
video stream or recording, which may include using speech-to-text
conversion.
[0150] The natural language processing unit 3710 may analyze,
process, or evaluate the natural language string, or a portion
thereof, to generate or determine the semantic structure,
correlation to the low-latency data access and analysis system
3000, or both, for at least a portion of the natural language
string. For example, the natural language processing unit 3710 may
identify one or more words or terms in the natural language string
and may correlate the identified words to tokens defined in the
low-latency data access and analysis system 3000. In another
example, the natural language processing unit 3710 may identify a
semantic structure for the natural language string, or a portion
thereof. In another example, the natural language processing unit
3710 may identify a probabilistic intent for the natural language
string, or a portion thereof, which may correspond to an operative
feature of the low-latency data access and analysis system 3000,
such as retrieving data from the internal data, analyzing data the
internal data, or modifying the internal data.
[0151] The natural language processing unit 3710 may send,
transmit, or otherwise communicate request data indicating the
tokens, relationships, semantic data, probabilistic intent, or a
combination thereof or one or more portions thereof, identified
based on a natural language string to the relational search unit
3700.
[0152] The data utility unit 3720 may receive, process, and
maintain user-agnostic utility data, such as system configuration
data, user-specific utility data, such as utilization data, or both
user-agnostic and user-specific utility data. The utility data may
indicate whether a data portion, such as a column, a record, an
insight, or any other data portion, has high utility or low utility
within the system, such across all users of the system. For
example, the utility data may indicate that a defined column is a
high-utility column or a low-utility column. The data utility unit
3720 may store the utility data, such as using the low-latency data
structure. For example, in response to a user using, or accessing,
a data portion, data utility unit 3720 may store utility data
indicating the usage, or access, event for the data portion, which
may include incrementing a usage event counter associated with the
data portion. In some embodiments, the data utility unit 3720 may
receive the information indicating the usage, or access, event for
the data portion from the insight unit 3730, and the usage, or
access, event for the data portion may indicate that the usage is
associated with an insight.
[0153] The data utility unit 3720 may receive a signal, message, or
other communication, indicating a request for utility information.
The request for utility information may indicate an object or data
portion. The data utility unit 3720 may determine, identify, or
obtain utility data associated with the identified object or data
portion. The data utility unit 3720 may generate and send utility
response data responsive to the request that may indicate the
utility data associated with the identified object or data
portion.
[0154] The data utility unit 3720 may generate, maintain, operate,
or a combination thereof, one or more indexes, such as one or more
of a usage (or utility) index, a resolved-request index, or a
phrase index, based on the low-latency data stored in the
distributed in-memory database 3300, the low-latency data access
and analysis system 3000, or both.
[0155] The insight unit 3730 may automatically identify one or more
insights, which may be data other than data expressly requested by
a user, and which may be identified and prioritized, or both, based
on probabilistic utility.
[0156] The object search unit 3800 may generate, maintain, operate,
or a combination thereof, one or more object-indexes, which may be
based on the analytical-objects represented in the low-latency data
access and analysis system 3000, or a portion thereof, such as
pinboards, answers, and worksheets. An object-index may be a
defined data structure, or combination of data structures, for
storing analytical-object data in a form optimized for searching.
Although shown as a single unit in FIG. 3, the object search unit
3800 may interface with a distinct, separate, object indexing unit
(not expressly shown).
[0157] The object search unit 3800 may include an object-index
population interface, an object-index search interface, or both.
The object-index population interface may obtain and store, load,
or populate analytical-object data, or a portion thereof, in the
object-indexes. The object-index search interface may efficiently
access or retrieve analytical-object data from the object-indexes
such as by searching or traversing the object-indexes, or one or
more portions thereof. In some implementations, the object-index
population interface, or a portion thereof, may be a distinct,
independent unit.
[0158] The object-index population interface may populate, update,
or both the object-indexes, such as periodically, such as in
accordance with a defined temporal period, such as thirty minutes.
Populating, or updating, the object-indexes may include obtaining
object indexing data for indexing the analytical-objects
represented in the low-latency data access and analysis system
3000. For example, the object-index population interface may obtain
the analytical-object indexing data, such as from the distributed
in-memory ontology unit 3500. Populating, or updating, the
object-indexes may include generating or creating an indexing data
structure representing an object. The indexing data structure for
representing an object may differ from the data structure used for
representing the object in other components of the low-latency data
access and analysis system 3000, such as in the distributed
in-memory ontology unit 3500.
[0159] The object indexing data for an analytical-object may be a
subset of the object data for the analytical-object. The object
indexing data for an analytical-object may include an object
identifier for the analytical-object uniquely identifying the
analytical-object in the low-latency data access and analysis
system 3000, or in a defined data-domain within the low-latency
data access and analysis system 3000. The low-latency data access
and analysis system 3000 may uniquely, unambiguously, distinguish
an object from other objects based on the object identifier
associated with the object. The object indexing data for an
analytical-object may include data non-uniquely identifying the
object. The low-latency data access and analysis system 3000 may
identify one or more analytical-objects based on the non-uniquely
identifying data associated with the respective objects, or one or
more portions thereof. In some implementations, an object
identifier may be an ordered combination of non-uniquely
identifying object data that, as expressed in the ordered
combination, is uniquely identifying. The low-latency data access
and analysis system 3000 may enforce the uniqueness of the object
identifiers.
[0160] Populating, or updating, the object-indexes may include
indexing the analytical-object by including or storing the object
indexing data in the object-indexes. For example, the object
indexing data may include data for an analytical-object, the
object-indexes may omit data for the analytical-object, and the
object-index population interface may include or store the object
indexing data in an object-index. In another example, the object
indexing data may include data for an analytical-object, the
object-indexes may include data for the analytical-object, and the
object-index population interface may update the object indexing
data for the analytical-object in the object-indexes in accordance
with the object indexing data.
[0161] Populating, or updating, the object-indexes may include
obtaining object utility data for the analytical-objects
represented in the low-latency data access and analysis system
3000. For example, the object-index population interface may obtain
the object utility data, such as from the object utility unit 3810.
The object-index population interface may include the object
utility data in the object-indexes in association with the
corresponding objects.
[0162] In some implementations, the object-index population
interface may receive, obtain, or otherwise access the object
utility data from a distinct, independent, object utility data
population unit, which may read, obtain, or otherwise access object
utility data from the object utility unit 3810 and may send,
transmit, or otherwise provide, the object utility data to the
object search unit 3800. The object utility data population unit
may send, transmit, or otherwise provide, the object utility data
to the object search unit 3800 periodically, such as in accordance
with a defined temporal period, such as thirty minutes.
[0163] The object-index search interface may receive, access, or
otherwise obtain data expressing usage intent with respect to the
low-latency data access and analysis system 3000, which may
represent a request to access data in the low-latency data access
and analysis system 3000, which may represent a request to access
one or more analytical-objects represented in the low-latency data
access and analysis system 3000. The object-index search interface
may generate one or more object-index queries based on the data
expressing usage intent. The object-index search interface may
send, transmit, or otherwise make available the object-index
queries to one or more of the object-indexes.
[0164] The object-index search interface may receive, obtain, or
otherwise access object search results data indicating one or more
analytical-objects identified by searching or traversing the
object-indexes in accordance with the object-index queries. The
object-index search interface may sort or rank the object search
results data based on probabilistic utility in accordance with the
object utility data for the analytical-objects in the object search
results data. In some implementations, the object-index search
interface may include one or more object search ranking metrics
with the object-index queries and may receive the object search
results data sorted or ranked based on probabilistic utility in
accordance with the object utility data for the objects in the
object search results data and in accordance with the object search
ranking metrics.
[0165] For example, the data expressing usage intent may include a
user identifier, and the object search results data may include
object search results data sorted or ranked based on probabilistic
utility for the user. In another example, the data expressing usage
intent may include a user identifier and one or more search terms,
and the object search results data may include object search
results data sorted or ranked based on probabilistic utility for
the user identified by searching or traversing the object-indexes
in accordance with the search terms.
[0166] The object-index search interface may generate and send,
transmit, or otherwise make available the sorted or ranked object
search results data to another component of the low-latency data
access and analysis system 3000, such as for further processing and
display to the user.
[0167] The object utility unit 3810 may receive, process, and
maintain user-specific object utility data for objects represented
in the low-latency data access and analysis system 3000. The
user-specific object utility data may indicate whether an object
has high utility or low utility for the user.
[0168] The object utility unit 3810 may store the user-specific
object utility data, such as on a per-object basis, a per-activity
basis, or both. For example, in response to data indicating an
object access activity, such as a user using, viewing, or otherwise
accessing, an object, the object utility unit 3810 may store
user-specific object utility data indicating the object access
activity for the object, which may include incrementing an object
access activity counter associated with the object, which may be a
user-specific object access activity counter. In another example,
in response to data indicating an object storage activity, such as
a user storing an object, the object utility unit 3810 may store
user-specific object utility data indicating the object storage
activity for the object, which may include incrementing a storage
activity counter associated with the object, which may be a
user-specific object storage activity counter. The user-specific
object utility data may include temporal information, such as a
temporal location identifier associated with the object activity.
Other information associated with the object activity may be
included in the object utility data.
[0169] The object utility unit 3810 may receive a signal, message,
or other communication, indicating a request for object utility
information. The request for object utility information may
indicate one or more objects, one or more users, one or more
activities, temporal information, or a combination thereof. The
request for object utility information may indicate a request for
object utility data, object utility counter data, or both.
[0170] The object utility unit 3810 may determine, identify, or
obtain object utility data in accordance with the request for
object utility information. The object utility unit 3810 may
generate and send object utility response data responsive to the
request that may indicate the object utility data, or a portion
thereof, in accordance with the request for object utility
information.
[0171] For example, a request for object utility information may
indicate a user, an object, temporal information, such as
information indicating a temporal span, and an object activity,
such as the object access activity. The request for object utility
information may indicate a request for object utility counter data.
The object utility unit 3810 may determine, identify, or obtain
object utility counter data associated with the user, the object,
and the object activity having a temporal location within the
temporal span, and the object utility unit 3810 may generate and
send object utility response data including the identified object
utility counter data.
[0172] In some implementations, a request for object utility
information may indicate multiple users, or may omit indicating a
user, and the object utility unit 3810 may identify user-agnostic
object utility data aggregating the user-specific object utility
data. In some implementations, a request for object utility
information may indicate multiple objects, may omit indicating an
object, or may indicate an object type, such as answer, pinboard,
or worksheet, and the object utility unit 3810 may identify the
object utility data by aggregating the object utility data for
multiple objects in accordance with the request. Other object
utility aggregations may be used.
[0173] The system configuration unit 3820 implement or apply one or
more low-latency data access and analysis system configurations to
enable, disable, or configure one or more operative features of the
low-latency data access and analysis system 3000. The system
configuration unit 3820 may store data representing or defining the
one or more low-latency data access and analysis system
configurations. The system configuration unit 3820 may receive
signals or messages indicating input data, such as input data
generated via a system access interface, such as a user interface,
for accessing or modifying the low-latency data access and analysis
system configurations. The system configuration unit 3820 may
generate, modify, delete, or otherwise maintain the low-latency
data access and analysis system configurations, such as in response
to the input data. The system configuration unit 3820 may generate
or determine output data, and may output the output data, for a
system access interface, or a portion or portions thereof, for the
low-latency data access and analysis system configurations, such as
for presenting a user interface for the low-latency data access and
analysis system configurations. Although not shown in FIG. 3, the
system configuration unit 3820 may communicate with a repository,
such as an external centralized repository, of low-latency data
access and analysis system configurations; the system configuration
unit 3820 may receive one or more low-latency data access and
analysis system configurations from the repository, and may control
or configure one or more operative features of the low-latency data
access and analysis system 3000 in response to receiving one or
more low-latency data access and analysis system configurations
from the repository.
[0174] The user customization unit 3830 may receive, process, and
maintain user-specific utility data, such as user defined
configuration data, user defined preference data, or a combination
thereof. The user-specific utility data may indicate whether a data
portion, such as a column, a record, an insight, or any other data
portion or object, has high utility or low utility to an identified
user. For example, the user-specific utility data may indicate that
a defined column is a high-utility column or a low-utility column.
The user customization unit 3830 may store the user-specific
utility data, such as using the low-latency data structure. The
user customization unit 3830 may store the feedback at an
individual level and may include the context in which feedback was
received from the user. Feedback may be stored in a disk-based
system. In some implementations, feedback may be stored in an
in-memory storage.
[0175] The system access interface unit 3900 may interface with, or
communicate with, a system access unit (not shown in FIG. 3), which
may be a client device, a user device, or another external device
or system, or a combination thereof, to provide access to the
internal data, features of the low-latency data access and analysis
system 3000, or a combination thereof. For example, the system
access interface unit 3900 may receive signals, message, or other
communications representing interactions with the internal data,
such as data expressing usage intent and may output response
messages, signals, or other communications responsive to the
received requests.
[0176] The system access interface unit 3900 may generate data for
presenting a user interface, or one or more portions thereof, for
the low-latency data access and analysis system 3000. For example,
the system access interface unit 3900 may generate instructions for
rendering, or otherwise presenting, the user interface, or one or
more portions thereof and may transmit, or otherwise make
available, the instructions for rendering, or otherwise presenting,
the user interface, or one or more portions thereof to the system
access unit, for presentation to a user of the system access unit.
For example, the system access unit may present the user interface
via a web browser or a web application and the instructions may be
in the form of HTML, JavaScript, or the like.
[0177] In an example, the system access interface unit 3900 may
include a data-analytics field user interface element in the user
interface. The data-analytics field user interface element may be
an unstructured string user input element or field. The system
access unit may display the unstructured string user input element.
The system access unit may receive input data, such as user input
data, corresponding to the unstructured string user input element.
The system access unit may transmit, or otherwise make available,
the unstructured string user input to the system access interface
unit 3900. The user interface may include other user interface
elements and the system access unit may transmit, or otherwise make
available, other user input data to the system access interface
unit 3900.
[0178] The system access interface unit 3900 may obtain the user
input data, such as the unstructured string, from the system access
unit. The system access interface unit 3900 may transmit, or
otherwise make available, the user input data to one or more of the
other components of the low-latency data access and analysis system
3000.
[0179] In some embodiments, the system access interface unit 3900
may obtain the unstructured string user input as a sequence of
individual characters or symbols, and the system access interface
unit 3900 may sequentially transmit, or otherwise make available,
individual or groups of characters or symbols of the user input
data to one or more of the other components of the low-latency data
access and analysis system 3000.
[0180] In some embodiments, system access interface unit 3900 may
obtain the unstructured string user input may as a sequence of
individual characters or symbols, the system access interface unit
3900 may aggregate the sequence of individual characters or
symbols, and may sequentially transmit, or otherwise make
available, a current aggregation of the received user input data to
one or more of the other components of the low-latency data access
and analysis system 3000, in response to receiving respective
characters or symbols from the sequence, such as on a per-character
or per-symbol basis.
[0181] The real-time collaboration unit 3910 may receive signals or
messages representing input received in accordance with multiple
users, or multiple system access devices, associated with a
collaboration context or session, may output data, such as
visualizations, generated or determined by the low-latency data
access and analysis system 3000 to multiple users associated with
the collaboration context or session, or both. The real-time
collaboration unit 3910 may receive signals or messages
representing input received in accordance with one or more users
indicating a request to establish a collaboration context or
session, and may generate, maintain, or modify collaboration data
representing the collaboration context or session, such as a
collaboration session identifier. The real-time collaboration unit
3910 may receive signals or messages representing input received in
accordance with one or more users indicating a request to
participate in, or otherwise associate with, a currently active
collaboration context or session, and may associate the one or more
users with the currently active collaboration context or session.
In some implementations, the input, output, or both, of the
real-time collaboration unit 3910 may include synchronization data,
such as temporal data, that may be used to maintain
synchronization, with respect to the collaboration context or
session, among the low-latency data access and analysis system 3000
and one or more system access devices associated with, or otherwise
accessing, the collaboration context or session.
[0182] The third-party integration unit 3920 may include an
electronic communication interface, such as an application
programming interface (API), for interfacing or communicating
between an external, such as third-party, application or system,
and the low-latency data access and analysis system 3000. For
example, the third-party integration unit 3920 may include an
electronic communication interface to transfer data between the
low-latency data access and analysis system 3000 and one or more
external applications or systems, such as by importing data into
the low-latency data access and analysis system 3000 from the
external applications or systems or exporting data from the
low-latency data access and analysis system 3000 to the external
applications or systems. For example, the third-party integration
unit 3920 may include an electronic communication interface for
electronic communication with an external exchange, transfer, load
(ETL) system, which may import data into the low-latency data
access and analysis system 3000 from an external data source or may
export data from the low-latency data access and analysis system
3000 to an external data repository. In another example, the
third-party integration unit 3920 may include an electronic
communication interface for electronic communication with external
machine learning analysis software, which may export data from the
low-latency data access and analysis system 3000 to the external
machine learning analysis software and may import data into the
low-latency data access and analysis system 3000 from the external
machine learning analysis software. The third-party integration
unit 3920 may transfer data independent of, or in conjunction with,
the system access interface unit 3900, the enterprise data
interface unit 3400, or both.
[0183] The persistent storage unit 3930 may include an interface
for storing data on, accessing data from, or both, one or more
persistent data storage devices or systems. For example, the
persistent storage unit 3930 may include one or more persistent
data storage devices, such as the static memory 1200 shown in FIG.
1. Although shown as a single unit in FIG. 3, the persistent
storage unit 3930 may include multiple components, such as in a
distributed or clustered configuration. The persistent storage unit
3930 may include one or more internal interfaces, such as
electronic communication or application programming interfaces, for
receiving data from, sending data to, or both other components of
the low-latency data access and analysis system 3000. The
persistent storage unit 3930 may include one or more external
interfaces, such as electronic communication or application
programming interfaces, for receiving data from, sending data to,
or both, one or more external systems or devices, such as an
external persistent storage system. For example, the persistent
storage unit 3930 may include an internal interface for obtaining
key-value tuple data from other components of the low-latency data
access and analysis system 3000, an external interface for sending
the key-value tuple data to, or storing the key-value tuple data
on, an external persistent storage system, an external interface
for obtaining, or otherwise accessing, the key-value tuple data
from the external persistent storage system, and an internal
key-value tuple data for sending, or otherwise making available,
the key-value tuple data to other components of the low-latency
data access and analysis system 3000. In another example, the
persistent storage unit 3930 may include a first external interface
for storing data on, or obtaining data from, a first external
persistent storage system, and a second external interface for
storing data on, or obtaining data from, a second external
persistent storage system.
[0184] FIG. 4 is a flow diagram of a method of analytical data
access with system-defined chronometry 4000 in a low-latency data
access and analysis system. Analytical data access with
system-defined chronometry 4000 may be implemented in one or more
components of a low-latency data access and analysis system, such
as the low-latency data access and analysis system 3000 shown in
FIG. 3, such as in a distributed in-memory database, such as the
distributed in-memory database 3300 shown in FIG. 3, a relational
search unit, such as the relational search unit 3700 shown in FIG.
3, a natural language processing unit, such as the natural language
processing unit 3710 shown in FIG. 3, and a system access interface
unit, such as the system access interface unit 3900 shown in FIG.
3.
[0185] Analytical data access in a low-latency data access and
analysis system with system-defined chronometry 4000 includes
operating the low-latency data access and analysis system (LLDAS)
at 4100, receiving data expressing usage intent (DEUI) at 4200,
generating a resolved-request at 4300, obtaining response data at
4400, and outputting a response at 4500.
[0186] Operating the low-latency data access and analysis system at
4100 may include generating the low-latency data access and
analysis system. Generating the low-latency data access and
analysis system may include defining chronometry (system-defined
chronometry) for the low-latency data access and analysis system at
4110.
[0187] The data of the low-latency data access and analysis system,
such as the data stored in or accessed by the low-latency data
access and analysis system, may include temporal data indicating or
representing one or more points, locations, or intervals, in a
temporal continuum. A temporal point represents a discrete, or
unique, point in the temporal continuum, such that the ordered
contiguous sequence of temporal points comprises the temporal
continuum. A temporal location represents a discrete, or unique,
point, or a defined ordered contiguous sequence of points, in the
temporal continuum. A temporal interval represents a defined
cardinality of sequential contiguous temporal points or
locations.
[0188] The system-defined chronometry defines the measurement,
storage, processing, organization, scale, expression, and
representation of time and temporal data in the low-latency data
access and analysis system. For example, the temporal continuum may
correspond with natural time, or an approximation thereof, and the
system-defined chronometry may correspond with a Gregorian
calendar, or a defined variant thereof. The system-defined
chronometry defines one or more chronometric units, which may be
nominal, or named, representations of respective temporal
intervals. A reference chronometric unit, such as a `second`
chronometric unit, may represent a minimal temporal interval in the
low-latency data access and analysis system.
[0189] The low-latency data access and analysis system may define
or describe one or more chronometric unit types, such as a `second`
chronometric unit type, a `minute` chronometric unit type, an
`hour` chronometric unit type, a `day` chronometric unit type, a
`week` chronometric unit type, a `month` chronometric unit type, a
`quarter` chronometric unit type, a `year` chronometric unit type,
or any other type of chronometric unit.
[0190] A temporal point may be represented, such as stored or
processed, in the low-latency data access and analysis system as an
epoch value, which may be an integer value, such that each temporal
point from the contiguous sequence of temporal points that
comprises the temporal continuum corresponds with a respective
epoch value. A temporal location may be represented in the
low-latency data access and analysis system as an epoch value and
may be expressed in the low-latency data access and analysis system
using one or more chronometric units, or respective values
thereof.
[0191] Generating or defining the system-defined chronometry may
include, for example, generating, such as by writing and storing,
program instructions (code) describing the chronometry, or aspects
thereof. In some implementations, the system-defined chronometry,
or a portion thereof, may be compiled. Aspects of the
system-defined chronometry may be defined in one or more of the
components of the low-latency data access and analysis system, such
as one or more of the components 3100-3920 of the low-latency data
access and analysis system 3000 shown in FIG. 3.
[0192] One or more aspects of the system-defined chronometry may be
defined by the operating environment of the low-latency data access
and analysis system, such as by a hardware component, an operating
system, or a combination thereof. For example, a hardware
component, such as a system clock (clock circuit) may define the
temporal interval of the reference chronometric unit and an
operating system may define one or more other chronometric units
with reference to the reference chronometric unit.
[0193] The system-defined chronometry defines a minute chronometric
unit as an ordered contiguous sequence of sixty (60) minimal
chronometric units (seconds). The system-defined chronometry
defines an hour chronometric unit as an ordered contiguous sequence
of sixty (60) minute chronometric units. The system-defined
chronometry defines a day chronometric unit as an ordered
contiguous sequence of twenty-four (24) hour chronometric units.
The system-defined chronometry defines a week chronometric unit as
an ordered contiguous sequence of seven (7) day chronometric units.
The system-defined chronometry defines a month chronometric unit as
an ordered contiguous sequence of twenty-eight (28), twenty-nine
(29), thirty (30), or thirty-one (31) day chronometric units. The
system-defined chronometry defines a quarter chronometric unit as
an ordered contiguous sequence of three (3) month chronometric
units. The system-defined chronometry defines a year chronometric
unit as an ordered contiguous sequence of twelve (12) month
chronometric units. The system-defined chronometry defines the year
chronometric unit as an ordered contiguous sequence of four (4)
quarter chronometric units.
[0194] The system-defined chronometry defines respective
descriptors, such as a day-of-week-name, for instances of the day
chronometric unit with respect to the ordered contiguous sequence
of day chronometric units corresponding to, or within, an instance
of a week chronometric unit of the canonical chronometry. The
respective descriptors for the instances of the day chronometric
unit with respect to the ordered contiguous sequence of day
chronometric units corresponding to, or within, an instance of a
week chronometric unit of the canonical chronometry are "Monday",
"Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", and
"Sunday".
[0195] The system-defined chronometry defines respective
descriptors, such as a month-name, for instances of the month
chronometric unit with respect to the ordered contiguous sequence
of month chronometric units corresponding to, or within, an
instance of a year chronometric unit of the canonical chronometry.
The respective descriptors for the instances of the month
chronometric unit with respect to the ordered contiguous sequence
of month chronometric units corresponding to, or within, an
instance of a year chronometric unit of the canonical chronometry
are "January", "February", "March", "April", "May", "June", "July",
"August", "September", "October", "November", and "December".
[0196] Defined ordinals may be defined for the respective defined
ordered contiguous sequences of chronometric units, such as integer
value ordinals. For example, for the ordered contiguous sequence of
four (4) quarter chronometric units of the year chronometric unit,
the ordinal (quarter-ordinal) of the first quarter chronometric
unit may be one (1), the ordinal of the second quarter chronometric
unit may be two (2), the ordinal of the third quarter chronometric
unit may be three (3), and the ordinal for the fourth quarter
chronometric unit may be four (4). Other ordinals may be used, such
as day-ordinals, for the ordered contiguous sequence of day
chronometric units of the week chronometric unit, and
month-ordinals, for the ordered contiguous sequence of month
chronometric units of the year chronometric unit.
[0197] The system-defined chronometry may define or describe one or
more nominal values, descriptors, or display attributes, such a
system-defined display name, for respective chronometric units of
the respective defined ordered contiguous sequences of chronometric
units. For example, defining the system access interface unit may
include defining one or more system-defined temporal or
chronometric data visualizations, which may include the display
attributes, for output or presentation of the temporal data. For
example, for the ordered contiguous sequence of four (4) quarter
chronometric units of the year chronometric unit, the defined
display value of the first quarter chronometric unit may be "Q1",
the defined display value of the second quarter chronometric unit
may be "Q2", the defined display value of the third quarter
chronometric unit may be "Q3", and the defined display value of the
fourth quarter chronometric unit may be "Q4". The system access
interface unit may generate output or presentation data
representing date values with reference to the ordered contiguous
sequence of four (4) quarter chronometric units of the year
chronometric unit by determining a chronometric quarter
corresponding to the date value and outputting the corresponding
display value.
[0198] Generating the low-latency data access and analysis system
may include defining and configuring one or more configurable
chronometric ordinal offsets (system offsets) for the low-latency
data access and analysis system at 4120. The configurable
chronometric ordinal offsets, or other aspects of the low-latency
data access and analysis system, may be defined on a
domain-agnostic basis and may be configurable on a domain-specific
basis. Domains may be hierarchical. For example, the low-latency
data access and analysis system (system-domain) may include data
associated with one or more organizations (organization-domain).
Aspects of the low-latency data access and analysis system-defined
for or associated with a domain may be domain-agnostic with respect
to sub-domains of the domain. For example, a chronometric ordinal
offset defined for the system-domain may be domain-agnostic with
respect to sub-domains of the system-domain, such as the
organization-domains. Aspects of the low-latency data access and
analysis system-defined for or associated with a domain may be
domain-specific with respect to peer-domains and super-domains of
the domain. For example, a chronometric ordinal offset defined for
an organization-domain may be domain-specific with respect other
organization-domains and with respect to the system-domain. A
sub-domain for which a value of a configurable chronometric offset
is unconfigured may inherit the value of the configurable
chronometric offset configured for the super-domain of the
sub-domain (cascading). For example, the value of a configurable
chronometric ordinal offset that is unconfigured for an
organization-domain may be the value of the corresponding
configurable chronometric ordinal offset that is configured for the
system-domain. In some implementations, defining, configuring, or
both, the system offsets may be omitted, as indicated by the broken
line border at 4120.
[0199] The configurable chronometric ordinal offsets may be defined
for the respective defined ordered contiguous sequences of
chronometric units. For example, generating the low-latency data
access and analysis system may include defining a
first-month-of-year configurable chronometric ordinal offset for
the low-latency data access and analysis system with respect to the
ordered contiguous sequence of twelve (12) month chronometric units
of the year chronometric unit. The first-month-of-year configurable
chronometric ordinal offset may be defined as an integer value in a
defined range, such as [0-11], which may be based on the ordinal
values of the ordered contiguous sequence of twelve (12) month
chronometric units of the year chronometric unit. The value of the
first-month-of-year configurable chronometric ordinal offset may be
configurable on a domain-specific specific basis. A domain-agnostic
value for the first-month-of-year configurable chronometric offset
may be configured for the low-latency data access and analysis
system. In another example, generating the low-latency data access
and analysis system may include defining a first-day-of-week
configurable chronometric ordinal offset for the low-latency data
access and analysis system with respect to the ordered contiguous
sequence of seven (7) day chronometric units of the week
chronometric unit. The first-day-of-week configurable chronometric
ordinal offset may be defined as an integer value in a defined
range, such as [0-6], which may be based on the ordinal values of
the ordered contiguous sequence of seven (7) day chronometric units
of the week chronometric unit. The value of the first-day-of-week
configurable chronometric ordinal offset may be configurable on a
domain-specific specific basis. A domain-agnostic value for the
first-day-of-week configurable chronometric offset may be
configured for the low-latency data access and analysis system. In
some implementations, domain-specific configurable chronometric
ordinal offsets, other than the first-month-of-year configurable
chronometric ordinal offset and the first-day-of-week configurable
chronometric ordinal offset, may be undefined, unavailable, or
otherwise omitted.
[0200] Generating the low-latency data access and analysis system
at 4100 may include generating a system-defined ontological index,
such as the control-word index. Generating the system-defined
ontological index may include including one or more chronometric
tokens for the system-defined chronometry in the system-defined
ontological index. For example, tokens corresponding to the
chronometric units for the system-defined chronometry, such as
"date", "day", "days", "daily", "week", "weeks", "weekly", "month",
"months", "monthly", "quarter", "quarters", "quarterly", "year",
"years", "yearly", and the like, may be included in the
system-defined ontological index.
[0201] Generating the low-latency data access and analysis system
at 4100 may include defining a finite state machine (FSM) for the
low-latency data access and analysis system, such as the finite
state machine implemented by the relational search unit 3700 shown
in FIG. 3. Generating the finite state machine includes identifying
the names or descriptors for chronometric units for the
system-defined chronometry, such as day-of-week name, month-of-year
name, quarter-of-year name, or year names and including the names
or descriptors for chronometric units for the system-defined
chronometry in the finite state machine as state transition
identifiers.
[0202] Generating the low-latency data access and analysis system
at 4100 may include defining one or more chronometric phrase
patterns for the system-defined chronometry. For example,
generating the low-latency data access and analysis system at 4100
may include generating a relational search unit of the low-latency
data access and analysis system, such as the relational search unit
3700 shown in FIG. 3, which may include defining the chronometric
phrase patterns for the system-defined chronometry. A phrase
pattern may indicate an ordered sequence of one or more phrase
parts. The phrase parts may be defined tokens or token types. The
respective phrase parts in a phrase pattern may have corresponding
positions in the sequence of the phrase pattern. A chronometric
phrase pattern is a phrase pattern that includes one or more
chronometric tokens or token types.
[0203] Generating the low-latency data access and analysis system
at 4100 may include defining one or more chronometric transforms
for the system-defined chronometry. For example, generating the
low-latency data access and analysis system at 4100 may include
generating a semantic interface unit of the low-latency data access
and analysis system, such as the semantic interface unit 3600 shown
in FIG. 3, which may include defining the chronometric transforms
for the system-defined chronometry. A chronometric transform for
the system-defined chronometry may generate chronometric criteria
based on the resolved-request and the system-defined
chronometry.
[0204] Data expressing usage intent may be received at 4200. A
system access interface unit of the low-latency data access and
analysis system, such as the system access interface unit 3900
shown in FIG. 3, may receive data, such as user input data,
including the data expressing usage intent. The system access
interface unit may send, or otherwise make available, the data
expressing usage intent, or a portion thereof, to a relational
search unit of the low-latency data access and analysis system,
such as the relational search unit 3700 shown in FIG. 3.
[0205] A resolved-request may be generated at 4300. The relational
search unit may receive, or otherwise access, the data expressing
usage intent. The relational search unit may process, parse,
identify semantics, tokenize, or a combination thereof, the request
for data to generate a resolved-request, which may include
identifying a database and visualization agnostic ordered sequence
of tokens based on the data expressing usage intent. In some
implementations, the data expressing usage intent obtained at 4200
may identify a previously resolved-request and obtaining the
resolved-request at 4300 may include obtaining, such as by reading,
or otherwise accessing, previously stored data representing the
resolved-request.
[0206] Obtaining the resolved-request at 4300 may include
identifying one or more chronometric phrases at 4310. Identifying
the chronometric phrases may include traversing a finite state
machine at 4312. Identifying the chronometric phrases may include
identifying one or more chronometric tokens by traversing one or
more system-defined indices, such as the system-defined ontological
index, at 4314. The finite state machine may include criteria for
identifying a transition based on chronometric types, chronometric
unit names or descriptors, or a combination of chronometric types
and chronometric descriptors.
[0207] For example, the data expressing usage intent may include
the string "precipitation last year" (request string). Obtaining
the resolved-request at 4300 may include traversing the finite
state machine at 4312 to identify one or more indices for
identifying respective tokens corresponding to respective portions,
such as words, of the request string, such as a `precipitation`
token, a `last` token, and a `year` token. The request string
portion "precipitation" may be tokenized by traversing the
ontological index, and a `precipitation` column in a table in the
distributed in-memory database of the low-latency data access and
analysis system, such as the distributed in-memory database 3300,
may be identified. The portion of the resolved-request
corresponding to the request string portion "precipitation", the
`precipitation` token, and the `precipitation` column, may be
identified as indicating an aggregation, such as a summation, of
the data from the `precipitation` column in the table in the
constituent data. The `precipitation` token may be identified as a
phrase. Data indicating the identified column and the identified
aggregation may be included in the resolved-request. Traversing the
finite state machine may include identifying the system-defined
keyword index as a candidate index for tokenizing the request
string portion "last". The system-defined token corresponding to
the request string portion "last" may be identified by traversing
the system-defined keyword index. The request string portion "last"
may be identified as corresponding to a first part of a phrase,
such as based on the chronometric phrase patterns defined at 4100.
Traversing the finite state machine may include identifying the
system-defined keyword index as a candidate index for tokenizing
the request string portion "year". The system-defined token
corresponding to the request string portion "year" may be
identified by traversing the system-defined keyword index. A `date`
column may be identified from one or more candidate tables, such as
the table identified for the `precipitation` token, as
corresponding to the request string portion "year", or the `year`
token. The token sequence `last` followed by `year` may be
identified as a chronometric phrase, such as a phrase defined for
the system-defined chronometry at 4110, such as based on the
chronometric phrase patterns defined at 4100. Data indicating a
chronometric filter, or condition, for the resolved-request,
corresponding to the chronometric phrase and the respective `date`
column, may be included in the resolved-request. The chronometric
filter, or condition, may be a chronometric filter defined at 4110.
Chronometric filters, or conditions, other than the chronometric
filter defined at 4110 may be unavailable.
[0208] In another example, the data expressing usage intent may
include the string "precipitation January" (request string).
Obtaining the resolved-request at 4300 may include traversing the
finite state machine at 4312 to identify one or more indices for
identifying respective tokens corresponding to respective portions,
such as words, of the request string, such as a `precipitation`
token and a `January` token. The request string portion
"precipitation" may be tokenized by traversing the ontological
index, and a `precipitation` column in a table in the distributed
in-memory database of the low-latency data access and analysis
system, such as the distributed in-memory database 3300, may be
identified. The portion of the resolved-request corresponding to
the request string portion "precipitation", the `precipitation`
token, and the `precipitation` column, may be identified as
indicating an aggregation, such as a summation, of the data from
the `precipitation` column in the table in the constituent data.
The `precipitation` token may be identified as a phrase. Data
indicating the identified column and the identified aggregation may
be included in the resolved-request. Traversing the finite state
machine may include identifying a finite state machine transition,
tokenizing, or both, the request string portion "January", such as
based on the names or descriptors for chronometric units for the
system-defined chronometry defined in the finite state machine as
transition criteria. Traversing the system-defined keyword index
may include filtering the system-defined keyword index based on one
or more candidate edges in the current state of the finite state
machine. In some implementations, tokenizing the request string
portion "January" may omit traversing an index. The request string
portion "January" may be identified as corresponding to a phrase,
such as based on the chronometric phrase patterns defined at 4100.
A `date` column may be identified from one or more candidate
tables, such as the table identified for the `precipitation` token,
as corresponding to the request string portion "January", or the
`January` token. Data indicating a chronometric filter for the
system-defined chronometry, or condition, for the resolved-request,
corresponding to the `January` token and the respective `date`
column, may be included in the resolved-request. The chronometric
filter, or condition, may be a chronometric filter defined at 4110.
Chronometric filters, or conditions, other than the chronometric
filter defined at 4110 may be unavailable.
[0209] In some implementations, obtaining the resolved-request at
4300 may include identifying one or more portions of the input
string as unresolved (unresolved-request). For example, the data
expressing usage intent may include a string portion indicating a
chronometric unit other than the chronometric units defined at
4100, which may be otherwise omitted from the indices of the
low-latency data access and analysis system, that may be identified
as unresolved. For example, the data expressing usage intent may
include the string "precipitation last decade", the system-defined
ontological index may omit a token corresponding to the string
"decade", and the string portion "decade" may be identified as
unresolved. Obtaining data at 4400 may be omitted for an
unresolved-request and outputting the response at 4500 may include
outputting a response indicating the unresolved-request.
[0210] Results data may be obtained at 4400. The relational search
unit may send, or otherwise make available, the resolved-request to
a semantic interface of the low-latency data access and analysis
system, such as the semantic interface unit 3600 shown in FIG. 3.
The semantic interface may receive, or otherwise access, the
resolved-request.
[0211] The semantic interface unit may process or transform the
received resolved-request to generate one or more corresponding
data-queries, at 4410, that are compatible with a distributed
in-memory database of the low-latency data access and analysis
system, such as the distributed in-memory database 3300. The
semantic interface may determine that the resolved-request includes
one or more chronometric filters and may generate chronometric
criteria based on the resolved-request, the system-defined
chronometry, or both, using one or more chronometric
transforms.
[0212] For example, the resolved-request may include data
indicating a chronometric filter, which may correspond to a
chronometric phrase identified at 4310 based on a chronometric
phrase pattern defined at 4100, and the semantic interface unit in
accordance with a chronometric transform may generate chronometric
criteria corresponding to the data indicating a chronometric
filter, which may include determining, or otherwise obtaining, data
indicating one or more temporal segments. For example, the
resolved-request may include data indicating a chronometric filter
corresponding to the phrase `last year`, and the semantic interface
unit, in accordance with the chronometric transform corresponding
to the phrase `last year`, may determine, or otherwise obtain, data
representing a temporal segment corresponding to the year
chronometric unit prior to a current year chronometric unit as
described in the system-defined chronometry, which may be expressed
as a range of epoch values from, such as equal to or greater than,
a minimum epoch value corresponding to the first day chronometric
unit of the first month chronometric unit, defined in the
system-defined chronometry as the first of January, of the year
chronometric unit immediately preceding the current year
chronometric unit to, such as less than, a minimum epoch value
corresponding to the first day chronometric unit of the first month
chronometric unit, defined in the system-defined chronometry as the
first of January, of the current year chronometric unit.
[0213] The semantic interface unit, such as in accordance with the
chronometric transform corresponding to the phrase `last year`, may
obtain, or otherwise access, one or more of the configurable
chronometric ordinal offsets defined at 4120. For example, the
semantic interface unit may determine whether a value of a
configurable chronometric ordinal offset is available for the
domain corresponding to the resolved-request. The semantic
interface unit may determine that a value of the configurable
chronometric ordinal offset is available for the domain
corresponding to the resolved-request and may offset one or more of
the chronometric criteria responsive to the value of the
corresponding configurable chronometric ordinal offset.
[0214] For example, offsetting the chronometric criteria in
accordance with the first-month-of-year configurable chronometric
ordinal offset may include determining the range of epoch values
for a temporal segment corresponding to a year chronometric unit by
obtaining the value of the first-month-of-year configurable
chronometric ordinal offset and determining a sum of an ordinal
value of a first month chronometric unit of the year chronometric
unit, as defined in the system-defined chronometry, and the value
of the first-month-of-year configurable chronometric ordinal
offset. For example, the ordinal value of a first month
chronometric unit of the year chronometric unit defined in the
system-defined chronometry may be zero (0), corresponding to the
month chronometric unit January, the value of the
first-month-of-year configurable chronometric ordinal offset may be
one (1), indicating an offset of one month chronometric unit,
indicating that the first month chronometric unit of the year
chronometric unit for the corresponding domain is the second month
chronometric unit, February.
[0215] For example, the resolved-request may include data
indicating a chronometric filter corresponding to the phrase `last
year`, a value of the first-month-of-year configurable chronometric
ordinal offset may be unavailable for the domain corresponding to
the data-query, or the semantic interface unit may obtain a value
of zero (0), and the semantic interface unit, in accordance with
the chronometric transform corresponding to the phrase `last year`,
may determine, or otherwise obtain, the chronometric criteria
including data representing a temporal segment corresponding to the
year chronometric unit sequentially immediately preceding a current
year chronometric unit as described in the system-defined
chronometry, which may be expressed as a range of epoch values
from, such as equal to or greater than, a minimum epoch value
corresponding to the first day chronometric unit of the first month
chronometric unit, defined in the system-defined chronometry as the
first of January, of the year chronometric unit immediately
preceding the current year chronometric unit to, such as less than,
a minimum epoch value corresponding to the first day chronometric
unit of the first month chronometric unit, defined in the
system-defined chronometry as the first of January, of the current
year chronometric unit.
[0216] In another example, the resolved-request may include data
indicating a chronometric filter corresponding to the phrase `last
year`, the semantic interface unit may obtain a value, such as one
(1), of the first-month-of-year configurable chronometric ordinal
offset for the domain corresponding to the data-query, and the
semantic interface unit, in accordance with the chronometric
transform corresponding to the phrase `last year`, may determine,
or otherwise obtain, data representing the temporal segment
corresponding to the year chronometric unit sequentially
immediately preceding a current year chronometric unit as described
in the chronometric offset in accordance with the value (1) of the
first-month-of-year configurable chronometric ordinal offset, which
may be expressed as a range of epoch values from, such as equal to
or greater than, a minimum epoch value corresponding to the first
day chronometric unit of the second month chronometric unit,
defined in the system-defined chronometry as the first of February,
of the year chronometric unit immediately preceding the current
year chronometric unit (lower bound) to, such as less than, a
minimum epoch value corresponding to the first day chronometric
unit of the second month chronometric unit, defined in the
system-defined chronometry as the first of February, of the current
year chronometric unit (upper bound).
[0217] In some implementations, the data indicating the temporal
segments may be omitted, and the semantic interface unit may
include data compatible with the distributed in-memory database
indicating the chronometric filter or condition in the data-query
as the chronometric criteria. For example, the resolved-request may
include data indicating a chronometric condition for the
system-defined chronometry corresponding to the phrase `month of
year`, the semantic interface unit may obtain a value, such as one
(1), of the first-month-of-year configurable chronometric ordinal
offset for the domain corresponding to the data-query, and the
semantic interface unit, in accordance with the chronometric
transform corresponding to the phrase `month of year`, may include
data indicating the chronometric condition and the value of the
first-month-of-year configurable chronometric ordinal offset as the
chronometric criteria.
[0218] The semantic interface unit may send, or otherwise make
available, the data-queries to the distributed in-memory database
of the low-latency data access and analysis system, such as the
distributed in-memory database 3300 shown in FIG. 3. The
distributed in-memory database may receive, or otherwise access,
the data-queries. The distributed in-memory database may execute or
perform one or more queries to generate or obtain response data
responsive to the data-query based on the low-latency data at
4420.
[0219] In some implementations, the data-query may include a
chronometric criterion indicating a temporal segment and executing
the corresponding queries may include obtaining the response data
in accordance with the defined temporal segment. For example, the
data-query may include a request for a summation of values from a
`precipitation` column from a table in the distributed in-memory
database subject to a chronometric criterion indicating a temporal
segment and indicating a corresponding `date` column in the
distributed in-memory database that includes temporal location
values corresponding to respective values from the `precipitation`
column and executing the corresponding queries may include
obtaining the response data in accordance with the defined temporal
segment by aggregating values from the `precipitation` column that
correspond with epoch values from the corresponding `date` column
that are greater than or equal to the lower bound epoch value and
are less than the upper bound epoch value.
[0220] In some implementations, the data-query may include a
chronometric criterion indicating the chronometric filter or
condition and executing the corresponding queries may include
obtaining the response data in accordance with the chronometric
filter or condition. For example, the distributed in-memory
database may include one or more system-defined formulas,
functions, or algorithms, defining the execution of respective
chronometric filters or conditions. For example, the chronometric
criterion may include an indication of the chronometric condition
corresponding to the phase `month of year`, an indication of a data
portion, such as a `date` column of a table in the distributed
in-memory database, including temporal location values
corresponding to the chronometric condition, and the value of the
first-month-of-year configurable chronometric ordinal offset, and
the distributed in-memory database may include a system-defined
formula, function, or algorithm, defining the execution of the
chronometric conditions as including obtaining an offset, or domain
specific, ordinal value by determining a result, remainder, or
modulus, of the Euclidean division of a result of subtracting the
value of the first-month-of-year configurable chronometric ordinal
offset from a sum of the system-defined ordinal value and the
cardinality (12) of the ordered contiguous sequence of month
chronometric units of the year chronometric unit and a
system-defined ordinal value (date ordinal) for the month
chronometric unit corresponding to respective values from the
`date` column obtained by executing a system-defined `month`
formula, function, or algorithm, (the dividend), by the cardinality
(12) of the ordered contiguous sequence of month chronometric units
of the year chronometric unit (the divisor).
[0221] The semantic interface unit may send, or otherwise make
available, the data-queries to the distributed in-memory database
of the low-latency data access and analysis system, such as the
distributed in-memory database 3300 shown in FIG. 3. The
distributed in-memory database may receive, or otherwise access,
the data-queries. The distributed in-memory database may execute or
perform one or more queries to generate or obtain response data
responsive to the data-query based on the low-latency data at
4420.
[0222] The distributed in-memory database may output, or otherwise
make available, the results data, or a portion thereof, such as to
the semantic interface unit. The semantic interface unit may
receive, or otherwise access, the results data, or a portion
thereof, and may output, or otherwise make available, the results
data, or a portion thereof, such as to the relational search unit.
The relational search unit may receive, or otherwise access, the
results data, or a portion thereof, and may output, or otherwise
make available, the results data, or a portion thereof, such as to
the system access interface unit. The system access interface unit
may receive, or otherwise access, the results data, or a portion
thereof.
[0223] The system access interface unit may output a response
including data representing the results data, or a portion thereof,
at 4500. The system access interface unit may generate data for
presenting a user interface, or one or more portions thereof,
representing the results data. For example, the system access
interface unit may generate instructions for rendering, or
otherwise presenting, the user interface, or one or more portions
thereof and may transmit, or otherwise make available, the
instructions for rendering, or otherwise presenting, the user
interface, or one or more portions thereof to the system access
unit, for presentation to a user of a system access unit. For
example, the results data may include an epoch value and outputting
the response may include outputting data representing the epoch
value in accordance with a chronometric data visualization for the
system-defined chronometry at 4510, such as in accordance with the
display attributes defined at 4110.
[0224] FIG. 5 is a flow diagram of a method of analytical data
access with domain-specific chronometry 5000 in a low-latency data
access and analysis system. Analytical data access with
domain-specific chronometry 5000 may be implemented in one or more
components of a low-latency data access and analysis system, such
as the low-latency data access and analysis system 3000 shown in
FIG. 3, such as in a distributed in-memory database, such as the
distributed in-memory database 3300 shown in FIG. 3, a relational
search unit, such as the relational search unit 3700 shown in FIG.
3, a natural language processing unit, such as the natural language
processing unit 3710 shown in FIG. 3, and a system access interface
unit, such as the system access interface unit 3900 shown in FIG.
3. Analytical data access with domain-specific chronometry 5000 may
be similar to the analytical data access with system-defined
chronometry 4000 shown in FIG. 4, except as described herein or
otherwise clear from context.
[0225] Analytical data access with domain-specific chronometry 5000
includes operating the low-latency data access and analysis system
(LLDAS) at 5100, receiving data expressing usage intent (DEUI) at
5200, generating a resolved-request at 5300, obtaining response
data at 5400, and outputting a response at 5500.
[0226] Operating the low-latency data access and analysis system at
5100 may include generating the low-latency data access and
analysis system. Generating the low-latency data access and
analysis system may include defining canonical chronometry
(system-defined chronometry) for the low-latency data access and
analysis system at 5110.
[0227] Although not shown separately in FIG. 5, generating the
low-latency data access and analysis system may include defining,
generating, operating, or a combination thereof, a finite state
machine (FSM) for the low-latency data access and analysis system,
such as the finite state machine implemented by the relational
search unit 3700 shown in FIG. 3. Generating the finite state
machine may omit including names or descriptors for chronometric
units for the system-defined chronometry, such as day-of-week name,
month-of-year name, quarter-of-year name, or year names as keywords
in the finite state machine. Although not shown separately in FIG.
5, generating the low-latency data access and analysis system at
5100 may include defining one or more chronometric phrase patterns.
For example, a chronometric phrase pattern may indicate a
domain-specific chronometry.
[0228] Defining the canonical chronometry for the low-latency data
access and analysis system at 5110 may be similar to defining
system-defined chronometry as shown at 4110 in FIG. 4, except as
described herein or otherwise clear from context. Defining the
canonical chronometry may include generating a canonical
chronometry dataset. Generating the canonical chronometry dataset
may include generating, populating, or both, a chronometry table
for the canonical chronometry, such as in the distributed in-memory
database of the low-latency data access and analysis system.
[0229] The chronometry table for the canonical chronometry includes
a date column for storing respective epoch values. For example, the
canonical chronometry dataset may describe an era corresponding to
a duration from a minimum temporal location to a maximum temporal
location, and the chronometry table may include an epoch values
corresponding the days from the minimum temporal location to the
maximum temporal location. For example, the canonical chronometry
may correspond with a Gregorian calendar, or a defined variant
thereof. An epoch value in the chronometry table for the canonical
chronometry may be unique within the chronometry table for the
canonical chronometry.
[0230] The chronometry table for the canonical chronometry may
include one or more chronometric data columns. A value of a
chronometric data column may indicate chronometric data describing
a respective corresponding epoch value. For example, the
chronometry table for the canonical chronometry may include a
`Month` column and a value of the `Month` column may indicate a
name of a `Month` chronometric unit corresponding to a respective
epoch value. The chronometric data columns may be identified based
on a defined set of chronometric data columns for the
system-defined chronometry. An example of the chronometric data
columns of the chronometry table for the canonical chronometry is
shown in Table 1. Other system-defined columns may be used.
TABLE-US-00001 TABLE 1 Column Name Datatype Date Date day_of_week
String month String monthly String quarter String quarterly String
year String day_number_of_week Int64 day_number_of_month Int64
day_number_of_quarter Int64 day_number_of_year Int64
week_number_of_month Int64 week_number_of_quarter Int64
week_number_of_year Int64 month_number_of_quarter Int64
month_number_of_year Int64 quarter_number_of_year Int64
absolute_week_number Int64 start_of_week_epoch Date
end_of_week_epoch Date absolute_month_number Int64
start_of_month_epoch Date end_of_month_epoch Date
absolute_quarter_number Int64 start_of_quarter_epoch Date
end_of_quarter_epoch Date absolute_year_number Int64
start_of_year_epoch Date end_of_year_epoch Date is_weekend
Boolean
[0231] Defining the canonical chronometry may include generating
one or more chronometric objects for the canonical chronometry. For
example, a chronometric object may be a chronometry table type
object and may represent the chronometry table for the canonical
chronometry. The chronometric objects may be included in a
distributed in-memory ontology of the low-latency data access and
analysis system, such as by a distributed in-memory ontology unit
of the low-latency data access and analysis system, such as the
distributed in-memory ontology unit 3500 shown in FIG. 3. One or
more edges may be included in the distributed in-memory ontology
representing relationships for the chronometric objects. For
example, an edge in the distributed in-memory ontology may
represent a relationship between a date column and the chronometric
table. In some implementations, the distributed in-memory ontology
may omit an edge representing a relationship between a date column
and a chronometric table, and the domain-specific chronometry
associated with the domain corresponding to the respective date
column, which may be a sub-domain of the system-domain, such as an
organization-domain, may be identified for the respective date
column.
[0232] Generating the low-latency data access and analysis system
at 5110 may include generating a token type representing
chronometry. For example, the relational search unit of the
low-latency data access and analysis system may generate data
representing a chronometry token type. The relational search unit
include the data representing a chronometry token type in one or
more indexes, such as in the keyword index of the low-latency data
access and analysis system. A unique identifier associated with the
chronometry token type may be included in a data structure for
interfacing with the relational search unit.
[0233] Although not shown separately in FIG. 5, generating the
low-latency data access and analysis system may include generating
one or more chronometry indices for indexing the canonical
chronometry, or a portion thereof. The relational search unit may
generate respective chronometry indices on a per-chronometric unit
basis for one or more of the chronometric units defined by the
canonical chronometry. For example, the relational search unit may
generate a respective chronometry index for defined day-of-week
descriptors or names for the instances of the day chronometric unit
with respect to the ordered contiguous sequence of day chronometric
units corresponding to, or within, an instance of a week
chronometric unit of the canonical chronometry (canonical
chronometry days index). In another example, the relational search
unit may generate a respective chronometry index for defined month
descriptors or names for the instances of the month chronometric
unit with respect to the ordered contiguous sequence of month
chronometric units corresponding to, or within, an instance of a
year chronometric unit of the canonical chronometry (canonical
chronometry months index). In another example, the relational
search unit may generate a respective chronometry index for defined
quarter descriptors or names for the instances of the quarter
chronometric unit with respect to the ordered contiguous sequence
of quarter chronometric units corresponding to, or within, an
instance of a year chronometric unit of the canonical chronometry
(canonical chronometry quarters index). In another example, the
relational search unit may generate a respective chronometry index
for defined year descriptors or names, such as 1999, 2000, or 2019,
for the instances of the year chronometric unit with respect to the
ordered contiguous sequence of year chronometric units
corresponding to, or within, the era defined by the canonical
chronometry (canonical chronometry years index). In some
implementations, the canonical chronometry days index, the
canonical chronometry months index, the canonical chronometry
quarters index, and the canonical chronometry years index may be
implemented as a combined canonical chronometry index.
[0234] The relational search unit may obtain a descriptor value for
a chronometric unit from the domain-specific chronometry dataset.
The chronometric unit may be associated with a chronometric unit
type. The relational search unit may generate a chronometry index
for indexing descriptor values for chronometric units from the
domain-specific chronometry dataset associated with the
chronometric unit type. The relational search unit may include the
chronometry index for indexing descriptor values for chronometric
units from the domain-specific chronometry dataset associated with
the chronometric unit type in the chronometry index.
[0235] Although not shown separately in FIG. 5, operating the
low-latency data access and analysis system at 5100, such as
generating the low-latency data access and analysis system, may
include defining one or more data-query chronometric operators. The
data-query chronometric operator, or data-query chronometric
operators, may be included as data-query chronometric operators in
the distributed in-memory database of the low-latency data access
and analysis system.
[0236] A data-query chronometric operator may obtain an indication
of a chronometry, such as the system-defined chronometry or a
domain-specific chronometry. The indication of the chronometry may
be a unique identifier for a chronometry table corresponding to the
chronometry. For example, a request indicating the data-query
chronometric operator may include data identifying the chronometry.
In some implementations, the data identifying the chronometry may
include a unique identifier for a chronometry table corresponding
to the chronometry and identification of a schema version. In some
implementations, the data identifying the chronometry may include
string data indicating a name or descriptor for the
chronometry.
[0237] A data-query chronometric operator may obtain an indication
of a chronometry column from the chronometry table, which may
correspond with a respective chronometric unit described in the
chronometry. The indication of the chronometry column from the
chronometry table may be a unique identifier for the chronometry
column from the chronometry table. For example, a request
indicating the data-query chronometric operator may include data
identifying the chronometry column.
[0238] A data-query chronometric operator may obtain an indication
of a `date` column of a table in the distributed in-memory
database. The indication of the `date` column of a table in the
distributed in-memory database may be a unique identifier for the
`date` column of a table in the distributed in-memory database. For
example, a request indicating the data-query chronometric operator
may include data identifying the `date` column of a table in the
distributed in-memory database.
[0239] Although not shown separately in FIG. 5, operating the
low-latency data access and analysis system at 5100, such as
generating the low-latency data access and analysis system, may
include defining one or more query-transformation chronometric
operators. The query-transformation chronometric operator, or
query-transformation chronometric operators, may be included as
query-transformation chronometric operators in a semantic interface
of the low-latency data access and analysis system, such as the
semantic interface unit 3600 shown in FIG. 3.
[0240] Although not shown separately in FIG. 5, generating the
low-latency data access and analysis system may include defining
one or more chronometric transformations for generating a
data-query, or data-queries, based on a resolved-request. For
example, the chronometric transformation, or chronometric
transformations, may be included in a semantic interface of the
low-latency data access and analysis system, such as the semantic
interface unit 3600 shown in FIG. 3.
[0241] The chronometric transformation, or chronometric
transformations, may generate a portion of a data-query
corresponding to a portion of a resolved-request that includes a
chronometric phrase. For example, a chronometric transformation may
identify a data-query chronometric operator corresponding to the
respective chronometric phrase and may include data indicating the
data-query chronometric operator in the data-query to express the
portion of a resolved-request that includes a chronometric phrase,
or a portion thereof. The chronometric transformation, or
chronometric transformations, may include generating an expression
tree, which may be distinct from the representation of the
resolved-request. The expression tree may include one or more
chronometric operators.
[0242] Although not shown separately in FIG. 5, generating the
low-latency data access and analysis system may include generating
and including an interface (or interfaces), such as an applications
programming interface, a command-line interface, or both, for
creating, accessing, modifying, deleting, or a combination thereof,
a domain-specific chronometry. For example, the semantic interface
unit may include an applications programming interface for
creating, accessing, modifying, deleting, or a combination thereof,
a domain-specific chronometry. One or more other components of the
low-latency data access and analysis system, such as an enterprise
data interface unit of the low-latency data access and analysis
system, such as the enterprise data interface unit 3400 shown in
FIG. 3, a system configuration unit of the low-latency data access
and analysis system, such as the system configuration unit 3820
shown in FIG. 3, a user customization unit of the low-latency data
access and analysis system, such as the user customization unit
3830 shown in FIG. 3, a system access interface unit of the
low-latency data access and analysis system, such as the system
access interface unit 3900 shown in FIG. 3, or a combination
thereof may include one or more interfaces for creating, accessing,
modifying, deleting, or a combination thereof, a domain-specific
chronometry.
[0243] Although not shown separately in FIG. 5, generating the
low-latency data access and analysis system may include generating
and including one or more components of the low-latency data access
and analysis system for managing a domain-specific chronometry,
such as a component of the low-latency data access and analysis
system for managing ontological data for domain-specific
chronometry (chronometry-manager) and a component of the
low-latency data access and analysis system for managing
chronometric data of the domain-specific chronometry
(chronometry-data-manager). One or more other components of the
low-latency data access and analysis system may access or interface
with the chronometry-manager, the chronometry-data-manager, or
both.
[0244] Operating the low-latency data access and analysis system at
5100 may include obtaining domain-specific chronometry at 5120. An
example of obtaining domain-specific chronometry is shown in FIG.
6. As described in FIG. 6, obtaining the domain-specific
chronometry at 5120 may include generating, or otherwise
identifying, one or more chronometric objects, such as a table
object, corresponding to the domain-specific chronometry,
generating, or otherwise identifying, one or more chronometry
indices corresponding to the domain-specific chronometry, modifying
the finite state machine in accordance with the domain-specific
chronometry, or a combination thereof. Although not shown
separately in FIG. 5, operating the low-latency data access and
analysis system at 5100 may include obtaining one or more
previously generated domain-specific chronometry datasets.
[0245] Although not shown separately in FIG. 5, operating the
low-latency data access and analysis system at 5100 may include
generating, storing, or both, data identifying a primary
chronometry for a respective domain. The primary chronometry may be
identified as the canonical chronometry or a domain-specific
chronometry.
[0246] Although not shown separately in FIG. 5, operating the
low-latency data access and analysis system at 5100 may include
generating, storing, or both, data associating a respective
temporal data object, such as an object representing a date column
in a table other than a chronometry table, with a respective
chronometry, such as the canonical chronometry or a domain-specific
chronometry. For example, the data associating the respective
temporal data object with a respective chronometry may be
identified in response to input, such as user input, indicating the
association. Data, such as a Boolean flag or bit, identifying a
data structure as representing chronometric data may be included in
a data structure for interfacing with the distributed in-memory
database.
[0247] Data expressing usage intent (DEUI) may be received at 5200.
A system access interface unit of the low-latency data access and
analysis system, such as the system access interface unit 3900
shown in FIG. 3, may receive data, such as user input data,
including the data expressing usage intent. The system access
interface unit may send, or otherwise make available, the data
expressing usage intent, or a portion thereof, to a relational
search unit of the low-latency data access and analysis system,
such as the relational search unit 3700 shown in FIG. 3. Receiving
the data expressing usage intent may be similar to receiving data
expressing usage intent as shown at 4200 in FIG. 4, except as
described herein or otherwise clear from context. For example, the
data expressing usage intent received at 5200 may include data
expressly indicating a chronometry, such as string data including
the name or descriptor of the chronometry.
[0248] A resolved-request may be obtained at 5300. Obtaining the
resolved-request may be similar to generating a resolved-request as
described with respect to the relational search unit 3700 shown in
FIG. 3, except as described herein or otherwise clear from context.
The relational search unit may receive, or otherwise access, the
data expressing usage intent. The relational search unit may
process, parse, identify semantics, tokenize, or a combination
thereof, the request for data to generate a resolved-request, which
may include identifying a database and visualization agnostic
ordered sequence of tokens based on the data expressing usage
intent. In some implementations, the data expressing usage intent
obtained at 5200 may indicate a previously generated
resolved-request and obtaining the resolved-request at 5300 may
include obtaining, such as by reading, or otherwise accessing,
previously stored data representing the previously generated
resolved-request.
[0249] Obtaining the resolved-request at 5300 may include
identifying one or more chronometric phrases at 5310. Identifying
the chronometric phrase, or phrases, at 5310 may be similar to
identifying one or more chronometric phrases as shown at 4310 in
FIG. 4, except as described herein or otherwise clear from context.
Identifying the chronometric phrases may include traversing the
finite state machine at 5312. Identifying the chronometric phrases
may include identifying one or more chronometric tokens by
traversing one or more indices, such as system-defined indices at
5314 (system indices), chronometry indices at 5316, or a
combination thereof. The chronometry indices at 5316 may include
one or more chronometry indices for the canonical chronometry, one
or more chronometry indices for the domain-specific chronometry, or
one or more chronometry indices for the canonical chronometry and
one or more chronometry indices for the domain-specific
chronometry.
[0250] For example (Example 1), the data expressing usage intent
may include the string "precipitation last year" (request string).
Obtaining the resolved-request at 5300 may include traversing the
finite state machine at 5312 to identify one or more indices for
identifying respective tokens corresponding to respective portions,
such as words, of the request string, such as a `precipitation`
token, a `last` token, and a `year` token. For example, the finite
state machine may identify the ontological index as an available
index for tokenizing the request string portion "precipitation",
the request string portion "precipitation" may be tokenized by
traversing the ontological index, and a `precipitation` column in a
table in the distributed in-memory database of the low-latency data
access and analysis system may be identified. The portion of the
resolved-request corresponding to the request string portion
"precipitation", the `precipitation` token, and the `precipitation`
column, may be identified as indicating an aggregation, such as a
summation, of the data from the `precipitation` column in the table
in the constituent data. The `precipitation` token may be
identified as a phrase (non-chronometric). Data indicating the
identified column and the identified aggregation may be included in
the resolved-request. The system-defined token corresponding to the
request string portion "last" may be identified by traversing the
system-defined keyword index. Traversing the finite state machine
may include identifying the request string portion "last" as
corresponding to a first part of a phrase, such as based on the
chronometric phrase patterns defined at 5100. Traversing the finite
state machine may include identifying the system-defined keyword
index as a candidate index for tokenizing the request string
portion "year". The system-defined token corresponding to the
request string portion "year" may be identified by traversing the
system-defined keyword index. The token sequence `last` followed by
`year` may be identified as a chronometric phrase, such as a phrase
defined for the system-defined chronometry at 5110, such as based
on the chronometric phrase patterns defined at 5100. A `date`
column may be identified from one or more candidate tables, such as
the table identified for the `precipitation` token, as
corresponding to the request string portion "year", or the `year`
token. Data indicating a chronometric filter, or condition, for the
resolved-request, corresponding to the chronometric phrase and the
respective `date` column, may be included in the resolved-request.
The chronometric filter, or condition, may be a chronometric filter
defined at 5110. Chronometric filters, or conditions, other than
the chronometric filter defined at 5110 may be unavailable.
[0251] Obtaining the resolved-request at 5300 may include
identifying one or more chronometric tokens. For example (Example
2), a domain-specific chronometry may be obtained at 5120. The
canonical chronometry may define a year chronometric unit and a
month chronometric unit. The canonical chronometry may define
respective descriptors for instances of the month chronometric unit
with respect to an ordered contiguous sequence of month
chronometric units corresponding to, or within, an instance of a
year chronometric unit of the canonical chronometry, as "January",
"February", "March", "April", "May", "June", "July", "August",
"September", "October", "November", and "December". The
domain-specific chronometry may define a `month` chronometric unit
and a `year` chronometric unit. Although described using the word
`month` and the word `year` for simplicity, the `month`
chronometric unit of the domain-specific chronometry and the `year`
chronometric unit of the domain-specific chronometry may differ
from the month chronometric unit of the canonical chronometry and
the year chronometric unit of the canonical chronometry
respectively. The domain-specific chronometry may define
descriptors for respective instances of the `month` chronometric
unit with respect to an ordered contiguous sequence of `month`
chronometric units corresponding to, or within, an instance of the
`year` chronometric unit of the domain-specific chronometry as
"M1", "M2", "M3", "M4", "M5", "M6", "M7", "M8", "M9", "M10", "M11",
and "M12". The data expressing usage intent may include the string
"precipitation M1" (request string). Obtaining the resolved-request
at 5300 with respect to the request string portion "precipitation"
in Example 2 may be similar to Example 1, and Example 2 may be
otherwise similar to Example 1, except as described herein or
otherwise clear from context. Traversing the finite state machine
may include identifying one or more candidate chronometry token
types. The candidate chronometry token types may include one or
more chronometry token types corresponding to respective
chronometric units defined in the chronometry data. Traversing the
finite state machine may include identifying one or more
chronometry indices as candidate indices for tokenizing the request
string portion "M1". The chronometry indices identified as
candidate indices may respectively correspond with the candidate
chronometry token types. For example, the candidate chronometry
token types may include a `month` chronometry token type and the
chronometry indices identified as candidate indices may include a
`month` chronometry index. One or more of the chronometry indices
for the canonical chronometry may be included in the candidate
indices. One or more of the chronometry indices for the
domain-specific chronometry may be included in the candidate
indices. A token corresponding to the request string portion "M1"
may be identified by traversing a `month` chronometry index for the
domain-specific chronometry. The token `M1` may be identified as a
chronometric phrase. A `date` column may be identified from one or
more candidate tables, such as the table identified for the
`precipitation` token, as corresponding to the request string
portion "M1", or the `M1` token. Data indicating a chronometric
filter, or condition, for the resolved-request, corresponding to
the chronometric phrase and the respective `date` column, may be
included in the resolved-request. The data indicating a
chronometric filter may include data identifying, such as a unique
identifier, the domain-specific chronometry.
[0252] Obtaining the resolved-request at 5300 may include
identifying a chronometry dataset for the resolved-request. For
example (Example 3), a domain-specific chronometry having the name
or descriptor "Meteorological" may be obtained at 5120. The data
expressing usage intent may include the string "precipitation last
year Meteorological" (request string). Obtaining the
resolved-request at 5300 with respect to the request string
portions "precipitation", "last", and "year" in Example 3 may be
similar to Example 1, and Example 3 may be otherwise similar to
Example 1, except as described herein or otherwise clear from
context. Traversing the finite state machine may include
identifying an index of chronometry, which may include the names or
descriptors of respective domain-specific chronometry datasets, for
tokenizing the request string portion "Meteorological". The
domain-specific chronometry having the name or descriptor
"Meteorological" may be identified as the chronometry for the
resolved-request. Data indicating a chronometric filter, or
condition, for the resolved-request, corresponding to the
chronometric phrase, the respective `date` column, and the
domain-specific chronometry, may be included in the
resolved-request.
[0253] The resolved-request may include data indicating, such as
identifying, one or more objects in the low-latency data access and
analysis system, such as one or more columns, one or more tables,
one or more join paths for joining respective tables, one or more
filters, or a combination thereof.
[0254] Although not shown separately in FIG. 5, obtaining the
resolved-request at 5300 may include ambiguity resolution, such as
token ambiguity resolution, phrase ambiguity resolution, request
ambiguity resolution, or a combination thereof. Ambiguity
resolution may include identifying a candidate ambiguity resolution
token, or candidate ambiguity resolution tokens, for the data
expressing usage intent. In some implementations, a candidate
ambiguity resolution token having a maximal probabilistic utility
may be identified as a resolved token of the resolved-request. Data
representing a candidate ambiguity resolution token, or candidate
ambiguity resolution tokens, may be included in the response output
at 5500.
[0255] Token ambiguity resolution, which may be referred to as
automatic token completion or automatic token suggestion, may
include identifying one or more candidate token resolution tokens.
A candidate token resolution token may be a token identified as
corresponding to a portion of a request string for the data
expressing usage intent, wherein the value, such as string value,
of the candidate token resolution token differs from the respective
portion of the request string. The data expressing usage intent
omits data corresponding to the candidate token resolution token,
or tokens, such as data expressly indicating the candidate token
resolution token. The relational search unit may identify a
candidate token resolution token, or tokens, in response to
identifying a request string portion from the data expressing usage
intent as ambiguous. Token ambiguity resolution may include
traversing the finite state machine to identify one or more
candidate token resolution tokens. The candidate token resolution
tokens may include chronometric tokens, corresponding to
chronometric objects represented in the low-latency data access and
analysis system. The candidate token resolution tokens may be
identified, sorted, ordered, ranked, weighted, or filtered based on
probabilistic utility. Generating the resolved-request may include
determining that the request string, or the data expressing usage
intent, omits request string data corresponding to a portion of a
candidate token resolution token. Token ambiguity resolution may be
chronometry dataset specific. Obtaining the resolved-request at
5100 may include obtaining a probable, such as identified based on
probabilistic utility, chronometric object, such as corresponding
to a candidate token resolution token, by traversing the finite
state machine. The finite state machine may represent an identified
chronometry dataset. Data representing a defined cardinality of the
candidate token resolution tokens, such as five, which may be in
probabilistic utility order, may be included in the response data
output at 5500. Data representing probable chronometric object may
be included in the response data output at 5500.
[0256] An example (Example 4), of token ambiguity resolution may be
similar to Example 3, except as described herein or otherwise clear
from context. A domain-specific chronometry having the name or
descriptor "Meteorological" may be obtained at 5120. The
domain-specific chronometry may define descriptors for respective
instances of the `month` chronometric unit with respect to an
ordered contiguous sequence of `month` chronometric units
corresponding to, or within, an instance of the `year` chronometric
unit of the domain-specific chronometry as "M1", "M2", "M3", "M4",
"M5", "M6", "M7", "M8", "M9", "M10", "M11", and "M12". The data
expressing usage intent may include the string "precipitation M"
(request string). Request string data subsequent to "precipitation
M" may be omitted from the data expressing usage intent. Obtaining
the resolved-request at 5300 with respect to the request string
portion ""precipitation" in Example 4 may be similar to Example 3.
Ontological object data corresponding to a "precipitation" token
may indicate that a "precipitation" column corresponding to the
"precipitation" token is associated with the domain-specific
chronometry having the name or descriptor "Meteorological", which
may be identified as a chronometry for the resolved-request. The
request string portion "M" may be identified as ambiguous. A
keyword token corresponding to the `month` chronometric unit may be
identified as a candidate token resolution token corresponding to
the request string portion "M". Respective tokens corresponding to
the respective `month` chronometric units from the ordered
contiguous sequence of `month` chronometric units defined by the
"Meteorological" domain-specific chronometry may be identified as
candidate token resolution tokens. Generating the resolved-request
may omit identifying tokens from chronometry datasets other than
the chronometry dataset, or datasets, identified for the
resolved-request. For example, the canonical chronometry may define
respective descriptors for instances of the month chronometric
unit, which may include "March" and "May". The resolved-request may
omit a token associated with the canonical chronometry, and token
ambiguity resolution for the request string portion "M" may omit
identifying "March" and "May".
[0257] Phrase ambiguity resolution, or automatic phrase completion,
may include identifying one or more candidate phrase completion
tokens. A candidate phrase completion token may be a token other
than previously resolved tokens for the resolved-request, the
tokens identified as corresponding to a portion of the request
string, or both. The data expressing usage intent omits data
corresponding to the candidate phrase completion tokens, such as
data expressly indicating the candidate phrase completion tokens.
The relational search unit may identify a candidate phrase
completion token, or tokens, in response to identifying a token,
corresponding to a request string portion from the data expressing
usage intent as corresponding to a phrase part, wherein the phrase
pattern corresponding to the phrase includes a subsequent phrase
part, or phrase parts, and determining that the request string
omits request string data, subsequent to the request string portion
corresponding to the phrase part, corresponding to the subsequent
phrase part, or phrase parts. Phrase ambiguity resolution may
include traversing the finite state machine to identify one or more
candidate phrase completion tokens. The candidate phrase completion
tokens may include chronometric tokens, corresponding to
chronometric objects represented in the low-latency data access and
analysis system. The candidate phrase completion tokens may be
identified, sorted, ordered, ranked, weighted, or filtered based on
probabilistic utility. Generating the resolved-request may include
determining that the request string, or the data expressing usage
intent, omits request string data corresponding to a subsequent
phrase part, or respective subsequent phrase parts, indicated by
the candidate phrase pattern, or patterns, or that omits request
string data corresponding to a subsequent phrase part, or
respective subsequent phrase parts, indicated by the candidate
phrase pattern, or patterns is otherwise unavailable. Obtaining the
resolved-request at 5100 may include obtaining a probable, such as
identified based on probabilistic utility, chronometric object,
such as corresponding to a candidate phrase completion token, by
traversing the finite state machine. Data representing a defined
cardinality of the candidate phrase completion tokens, such as
five, which may be in probabilistic utility order, may be included
in the response data output at 5500. Data representing probable
chronometric object may be included in the response data output at
5500. Phrase ambiguity resolution may be chronometry dataset
agnostic.
[0258] For example, the data expressing usage intent may include
the string "precipitation last" (request string). Request string
data subsequent to "precipitation last" may be omitted from the
data expressing usage intent. Traversing the finite state machine
may include identifying the request string portion "last" as
corresponding to a first part of one or more candidate phrases,
such as based on the chronometric phrase patterns defined at 5100.
For example, a chronometric phrase pattern may indicate a sequence
of a chronometric keyword token, such as "last", followed by a
chronometric unit token type. Generating the resolved-request may
include identifying one or more tokens in accordance with the
subsequent phrase part, or respective subsequent phrase parts,
indicated by the candidate phrase pattern, or patterns as candidate
phrase completion tokens. For example, the finite state machine may
include a candidate transition, or respective candidate
transitions, from a state corresponding to the request string
portion "last" to the token, or respective tokens, and the
candidate phrase completion tokens may be identified in accordance
with the identified candidate transitions. Generating the
resolved-request may include identifying a chronometric token, or
tokens, corresponding to the chronometric unit token type as a
candidate phrase completion token, or respective a candidate phrase
completion tokens, such as an "hour" token, a "day" token, a
"month" token, a "quarter" token, or a "year" token. The
probabilistic utility for the "year" token may be identified as the
maximal probity candidate phrase completion token and the request
may be resolved as corresponding to a request for data indicating
precipitation for last year.
[0259] Request ambiguity resolution may include identifying one or
more candidate resolved-requests. A candidate resolved-requests may
be a previously generated, or previously resolved,
resolved-request. The data expressing usage intent omits data
corresponding to at least a portion of the candidate
resolved-request, such as data expressly indicating the candidate
resolved-request. The relational search unit may identify a
candidate resolved-request, or candidate resolved-requests, in
response to identifying a token, or a sequence of tokens,
corresponding to a request string portion, or respective request
string portions in sequence, from the data expressing usage intent,
as corresponding to a portion of a candidate resolved-request
(referred to herein as a resolved portion of the resolved-request),
wherein the candidate resolved-request includes a subsequent
portion, and determining that the request string omits request
string data, subsequent to the request string portion corresponding
to the resolved portion of the resolved-request, corresponding to
the subsequent portion of the candidate resolved request. Request
ambiguity resolution may include traversing the finite state
machine to identify one or more candidate resolved-requests. The
resolved-requests may be identified, sorted, ordered, ranked,
weighted, or filtered based on probabilistic utility. Data
representing a defined cardinality of the candidate
resolved-requests, such as five, which may be in probabilistic
utility order, may be included in the response data output at 5500.
Request ambiguity resolution may be chronometry dataset agnostic. A
candidate resolved-request may be chronometry dataset specific.
[0260] Results data may be obtained at 5400. The relational search
unit may send, or otherwise make available, the resolved-request to
a semantic interface of the low-latency data access and analysis
system, such as the semantic interface unit 3600 shown in FIG. 3.
The semantic interface may receive, or otherwise access, the
resolved-request. Obtaining the results data at 5400 may be similar
to obtaining results data at 4400 as shown in FIG. 4, except as
described herein or otherwise clear from context.
[0261] An answer object is obtained at 5410. For example, the
semantic interface unit may process or transform the received
resolved-request to obtain the answer object. In some
implementations, the resolved-request, or the data expressing usage
intent obtained at 5200, may identify a previously generated answer
object and obtaining the answer object may include obtaining data
representing the answer object, such as from the distributed
in-memory ontology unit. In some implementations, obtaining the
answer object may include generating the answer object, such as
based on the resolved-request.
[0262] The answer object may include a definition or description of
the ontological structure of response data for responding to the
resolved-request. The ontological structure of the results data
responsive to the resolved-request may include one or more
ontological elements, which may describe respective portions of the
results data responsive to the resolved-request. The ontological
structure of the response data may differ from the structure or
organization of a visualization output responsive to the
resolved-request. The answer object may be a data structure, which
may be hierarchical. A respective portion, or node, of the answer
object may include a definition or description of an ontological
element of the answer object which may represent a distinct
structural portion of the results data responsive to the
resolved-request. For example, the results data may be organized as
a tabular data structure and an ontological element of the answer
object may correspond with a respective column of the results
data.
[0263] The ontological structure of the results data responsive to
the resolved-request may include one or more display-data
ontological elements for including data that may be included for
display in a visualization.
[0264] The ontological structure of the results data responsive to
the resolved-request may include one or more control-data (hidden
or non-display) ontological elements, such as a minimal temporal
location (min) for a temporal interval corresponding to a
respective chronometric ontological element, which may be expressed
as an epoch value, a maximal temporal location (max) for the
temporal interval corresponding to the respective chronometric
ontological element, which may be expressed as an epoch value, or
both. In some implementations, the maximal temporal location may be
the minimal temporal location immediately temporally subsequent to
the temporal interval such that a temporal location (X) in the
temporal interval may be expressed as min<=X<max.
[0265] The data for the control-data ontological elements may
include respective deterministic, or contextually-unique,
identifiers to indicate respective associations between the
control-data ontological elements and chronometric display-data
ontological elements. The deterministic identifier for a
control-data ontological element may be generated based on the
identifier for the corresponding chronometric display-data
ontological element.
[0266] For example, the resolved-request may represent the request
string "precipitation last year", which may be a request for
precipitation values aggregated for a `year` chronometric unit
immediately preceding a current `year` chronometric unit. The
resolved-request may include data representing a measure token,
such as the `precipitation` token, which may include a unique
identifier for a data source, such as a column, such as the
`precipitation` column, in the distributed in-memory database, and
data indicating an aggregation, such as a summation, for the
measure token. The resolved-request may include data representing a
chronometric filter, such as a `LAST YEAR` filter, which may
include a unique identifier for a data source, such as a date
column, corresponding to the `last year` phrase from the request
string (date source identifier). The ontological structure of the
results data responsive to the resolved-request may include one or
more display-data ontological elements, such as a `precipitation`
results ontological element for including aggregated precipitation
values.
[0267] In another example, the resolved-request may represent the
request string "precipitation yearly meteorological", which may be
a request for precipitation values aggregated on a yearly, or
per-year, basis with respect to a meteorological domain-specific
chronometry. The resolved-request may include data representing a
measure token, such as the `precipitation` token, which may include
a unique identifier for a data source, such as a column, such as
the `precipitation` column, in the distributed in-memory database,
and data indicating an aggregation, such as a summation, for the
measure token. The resolved-request may include data representing a
key-word token, such as the `yearly` token, which may include a
unique identifier for a data source, such as a date column,
corresponding to the `yearly` token. The resolved-request may
include data representing the meteorological domain-specific
chronometry. The ontological structure of the results data
responsive to the resolved-request may include one or more
display-data ontological elements, such as a `precipitation`
results ontological element for including aggregated precipitation
values, and a `yearly` results chronometric ontological element for
including data representing respective descriptors for year-wise
date values. The ontological structure of the results data
responsive to the resolved-request may include one or more
control-data ontological elements, such as a minimal temporal
location (min) for a temporal interval corresponding to the
`yearly` results ontological element and a maximal temporal
location (max) for the temporal interval corresponding to the
`yearly` results ontological element.
[0268] The answer object may include a definition or description of
one or more visualizations for responding to the resolved-request.
For example, the definition or description of the visualization, or
visualizations, may include axis labels, visualization type data,
color data, presentation format data, or other visualization
description data.
[0269] The answer object may include a definition or description of
one or more headline data elements for responding to the
resolved-request. The headline data elements may include data
describing the response data, such as a cardinality of the set of
response data.
[0270] Generating the answer object may include storing the answer
object, such as by sending, transmitting, or otherwise making
available, the data representing the answer object to the
distributed in-memory ontology unit.
[0271] The semantic interface unit may generate one or more
corresponding data-queries at 5420, that are compatible with a
distributed in-memory database of the low-latency data access and
analysis system, such as the distributed in-memory database 3300.
For example, the relational search unit may generate the
data-query, or data-queries based on the answer object obtained at
5410. Generating a data-query, or data-queries, at 5420 may be
similar to generating one or more data-queries at 4410 as shown in
FIG. 4, except as described herein or otherwise clear from
context.
[0272] Generating a data-query, or data-queries, at 5420 may
include generating a proto-query based on the resolved-request. A
proto-query, which may be database agnostic, may be structured or
formatted in a form, language, or protocol that differs from the
defined structured query language of the distributed in-memory
database 3300.
[0273] Generating the proto-query may include generating one or
more proto-query expressions including one or more
query-transformation chronometric operators corresponding to
respective portions of the answer object. For example, the
definition or description of an ontological element of the answer
object may include data for generating a proto-query, or a portion
thereof, to obtain results data for the respective ontological
element of the answer object. Generating the proto-query may
include generating an expression for obtaining results data for the
ontological element of the answer object, or a portion thereof. For
example, the ontological element of the answer object may include
data describing a filter for the results data and generating the
proto-query may include generating an expression for filtering
results data in accordance with the data describing the filter for
the results data from the answer object.
[0274] The expressions may include expressions for `last period`
type filters, which may indicate filtering the results to omit data
other than data corresponding a temporal interval immediately
preceding a current temporal interval.
[0275] For example, the answer object may include data describing a
`last year` filter. The semantic interface unit may identify the
`last year` filter and may generate a corresponding `last year`
expression including the query-transformation chronometric
operator, which may express the `last year` filter wherein a
representative value, such as a `year` ordinal value, of the
chronometric `date` column may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `absolute_year_number` as shown in Table 1, the
chronometric filter criteria may be expressed as the difference
between a representative value, such as a `year` ordinal value, of
the current date, which may be expressed using the
query-transformation chronometric operator with reference to the
current date and the chronometric column identifier, and the
constant one (1), such that data corresponding to a respective
value of the chronometric `date` column is included in the results
data if the representative value of the chronometric `date` column
is equivalent to the difference.
[0276] In another example, the answer object may include data
describing a `last quarter` filter. The semantic interface unit may
identify the `last quarter` filter and may generate a corresponding
`last quarter` expression including the query-transformation
chronometric operator, which may express the `last quarter` filter
wherein a representative value, such as a `quarter` ordinal value,
of the chronometric `date` column may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `absolute_quarter_number` as shown in Table 1, the
chronometric filter criteria may be expressed as the difference
between the representative value, such as a `year` ordinal value,
of the current date, which may be expressed using the
query-transformation chronometric operator with reference to the
current date and the chronometric column identifier, and the
constant one (1), such that data corresponding to a respective
value of the chronometric `date` column is included in the results
data if the representative value of the chronometric `date` column
is equivalent to the difference.
[0277] In another example, the answer object may include data
describing a `last month` filter. The semantic interface unit may
identify the `last month` filter and may generate a corresponding
`last month` expression including the query-transformation
chronometric operator, which may express the `last month` filter
wherein a representative value, such as a `month` ordinal value, of
the chronometric `date` column may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `absolute_month_number` as shown in Table 1, the
chronometric filter criteria may be expressed as the difference
between the representative value, such as a `year` ordinal value,
of the current date, which may be expressed using the
query-transformation chronometric operator with reference to the
current date and the chronometric column identifier, and the
constant one (1), such that data corresponding to a respective
value of the chronometric `date` column is included in the results
data if the representative value of the chronometric `date` column
is equivalent to the difference.
[0278] In another example, the semantic interface unit may identify
the `last week` filter and may generate a corresponding `last week`
expression including the query-transformation chronometric
operator, which may express the `last week` filter wherein a
representative value, such as a `week` ordinal value, of the
chronometric `date` column may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `absolute_week_number` as shown in Table 1, the
chronometric filter criteria may be expressed as the difference
between the representative value, such as a `year` ordinal value,
of the current date, which may be expressed using the
query-transformation chronometric operator with reference to the
current date and the chronometric column identifier, and the
constant one (1), such that data corresponding to a respective
value of the chronometric `date` column is included in the results
data if the value of the representative chronometric `date` column
is equivalent to the difference.
[0279] The expressions may include expressions for `last_N_period`
type filters, which may indicate filtering the results to omit data
other than data corresponding a specified cardinality (N) of
temporal intervals immediately preceding a current temporal
interval.
[0280] For example, the answer object may include data describing a
`last 3 years` filter. The semantic interface unit may identify the
`last 3 years` filter and may generate a corresponding `last N
years` expression including the query-transformation chronometric
operator, which may express the `last 3 years` filter wherein a
representative value, such as a `year` ordinal value, of the
chronometric `date` column may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `absolute_year_number` as shown in Table 1, such that data
corresponding to a respective value of the chronometric `date`
column is included in the results data if a difference between a
representative value, such as a `year` ordinal value, of the
current date, which may be expressed using the query-transformation
chronometric operator with reference to the current date and the
chronometric column identifier, and the specified cardinality (3),
is at within, such as less than or equal to, the representative
value of the chronometric `date` column and the representative
value of the chronometric `date` column is less than the
representative value of the current date.
[0281] In another example, the answer object may include data
describing a `last 3 quarters` filter. The semantic interface unit
may identify the `last 3 quarters` filter and may generate a
corresponding `last 3 quarters` expression including the
query-transformation chronometric operator, which may express the
`last 3 quarters` filter wherein a representative value, such as a
`quarter` ordinal value, of the chronometric `date` column may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `absolute_quarter_number` as shown in
Table 1, such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if a
difference between a representative value, such as a `quarter`
ordinal value, of the current date, which may be expressed using
the query-transformation chronometric operator with reference to
the current date and the chronometric column identifier, and the
specified cardinality (3), is at within, such as less than or equal
to, the representative value of the chronometric `date` column and
the representative value of the chronometric `date` column is less
than the representative value of the current date.
[0282] In another example, the answer object may include data
describing a `last 3 months` filter. The semantic interface unit
may identify the `last 3 months` filter and may generate a
corresponding `last 3 months` expression including the
query-transformation chronometric operator, which may express the
`last 3 months` filter wherein a representative value, such as a
`month` ordinal value, of the chronometric `date` column may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `absolute_month_number` as shown in
Table 1, such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if a
difference between a representative value, such as a `month`
ordinal value, of the current date, which may be expressed using
the query-transformation chronometric operator with reference to
the current date and the chronometric column identifier, and the
specified cardinality (3), is at within, such as less than or equal
to, the representative value of the chronometric `date` column and
the representative value of the chronometric `date` column is less
than the representative value of the current date.
[0283] In another example, the answer object may include data
describing a `last 3 weeks` filter. The semantic interface unit may
identify the `last 3 weeks` filter and may generate a corresponding
`last 3 weeks` expression including the query-transformation
chronometric operator, which may express the `last 3 weeks` filter
wherein a representative value, such as a `week` ordinal value, of
the chronometric `date` column may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `absolute_week_number` as shown in Table 1, such that data
corresponding to a respective value of the chronometric `date`
column is included in the results data if a difference between a
representative value, such as a `week` ordinal value, of the
current date, which may be expressed using the query-transformation
chronometric operator with reference to the current date and the
chronometric column identifier, and the specified cardinality (3),
is at within, such as less than or equal to, the representative
value of the chronometric `date` column and the representative
value of the chronometric `date` column is less than the
representative value of the current date.
[0284] The expressions may include expressions for `period_to_date`
type filters, which may indicate filtering the results to omit data
other than data corresponding a current temporal interval.
[0285] For example, the answer object may include data describing a
`year_to_date` filter. The semantic interface unit may identify the
`year_to_date` filter and may generate a corresponding
`year_to_date` expression including the query-transformation
chronometric operator, which may express the `year_to_date` filter
wherein a representative value, such as a `year` ordinal value, of
the chronometric `date` column may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `absolute_year_number` as shown in Table 1, such that data
corresponding to a respective value of the chronometric `date`
column is included in the results data if the respective value of
the chronometric `date` column is within, such as less than or
equal to, the current date, and the representative value of the
chronometric `date` column is equivalent to the representative
value, such as a `year` ordinal value, of the current date, which
may be expressed using the query-transformation chronometric
operator with reference to the current date and the chronometric
column identifier.
[0286] In another example, the answer object may include data
describing a `quarter_to_date` filter. The semantic interface unit
may identify the `quarter_to_date` filter and may generate a
corresponding `quarter_to_date` expression including the
query-transformation chronometric operator, which may express the
`quarter_to_date` filter wherein a representative value, such as a
`quarter` ordinal value, of the chronometric `date` column may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `absolute_quarter_number` as shown in
Table 1, such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if the
respective value of the chronometric `date` column is within, such
as less than or equal to, the current date, and the representative
value of the chronometric `date` column is equivalent to the
representative value, such as a `quarter` ordinal value, of the
current date, which may be expressed using the query-transformation
chronometric operator with reference to the current date and the
chronometric column identifier.
[0287] In another example, the answer object may include data
describing a `month_to_date` filter. The semantic interface unit
may identify the `month_to_date` filter and may generate a
corresponding `month_to_date` expression including the
query-transformation chronometric operator, which may express the
`month_to_date` filter wherein a representative value, such as a
`month` ordinal value, of the chronometric `date` column may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `absolute_month_number` as shown in
Table 1, such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if the
respective value of the chronometric `date` column is within, such
as less than or equal to, the current date, and the representative
value of the chronometric `date` column is equivalent to the
representative value, such as a `month` ordinal value, of the
current date, which may be expressed using the query-transformation
chronometric operator with reference to the current date and the
chronometric column identifier.
[0288] In another example, the answer object may include data
describing a `week_to_date` filter. The semantic interface unit may
identify the `week_to_date` filter and may generate a corresponding
`week_to_date` expression including the query-transformation
chronometric operator, which may express the `week_to_date` filter
wherein a representative value, such as a `week` ordinal value, of
the chronometric `date` column may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `absolute_week_number` as shown in Table 1, such that data
corresponding to a respective value of the chronometric `date`
column is included in the results data if the respective value of
the chronometric `date` column is within, such as less than or
equal to, the current date, and the representative value of the
chronometric `date` column is equivalent to the representative
value, such as a `week` ordinal value, of the current date, which
may be expressed using the query-transformation chronometric
operator with reference to the current date and the chronometric
column identifier.
[0289] The expressions may include expressions for a `specified
year` type filter, which may indicate filtering the results to omit
data other than data corresponding a temporal interval
corresponding to a specified `year` chronometric unit, wherein the
value of the specified `year` chronometric unit is expressed as an
ordinal `year` value. For example, the answer object may include
data describing a `specified year` filter. The semantic interface
unit may identify the `specified year` filter and may generate a
corresponding `specified year` expression including the
query-transformation chronometric operator, which may express the
`specified year` filter wherein a representative value, such as a
`year` ordinal value, of the chronometric `date` column may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `absolute_year_number` as shown in Table
1, such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if the
respective value of the chronometric `date` column is equivalent to
the specified a `year` ordinal value.
[0290] The expressions may include expressions for a `specified
quarter` type filter, which may indicate filtering the results to
omit data other than data corresponding a temporal interval
corresponding to a specified `quarter` chronometric unit with
respect to a specified `year` chronometric unit, wherein the
specified `quarter` chronometric unit is expressed as a descriptor
of the `quarter` chronometric unit and the specified `year`
chronometric unit is expressed as a descriptor of the `year`
chronometric unit. For example, the answer object may include data
describing a `specified quarter` filter, the descriptor of the
`quarter` chronometric unit, and the descriptor of the `year`
chronometric unit. The semantic interface unit may identify the
`specified quarter` filter and may generate a corresponding
`specified quarter` expression including the query-transformation
chronometric operator, which may express the `specified quarter`
filter such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if the
descriptor corresponding to the `quarter` chronometric unit for the
respective value of the chronometric `date` column, which may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `quarter` as shown in Table 1, is
equivalent to the specified descriptor of the `quarter`
chronometric unit, and the descriptor corresponding to the `year`
chronometric unit for the respective value of the chronometric
`date` column, which may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `year` as shown in Table 1, is equivalent to the specified
descriptor of the `year` chronometric unit.
[0291] The expressions may include expressions for a `specified
month` type filter, which may indicate filtering the results to
omit data other than data corresponding a temporal interval
corresponding to a specified `month` chronometric unit, wherein the
specified `month` chronometric unit is expressed as a descriptor of
the `month` chronometric unit. For example, the answer object may
include data describing a `specified month` filter. The semantic
interface unit may identify the `specified month` filter and may
generate a corresponding `specified month` expression including the
query-transformation chronometric operator, which may express the
`specified month` filter such that data corresponding to a
respective value of the chronometric `date` column is included in
the results data if the descriptor corresponding to the `month`
chronometric unit for the respective value of the chronometric
`date` column, which may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `month` as shown in Table 1, is equivalent to the specified
descriptor of the `month` chronometric unit.
[0292] The expressions may include expressions for a `weekday` or
`weekend` type filter, which may indicate filtering the results to
omit data other than data corresponding chronometric units having
the `weekday` or `weekend` property respectively.
[0293] For example, the answer object may include data describing a
`weekday` type filter. The semantic interface unit may identify the
`weekday` type filter and may generate a corresponding `weekday`
expression including the query-transformation chronometric
operator, which may express the `weekday` type filter such that
data corresponding to a respective value of the chronometric `date`
column is included in the results data if the data corresponding to
the `day` chronometric unit for the respective value of the
chronometric `date` column, which may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `is_weekend` as shown in Table 1, is equivalent to the
value, such as zero (0), indicating that the respective
chronometric unit has the `weekday` property. Values of the
chronometric `date` column for which the corresponding value with
respect to the chronometric column identifier `is_weekend` is
equivalent to the value, such as one (1), indicating that the
respective chronometric unit has the `weekend` property may be
omitted or excluded.
[0294] In another example, the answer object may include data
describing a `weekend` type filter. The semantic interface unit may
identify the `weekend` type filter and may generate a corresponding
`weekend` expression including the query-transformation
chronometric operator, which may express the `weekend` type filter
such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if the
data corresponding to the `day` chronometric unit for the
respective value of the chronometric `date` column, which may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `is_weekend` as shown in Table 1, is
equivalent to the value, such as one (1), indicating that the
respective chronometric unit has the `weekend` property. Values of
the chronometric `date` column for which the corresponding value
with respect to the chronometric column identifier `is_weekend` is
equivalent to the value, such as zero (0), indicating that the
respective chronometric unit has the `weekday` property may be
omitted or excluded.
[0295] The expressions may include expressions for a `month of
year` type filter, which may indicate filtering the results to omit
data other than data corresponding a temporal interval
corresponding to a specified `month` chronometric unit with respect
to a specified `year` chronometric unit, wherein the specified
`month` chronometric unit is expressed as a descriptor of the
`month` chronometric unit and the specified `year` chronometric
unit is expressed as a descriptor of the `year` chronometric unit.
For example, the answer object may include data describing a
`specified month` filter, the descriptor of the `month`
chronometric unit, and the descriptor of the `year` chronometric
unit. The semantic interface unit may identify the `specified
month` filter and may generate a corresponding `specified month`
expression including the query-transformation chronometric
operator, which may express the `specified month` filter such that
data corresponding to a respective value of the chronometric `date`
column is included in the results data if the descriptor
corresponding to the `month` chronometric unit for the respective
value of the chronometric `date` column, which may be expressed
using the query-transformation chronometric operator with reference
to the chronometric `date` column and a chronometric column
identifier, such as `month` as shown in Table 1, is equivalent to
the specified descriptor of the `month` chronometric unit, and the
descriptor corresponding to the `year` chronometric unit for the
respective value of the chronometric `date` column, which may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `year` as shown in Table 1, is
equivalent to the specified descriptor of the `year` chronometric
unit.
[0296] The expressions may include expressions for
`N_periods_prior` type filters, which may indicate filtering the
results to omit data other than data corresponding a temporal
interval preceding the current temporal interval by a specified
cardinality (N) of temporal intervals.
[0297] For example, the answer object may include data describing a
`3_years_prior` filter. The semantic interface unit may identify
the `3_years_prior` filter and may generate a corresponding
`3_years_prior` expression including the query-transformation
chronometric operator, which may express the `3_years_prior` filter
wherein a representative value, such as a `year` ordinal value, of
the chronometric `date` column may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `absolute_year_number` as shown in Table 1, such that data
corresponding to a respective value of the chronometric `date`
column is included in the results data if a difference between a
representative value, such as a `year` ordinal value, of the
current date, which may be expressed using the query-transformation
chronometric operator with reference to the current date and the
chronometric column identifier, and the specified cardinality (3),
is at within, such as less than or equal to, the representative
value of the chronometric `date` column and the specified
cardinality (3) of temporal intervals is equivalent to the
representative value of the chronometric `date` column.
[0298] In another example, the answer object may include data
describing a `3 quarters_prior` filter. The semantic interface unit
may identify the `3 quarters_prior` filter and may generate a
corresponding `3 quarters_prior` expression including the
query-transformation chronometric operator, which may express the
`3 quarters_prior` filter wherein a representative value, such as a
`quarter` ordinal value, of the chronometric `date` column may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `absolute_quarter_number` as shown in
Table 1, such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if a
difference between a representative value, such as a `quarter`
ordinal value, of the current date, which may be expressed using
the query-transformation chronometric operator with reference to
the current date and the chronometric column identifier, and the
specified cardinality (3), is at within, such as less than or equal
to, the representative value of the chronometric `date` column and
the specified cardinality (3) of temporal intervals is equivalent
to the representative value of the chronometric `date` column.
[0299] In another example, the answer object may include data
describing a `3_months_prior` filter. The semantic interface unit
may identify the `3_months_prior` filter and may generate a
corresponding `3_months_prior` expression including the
query-transformation chronometric operator, which may express the
`3 months_prior` filter wherein a representative value, such as a
`month` ordinal value, of the chronometric `date` column may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `absolute_month_number` as shown in
Table 1, such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if a
difference between a representative value, such as a `month`
ordinal value, of the current date, which may be expressed using
the query-transformation chronometric operator with reference to
the current date and the chronometric column identifier, and the
specified cardinality (3), is at within, such as less than or equal
to, the representative value of the chronometric `date` column and
the specified cardinality (3) of temporal intervals is equivalent
to the representative value of the chronometric `date` column.
[0300] The expressions may include expressions for `current_period`
type filters, which may indicate filtering the results to omit data
other than data corresponding a current temporal interval.
[0301] For example, the answer object may include data describing a
`current_year` filter. The semantic interface unit may identify the
`current_year` filter and may generate a corresponding
`current_year` expression including the query-transformation
chronometric operator, which may express the `current_year` filter
wherein a representative value, such as a `year` ordinal value, of
the chronometric `date` column may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `absolute_year_number` as shown in Table 1, such that data
corresponding to a respective value of the chronometric `date`
column is included in the results data if a representative value,
such as a `year` ordinal value, of the current date, which may be
expressed using the query-transformation chronometric operator with
reference to the current date and the chronometric column
identifier, is equivalent to the representative value of the
chronometric `date` column.
[0302] In another example, the answer object may include data
describing a `current quarter` filter. The semantic interface unit
may identify the `current quarter` filter and may generate a
corresponding `current quarter` expression including the
query-transformation chronometric operator, which may express the
`current quarter` filter wherein a representative value, such as a
`quarter` ordinal value, of the chronometric `date` column may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `absolute_quarter_number` as shown in
Table 1, such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if a
representative value, such as a `quarter` ordinal value, of the
current date, which may be expressed using the query-transformation
chronometric operator with reference to the current date and the
chronometric column identifier, is equivalent to the representative
value of the chronometric `date` column.
[0303] In another example, the answer object may include data
describing a `current_month` filter. The semantic interface unit
may identify the `current_month` filter and may generate a
corresponding `current_month` expression including the
query-transformation chronometric operator, which may express the
`current_month` filter wherein a representative value, such as a
`month` ordinal value, of the chronometric `date` column may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `absolute_month_number` as shown in
Table 1, such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if a
representative value, such as a `month` ordinal value, of the
current date, which may be expressed using the query-transformation
chronometric operator with reference to the current date and the
chronometric column identifier, is equivalent to the representative
value of the chronometric `date` column.
[0304] The expressions may include expressions for `subsequent
period` type filters, which may indicate filtering the results to
omit data other than data corresponding a temporal interval
immediately subsequent to a current temporal interval.
[0305] For example, the answer object may include data describing a
`subsequent year` filter. The semantic interface unit may identify
the `subsequent year` filter and may generate a corresponding
`subsequent year` expression including the query-transformation
chronometric operator, which may express the `subsequent year`
filter wherein a representative value, such as a `year` ordinal
value, of the chronometric `date` column may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `absolute_year_number` as shown in Table 1, the
chronometric filter criteria may be expressed as the sum of a
representative value, such as a `year` ordinal value, of the
current date, which may be expressed using the query-transformation
chronometric operator with reference to the current date and the
chronometric column identifier, and the constant one (1), such that
data corresponding to a respective value of the chronometric `date`
column is included in the results data if the representative value
of the chronometric `date` column is equivalent to the sum.
[0306] In another example, the answer object may include data
describing a `subsequent quarter` filter. The semantic interface
unit may identify the `subsequent quarter` filter and may generate
a corresponding `subsequent quarter` expression including the
query-transformation chronometric operator, which may express the
`subsequent quarter` filter wherein a representative value, such as
a `quarter` ordinal value, of the chronometric `date` column may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `absolute_quarter_number` as shown in
Table 1, the chronometric filter criteria may be expressed as the
sum of a representative value, such as a `quarter` ordinal value,
of the current date, which may be expressed using the
query-transformation chronometric operator with reference to the
current date and the chronometric column identifier, and the
constant one (1), such that data corresponding to a respective
value of the chronometric `date` column is included in the results
data if the representative value of the chronometric `date` column
is equivalent to the sum.
[0307] In another example, the answer object may include data
describing a `subsequent month` filter. The semantic interface unit
may identify the `subsequent month` filter and may generate a
corresponding `subsequent month` expression including the
query-transformation chronometric operator, which may express the
`subsequent month` filter wherein a representative value, such as a
`month` ordinal value, of the chronometric `date` column may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `absolute_month_number` as shown in
Table 1, the chronometric filter criteria may be expressed as the
sum of a representative value, such as a `month` ordinal value, of
the current date, which may be expressed using the
query-transformation chronometric operator with reference to the
current date and the chronometric column identifier, and the
constant one (1), such that data corresponding to a respective
value of the chronometric `date` column is included in the results
data if the representative value of the chronometric `date` column
is equivalent to the sum.
[0308] In another example, the answer object may include data
describing a `subsequent week` filter. The semantic interface unit
may identify the `subsequent week` filter and may generate a
corresponding `subsequent week` expression including the
query-transformation chronometric operator, which may express the
`subsequent week` filter wherein a representative value, such as a
`week` ordinal value, of the chronometric `date` column may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `absolute_week_number` as shown in Table
1, the chronometric filter criteria may be expressed as the sum of
a representative value, such as a `week` ordinal value, of the
current date, which may be expressed using the query-transformation
chronometric operator with reference to the current date and the
chronometric column identifier, and the constant one (1), such that
data corresponding to a respective value of the chronometric `date`
column is included in the results data if the representative value
of the chronometric `date` column is equivalent to the sum.
[0309] The expressions may include expressions for
`subsequent_N_periods` type filters, which may indicate filtering
the results to omit data other than data corresponding a specified
cardinality (N) of temporal intervals immediately subsequent to a
current temporal interval.
[0310] For example, the answer object may include data describing a
`subsequent 3 years` filter. The semantic interface unit may
identify the `subsequent 3 years` filter and may generate a
corresponding `subsequent 3 years` expression including the
query-transformation chronometric operator, which may express the
`subsequent 3 years` filter wherein a representative value, such as
a `year` ordinal value, of the chronometric `date` column may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `absolute_year_number` as shown in Table
1, such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if the
representative value of the chronometric `date` column is less than
a representative value, such as a `year` ordinal value, of the
current date, which may be expressed using the query-transformation
chronometric operator with reference to the current date and the
chronometric column identifier, and a sum of the representative
value of the current date and the specified cardinality (3) of
temporal intervals is at least, such as greater than or equal to,
the representative value of the chronometric `date` column.
[0311] In another example, the answer object may include data
describing a `subsequent 3 quarters` filter. The semantic interface
unit may identify the `subsequent 3 quarters` filter and may
generate a corresponding `subsequent 3 quarters` expression
including the query-transformation chronometric operator, which may
express the `subsequent 3 quarters` filter wherein a representative
value, such as a `quarter` ordinal value, of the chronometric
`date` column may be expressed using the query-transformation
chronometric operator with reference to the chronometric `date`
column and a chronometric column identifier, such as
`absolute_quarter_number` as shown in Table 1, such that data
corresponding to a respective value of the chronometric `date`
column is included in the results data if the representative value
of the chronometric `date` column is less than a representative
value, such as a `quarter` ordinal value, of the current date,
which may be expressed using the query-transformation chronometric
operator with reference to the current date and the chronometric
column identifier, and a sum of the representative value of the
current date and the specified cardinality (3) of temporal
intervals is at least, such as greater than or equal to, the
representative value of the chronometric `date` column.
[0312] In another example, the answer object may include data
describing a `subsequent 3 months` filter. The semantic interface
unit may identify the `subsequent 3 months` filter and may generate
a corresponding `subsequent 3 months` expression including the
query-transformation chronometric operator, which may express the
`subsequent 3 months` filter wherein a representative value, such
as a `month` ordinal value, of the chronometric `date` column may
be expressed using the query-transformation chronometric operator
with reference to the chronometric `date` column and a chronometric
column identifier, such as `absolute_month_number` as shown in
Table 1, such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if the
representative value of the chronometric `date` column is less than
a representative value, such as a `month` ordinal value, of the
current date, which may be expressed using the query-transformation
chronometric operator with reference to the current date and the
chronometric column identifier, and a sum of the representative
value of the current date and the specified cardinality (3) of
temporal intervals is at least, such as greater than or equal to,
the representative value of the chronometric `date` column.
[0313] In another example, the answer object may include data
describing a `subsequent 3 weeks` filter. The semantic interface
unit may identify the `subsequent 3 weeks` filter and may generate
a corresponding `subsequent 3 weeks` expression including the
query-transformation chronometric operator, which may express the
`subsequent 3 weeks` filter wherein a representative value, such as
a `week` ordinal value, of the chronometric `date` column may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier, such as `absolute_week_number` as shown in Table
1, such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if the
representative value of the chronometric `date` column is less than
a representative value, such as a `week` ordinal value, of the
current date, which may be expressed using the query-transformation
chronometric operator with reference to the current date and the
chronometric column identifier, and a sum of the representative
value of the current date and the specified cardinality (3) of
temporal intervals is at least, such as greater than or equal to,
the representative value of the chronometric `date` column.
[0314] The expressions may include expressions for
`last_sub_per_super` type filters, which may indicate filtering the
results to omit data other than data corresponding temporal
intervals expressed with reference to a first chronometric unit
(sub) and a second chronometric unit (super) wherein instances of
the second chronometric unit include instances of the first
chronometric unit and wherein a respective instance of the first
chronometric unit is the temporally maximal (last) instance of the
first chronometric unit within a respective instance of the second
chronometric unit.
[0315] A `last_sub_per_super` expression may include a first
representative value, such as a maximum epoch value for a
respective instance of the sub chronometric unit, of the
chronometric `date` column, which may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier
indicating the chronometric column corresponding to the maximum
epoch value for a respective instance of the sub chronometric unit;
a second representative value, such as an ordinal value for the
super chronometric unit, of the first representative value of the
chronometric `date` column, which may be expressed using the
query-transformation chronometric operator with reference to the
first representative value of the chronometric `date` column and a
chronometric column identifier indicating the chronometric column
corresponding to the ordinal value for the super chronometric unit,
such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if the
second representative value is equivalent to a sum of one (1) and a
representative value, such as the ordinal value for the super
chronometric unit, of the of the chronometric `date` column, which
may be expressed using the query-transformation chronometric
operator with reference to the chronometric `date` column and the
chronometric column identifier indicating the chronometric column
corresponding to the ordinal value for the super chronometric
unit.
[0316] For example, the answer object may include data describing a
`last_month_per_quarter` filter. The semantic interface unit may
identify the data describing the `last_month_per_quarter` filter
and may generate a corresponding `last_month_per_quarter`
expression including the query-transformation chronometric
operator, which may express the `last_month_per_quarter` filter
wherein a first representative value, such as a maximum epoch value
for a respective instance of `month` chronometric unit, of the
chronometric `date` column may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `end_of_month_epoch` as shown in Table 1, a second
representative value, such as a `quarter` ordinal value, of the
first representative value of the chronometric `date` column may be
expressed using the query-transformation chronometric operator with
reference to the first representative value of the chronometric
`date` column and a chronometric column identifier, such as
`absolute_quarter_number` as shown in Table 1, such that data
corresponding to a respective value of the chronometric `date`
column is included in the results data if the second representative
value is equivalent to a sum of one (1) and a representative value,
such as a `quarter` ordinal value, of the of the chronometric
`date` column, which may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `absolute_quarter_number` as shown in Table 1.
[0317] The expressions may include expressions for
`last_N_subs_per_super` type filters, which may indicate filtering
the results to omit data other than data corresponding a specified
cardinality (N) of temporal intervals expressed with reference to a
first chronometric unit (sub) and a second chronometric unit
(super) wherein instances of the second chronometric unit include
instances of the first chronometric unit and wherein a contiguous
sequence of N respective instances of the first chronometric unit
includes the temporally maximal (last) instance of the first
chronometric unit within a respective instance of the second
chronometric unit.
[0318] A `last_N_subs_per_super` expression may include a first
representative value, such as a maximum epoch value for a
respective instance of the super chronometric unit, of the
chronometric `date` column, which may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier
indicating the chronometric column corresponding to the maximum
epoch value for a respective instance of the super chronometric
unit; a difference value, which may be expressed using a day
difference query-transformation chronometric operator with
reference to the first representative value and a defined
cordiality of days, such as one (1) day, which may indicate an
epoch value corresponding to a day temporally preceding the first
representative value by the defined cardinality of days; a second
representative value, such as an ordinal value for the sub
chronometric unit with respect to the super chronometric unit, of
the difference value, which may be expressed using the
query-transformation chronometric operator with reference to the
difference value and a chronometric column identifier indicating
the chronometric column corresponding to the ordinal value for the
sub chronometric unit with respect to the super chronometric unit;
such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if a
difference between the second representative value and a
representative value, such as the ordinal value for the sub
chronometric unit with respect to the super chronometric unit, of
the chronometric `date` column, which may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and the chronometric column identifier
indicating the chronometric column corresponding to the ordinal
value for the sub chronometric unit with respect to the super
chronometric unit, is less than the cardinality (N) of temporal
intervals.
[0319] For example, the answer object may include data describing a
`last_3_weeks_per_month` filter. The semantic interface unit may
identify the data describing the `last_3_weeks_per_month` filter
and may generate a corresponding `last_3_weeks_per_month`
expression including the query-transformation chronometric
operator, which may express the `last_3_weeks_per_month` filter
wherein a `last_3_weeks_per_month` expression may include a first
representative value, such as a maximum epoch value for a
respective instance of the `month` chronometric unit, of the
chronometric `date` column, which may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and a chronometric column identifier,
such as `end_of_month_epoch` as shown in Table 1; a difference
value, which may be expressed using a day difference
query-transformation chronometric operator with reference to the
first representative value and a defined cordiality of days, such
as one (1) day, which may indicate an epoch value corresponding to
a day temporally preceding the first representative value by the
defined cardinality of days; a second representative value, such as
an ordinal value for the `week` chronometric unit with respect to
the `month` chronometric unit, of the difference value, which may
be expressed using the query-transformation chronometric operator
with reference to the difference value and a chronometric column
identifier, such as `week_number_of_month` as shown in Table 1;
such that data corresponding to a respective value of the
chronometric `date` column is included in the results data if a
difference between the second representative value and a
representative value, such as the ordinal value for the `week`
chronometric unit with respect to the `month` chronometric unit, of
the chronometric `date` column, which may be expressed using the
query-transformation chronometric operator with reference to the
chronometric `date` column and the chronometric column identifier,
such as `week_number_of_month` as shown in Table 1, is less than
the cardinality (3) of temporal intervals.
[0320] In some implementations, an instance of a first chronometric
unit, such as a `year` chronometric unit, may include an instance
of a second chronometric unit, such as a `week` chronometric unit,
that includes an instance of a third chronometric unit, such as a
`day` chronometric unit that is omitted from the instance of the
first chronometric unit, and the `last_N_weeks_per_super`
expression may include a first representative value (DP1), such as
a maximum epoch value for a respective instance of the super
chronometric unit, of the chronometric `date` column, which may be
expressed using the query-transformation chronometric operator with
reference to the chronometric `date` column and a chronometric
column identifier indicating the chronometric column corresponding
to the maximum epoch value for a respective instance of the super
chronometric unit; a first difference value (DD1), which may be
expressed using the day difference query-transformation
chronometric operator with reference to the first representative
value and a defined cordiality of days, such as one (1) day, which
may indicate an epoch value corresponding to a day temporally
preceding the first representative value by the defined cardinality
of days; a second representative value (DP2), such as an ordinal
value for the sub chronometric unit with respect to the super
chronometric unit, of the first difference value, which may be
expressed using the query-transformation chronometric operator with
reference to the first difference value and a chronometric column
identifier indicating the chronometric column corresponding to the
ordinal value for the sub chronometric unit with respect to the
super chronometric unit; a third representative value (DP3), such
as a minimum epoch value for a respective instance of the sub
chronometric unit, of the first difference value, which may be
expressed using the query-transformation chronometric operator with
reference to the first difference value and a chronometric column
identifier indicating the chronometric column corresponding to the
minimum epoch value for a respective instance of the sub
chronometric unit; a second difference value (DD2), which may be
expressed using a day difference query-transformation chronometric
operator with reference to the third representative value and a
defined cordiality of days, such as one (1) day, which may indicate
an epoch value corresponding to a day temporally preceding the
third representative value by the defined cardinality of days; a
fourth representative value (DP4), such as an ordinal value for the
sub chronometric unit with respect to the super chronometric unit,
of the second difference value, which may be expressed using the
query-transformation chronometric operator with reference to the
second difference value and the chronometric column identifier
indicating the chronometric column corresponding to the ordinal
value for the sub chronometric unit with respect to the super
chronometric unit; and a fifth representative value (DP5), such as
the ordinal value for the sub chronometric unit with respect to the
super chronometric unit, of the chronometric `date` column, which
may be expressed using the query-transformation chronometric
operator with reference to the chronometric `date` column and the
chronometric column identifier indicating the chronometric column
corresponding to the ordinal value for the sub chronometric unit
with respect to the super chronometric unit; such that data
corresponding to a respective value of the chronometric `date`
column is included in the results data if the second representative
value differs from one (1) and a difference between the second
representative value and the fifth representative value is less
than the cardinality (N) of temporal intervals, or the second
representative value is equivalent to one (1) and a difference
between the fourth representative value and the fifth
representative value is less than the cardinality (N) of temporal
intervals, which may be expressed as the following:
((DP2!=1)&&((DP2-DP5)<N).parallel.(DP2==1)&&(DP4-DP5)).
[0321] Data corresponding to other values of the chronometric
`date` column may be omitted from the results data.
[0322] Generating a data-query, or data-queries, at 5420 may
include identifying or determining chronometry for the data-query,
or data-queries. For example, generating the proto-query may
include identifying the chronometry. An example of identifying
chronometry is shown in FIG. 7.
[0323] The semantic interface unit may send, or otherwise make
available, the data-queries to the distributed in-memory database
of the low-latency data access and analysis system. The distributed
in-memory database may receive, or otherwise access, the
data-queries. The distributed in-memory database may execute or
perform one or more queries to generate or obtain response data
responsive to the data-query based on the low-latency data at
5430.
[0324] The distributed in-memory database may output, or otherwise
make available, the results data, or a portion thereof, such as to
the semantic interface unit. The semantic interface unit may
receive, or otherwise access, the results data, or a portion
thereof, and may output, or otherwise make available, the results
data, or a portion thereof, such as to the relational search unit.
The relational search unit may receive, or otherwise access, the
results data, or a portion thereof, and may output, or otherwise
make available, the results data, or a portion thereof, such as to
the system access interface unit. The system access interface unit
may receive, or otherwise access, the results data, or a portion
thereof.
[0325] The results data may include a deterministic, or
contextually-unique, identifier for a respective results column.
For example, the deterministic identifier for a results column may
be generated based on a deterministic, or contextually-unique,
identifier for a corresponding ontological element in the answer
object. In some implementations, two or more results columns may
correspond with a respective ontological element in the answer
object and the respective deterministic, or contextually-unique,
identifier for the results columns may be determined based on the
deterministic, or contextually-unique, identifier for the
ontological element in the answer object and one or more other
properties of the ontological element.
[0326] In another example, a headline data element may be
associated with multiple results columns, and the deterministic
identifier for the respective results columns may be determined
based on a sequence identifier associated with the respective
results column.
[0327] The system access interface unit may output a response
including data representing the results data, or a portion thereof,
at 5500. The system access interface unit may generate data for
presenting a user interface, or one or more portions thereof,
representing the results data, such as visualization data. For
example, the system access interface unit may generate instructions
for rendering, or otherwise presenting, the user interface, or one
or more portions thereof and may transmit, or otherwise make
available, the instructions for rendering, or otherwise presenting,
the user interface, or one or more portions thereof to the system
access unit, for presentation to a user of a system access unit.
For example, the results data may include an epoch value and
outputting the response may include outputting data representing
the epoch value in accordance with a chronometric data
visualization for the system-defined chronometry at 5510.
[0328] The results data obtained at 5400 may include chronometric
results data for respective chronometric results ontological
elements. Obtaining the results data at 5400 may include obtaining
the chronometric results data from a respective chronometry
dataset, such as the canonical chronometry or a domain-specific
chronometry. The chronometric results data for a chronometric
results ontological element may include display-data, such as value
data, such as numeric value data, descriptor data, such as string
descriptor data or display value, or a combination thereof. Other
data defining or describing the chronometric results ontological
elements may be included. The results data obtained at 5400 may
include control-data ontological elements corresponding to
respective chronometric display-data ontological elements.
Outputting the results data at 5500 may include identifying the
data associating respective control-data ontological elements with
respective display-data ontological elements.
[0329] For example, the resolved-request may represent the request
string "precipitation yearly meteorological". The meteorological
domain-specific chronometry may define descriptors for respective
instances of the `year` chronometric unit with respect to an
ordered contiguous sequence of `year` chronometric units
corresponding to, or within, the era of the domain-specific
chronometry as "Y1", "Y2", "Y3", and the like. The results data
responsive to the resolved-request may include a `precipitation`
results display-data ontological element for including aggregated
precipitation values and a `yearly` results display-data
chronometric ontological element for including data representing
respective descriptors for year-wise date values, such as "Y1",
"Y2", and the like. The results data responsive to the
resolved-request may include a control-data ontological element for
a minimal temporal location value, which may be an epoch value, and
a control-data ontological element for a maximal temporal location
value, which may be an epoch value. The results data responsive to
the resolved-request may include a deterministic identifier for the
control-data ontological element for the minimal temporal location
value and a deterministic identifier for the control-data
ontological element for the maximal temporal location value.
Outputting the results data at 5500 may include determining that
the control-data ontological element for the minimal temporal
location value is associated with the `yearly` results display-data
chronometric ontological element based on the deterministic
identifier for the control-data ontological element for the minimal
temporal location value. Outputting the results data at 5500 may
include determining that the control-data ontological element for
the maximal temporal location value is associated with the `yearly`
results display-data chronometric ontological element based on the
deterministic identifier for the control-data ontological element
for the maximal temporal location value. For example, the
control-data ontological element for the minimal temporal location
value may include an epoch value corresponding to the descriptor
for the year-wise date value "Y1" indicating a temporally minimal
or earliest epoch value for the respective `year` chronometric unit
instance and the control-data ontological element for the maximal
temporal location value may include an epoch value corresponding to
the descriptor for the year-wise date value "Y1" indicating a
temporally maximal or latest epoch value for the respective `year`
chronometric unit instance.
[0330] In another example, the resolved-request may represent a
request string including the term "daily", the results data may
include a display-data oncological element including a descriptor,
such as "Monday", for an instance of a `day` chronometric unit, a
control data oncological element including a minimal temporal
location for the instance of the `day` chronometric unit, and a
control data oncological element including a maximal temporal
location for the instance of the `day` chronometric unit.
[0331] Although generating a resolved-request at 5300, obtaining
response data at 5400, and outputting a response at 5500 are shown
separately in FIG. 5, one or more aspects of generating a
resolved-request at 5300, obtaining response data at 5400, and
outputting a response at 5500 may be combined, one or more aspects
may be omitted, and one or more aspects other than the aspects
described may be included. For example, generating a
resolved-request at 5300, obtaining response data at 5400, and
outputting a response at 5500 may be referred to as obtaining
results data.
[0332] FIG. 6 is a flow diagram of a method of obtaining
domain-specific chronometry 6000 in a low-latency data access and
analysis system. Obtaining domain-specific chronometry 6000 may be
implemented in one or more components of a low-latency data access
and analysis system, such as the low-latency data access and
analysis system 3000 shown in FIG. 3, such as in a distributed
in-memory database, such as the distributed in-memory database 3300
shown in FIG. 3, a relational search unit, such as the relational
search unit 3700 shown in FIG. 3, a natural language processing
unit, such as the natural language processing unit 3710 shown in
FIG. 3, and a system access interface unit, such as the system
access interface unit 3900 shown in FIG. 3. Obtaining
domain-specific chronometry 6000 may be similar to the obtaining
domain-specific chronometry at 5120 shown in FIG. 5, except as
described herein, or otherwise clear from context.
[0333] Obtaining domain-specific chronometry 6000 includes
obtaining chronometry configuration data at 6100, generating a
domain-specific chronometry dataset at 6200, and storing the
domain-specific chronometry dataset at 6300.
[0334] Chronometry configuration data may be obtained at 6100.
Obtaining the chronometry configuration data may include obtaining
data expressing usage intent with respect to the low-latency data
access and analysis system indicating the chronometry configuration
data, or a portion thereof. In some implementations, the data
expressing usage intent may include a request to generate the
domain-specific chronometry, which may indicate, or may otherwise
be associated with, a current domain, which may be a sub-domain of
the system-domain, such as an organization-domain.
[0335] In some implementations, obtaining the chronometry
configuration data may include obtaining chronometry boundary
configuration data (CBCD) at 6110. For example, the data expressing
usage intent may include the chronometry boundary configuration
data, or a portion thereof. In some implementations, obtaining the
chronometry boundary configuration data may include obtaining the
chronometry boundary configuration data in response to user
input.
[0336] The chronometry boundary configuration data may include
chronometric location data. The chronometric location data may be
expressed, in the chronometry boundary configuration data, in
accordance with canonical chronometry, such as the system-defined
chronometry defined at 5100 as shown in FIG. 5. The chronometric
location data may include chronometric location data representing a
minimum temporal location for the domain-specific chronometry
dataset. The chronometric location data may include chronometric
location data representing a maximum temporal location for the
domain-specific chronometry dataset. The domain-specific
chronometry dataset may describe an era corresponding to a duration
from the minimum temporal location to the maximum temporal
location.
[0337] The chronometry boundary configuration data may include a
chronometry type identifier. The chronometry type identifier may
indicate a system-defined template, pattern, or prototype for the
domain-specific chronometry.
[0338] The chronometry boundary configuration data may include
chronometric relationship data. The chronometric relationship data
may define or describe a relationship among chronometric units of
the domain-specific chronometry.
[0339] For example, the domain-specific chronometry may define or
describe a first chronometric unit of the domain-specific
chronometry dataset as corresponding with, or including, an ordered
contiguous sequence of second chronometric units of the
domain-specific chronometry dataset, and the chronometric
relationship data may include chronometric relationship data
indicating a sequentially earliest second chronometric unit with
respect to the first chronometric unit, which may be expressed, in
the chronometry boundary configuration data, with reference to a
month-ordinal of the canonical chronometry.
[0340] In another example, the domain-specific chronometry may
define or describe a third chronometric unit of the domain-specific
chronometry dataset as corresponding with an ordered contiguous
sequence of fourth chronometric units of the domain-specific
chronometry dataset, and the chronometric relationship data may
include chronometric relationship data indicating a sequentially
earliest fourth chronometric unit with respect to the third
chronometric unit, which may be expressed, in the chronometry
boundary configuration data, with reference to a day-ordinal of the
canonical chronometry.
[0341] In another example, the chronometric relationship data may
include chronometric relationship data indicating, for the third
chronometric unit, a fourth chronometric unit from the ordered
sequence of fourth chronometric units, which may be expressed, in
the chronometry boundary configuration data, with reference to a
day-ordinal of the canonical chronometry, such that an instance of
a chronometric unit of the domain-specific chronometry dataset that
includes an instance of the fourth chronometric unit from the
ordered sequence of fourth chronometric units includes an instance
of the third chronometric unit that includes the instance of the
fourth chronometric unit.
[0342] The chronometry boundary configuration data may include
descriptor data for a chronometric unit of the domain-specific
chronometry dataset. A temporal location may be represented, such
as for output, such as for presentation to a user of the
low-latency data access and analysis system, based on the
descriptor data for a chronometric unit corresponding to the
temporal location, or a combination of the descriptor data and a
value of the chronometric unit corresponding to the temporal
location. For example, a temporal point may be represented in the
low-latency data access and analysis system as an epoch value, the
chronometry boundary configuration data may include descriptor data
for a year chronometric unit of the domain-specific chronometry
dataset, such as the string "Y", and output data expressing the
temporal point with reference to the year chronometric unit may
include a concatenation of the string "Y" with a value, such as and
integer value, of the year chronometric unit corresponding to the
temporal point.
[0343] Obtaining the chronometry configuration data may include
automatically generating the chronometry configuration data, or a
portion thereof, such as primary chronometry configuration data
(PCCD) at 6120. For example, one or more of the components of the
low-latency data access and analysis system may automatically
generate the primary chronometry configuration data, or a portion
thereof, in response to obtaining the chronometry boundary
configuration data, or a portion thereof, which may include
automatically generating the primary chronometry configuration
data, or the portion thereof, in accordance with the chronometry
boundary configuration data, or a portion thereof.
[0344] In some implementations, the chronometry boundary
configuration data may include output location data, such as output
location data indicating a computer storage location for outputting
the primary chronometry configuration data. Obtaining the
chronometry configuration data may include outputting the primary
chronometry configuration data, at 6130, in accordance with the
output location data, such as by storing the primary chronometry
configuration data, or otherwise causing the primary chronometry
configuration data to be stored, in the computer storage location
indicating in the chronometry boundary configuration data. In some
embodiments, the primary chronometry configuration data output at
6130 may be externally modified.
[0345] In some implementations, obtaining the chronometry boundary
configuration data at 6110, generating the primary chronometry
configuration data at 6120, outputting the primary chronometry
configuration data at 6130, or a combination thereof, may be
omitted, as indicated by the broken line boarder at 6110, 6120, and
6130.
[0346] Obtaining the chronometry configuration data may include
receiving, reading, or otherwise accessing, previously generated,
such as previously stored, domain-specific chronometry
configuration data (DSCCD) at 6140. Obtaining the chronometry
configuration data may include obtaining, receiving, reading, or
otherwise accessing, a name or descriptor for one or more
domain-specific chronometry datasets.
[0347] For example, obtaining the chronometry configuration data
may include automatically generating primary chronometry
configuration data at 6120, storing the primary chronometry
configuration data at 6130, such as in a computer storage location
indicated in chronometry boundary configuration data, and
receiving, reading, or otherwise accessing, chronometry
configuration data from a data source corresponding to the primary
chronometry configuration data, which may include the primary
chronometry configuration data, other chronometry configuration
data, or a combination thereof, as the domain-specific chronometry
configuration data at 6140.
[0348] In some implementations, obtaining the domain-specific
chronometry configuration data may include obtaining the
domain-specific chronometry configuration data in response to user
input. For example, the data expressing usage intent, which may be
obtained in response to user input, may include a request to
generate a domain-specific chronometry dataset and a computer
system file location, data storage location, or other data source,
and the domain-specific chronometry configuration data may be
obtained from the identified data source responsive to the request
to generate the domain-specific chronometry dataset.
[0349] The domain-specific chronometry dataset may define or
describe one or more chronometric units for the domain-specific
chronometry. One or more of the chronometric units for the
domain-specific chronometry may be correlated to corresponding
chronometric units for the system-defined chronometry. One or more
of the chronometric units for the domain-specific chronometry may
differ from the chronometric units for the system-defined
chronometry.
[0350] The domain-specific chronometry dataset may define or
describe a minimal chronometric unit (seconds) that is equivalent
to the minimal chronometric unit for the system-defined
chronometry. The domain-specific chronometry dataset may define or
describe a minute chronometric unit as an ordered contiguous
sequence of sixty (60) minimal chronometric units (seconds), which
may be equivalent to the minute chronometric unit of the
system-defined chronometry. The domain-specific chronometry dataset
may define or describe an hour chronometric unit as an ordered
contiguous sequence of sixty (60) minute chronometric units, which
may be equivalent to the hour chronometric unit of the
system-defined chronometry. The domain-specific chronometry dataset
may define or describe a day chronometric unit as an ordered
contiguous sequence of twenty-four (24) hour chronometric units,
which may be equivalent to the day chronometric unit of the
system-defined chronometry. In some implementations, the
domain-specific chronometry dataset may omit expressly defining or
describing the minimal chronometric unit (seconds), the minute
chronometric unit, the hour chronometric unit, the day chronometric
unit, or a combination thereof, and may use the minimal
chronometric unit (seconds), the minute chronometric unit, the hour
chronometric unit, the day chronometric unit, or a combination
thereof, defined in the system-defined chronometry.
[0351] The domain-specific chronometry configuration data may
include chronometric instance data, such as a row, record, or
otherwise delimited set of data (chronometry configuration data
portion), describing an instance of a chronometric unit for the
domain-specific chronometry dataset. For example, the
domain-specific chronometry configuration data may include
chronometric instance data describing an instance of the day
chronometric unit.
[0352] The domain-specific chronometry dataset may define or
describe an era corresponding to a duration from a minimum
chronometric location of the domain-specific chronometry dataset to
a maximum chronometric location of the domain-specific chronometry
dataset. The domain-specific chronometry configuration data may
include respective chronometric instance data describing the
respective instances of a chronometric unit, such as the day
chronometric unit, for the era of the domain-specific chronometry
dataset.
[0353] In some embodiments, automatically generating the primary
chronometry configuration data at 6120 may include generating the
chronometric instance data or a portion thereof. In some
embodiments, the chronometric instance data, or a portion thereof,
may differ from the primary chronometry configuration data.
Automatically generating the primary chronometry configuration data
may include identifying the minimum temporal location for the
domain-specific chronometry dataset from the chronometry boundary
configuration data. Automatically generating the primary
chronometry configuration data may include identifying the maximum
temporal location for the domain-specific chronometry dataset from
the chronometry boundary configuration data. For example,
automatically generating the primary chronometry configuration data
may include identifying a chronometry granularity for automatically
generating the primary chronometry configuration data. For example,
the system-defined day chronometric unit may be identified as the
chronometry granularity.
[0354] The chronometric instance data describing an instance of the
day chronometric unit may include a temporal location (temporal
location data) expressed in accordance with the canonical
chronometry, such as an epoch value (a first temporal location). In
some implementations, automatically generating the primary
chronometry configuration data based on the chronometry boundary
configuration data, as described herein, may include automatically
generating the temporal location data as primary chronometry
configuration data.
[0355] For example, automatically generating the primary
chronometry configuration data may include automatically generating
the primary chronometry configuration data on the basis of the
identified chronometry granularity, such as for respective day
chronometric units which may correspond with respective
chronometric instance data, or a portion thereof, such that the
primary chronometry configuration data includes primary chronometry
configuration data on a per-chronometry granularity chronometric
unit basis, such as a per-day basis, for a sequence of
corresponding chronometric units, such as a sequence of days,
corresponding to a duration from the minimum chronometric location
to the maximum chronometric location.
[0356] The chronometric instance data describing the instance of
the day chronometric unit may include a descriptor for the instance
of the day chronometric unit with respect to an ordered contiguous
sequence of day chronometric units corresponding to, or within, an
instance of a second chronometric unit of the domain-specific
chronometry dataset. The instance of the second chronometric unit
may have a temporal duration that is greater than or equal to
temporal duration of the day chronometric unit. The second
chronometric unit may differ from the day chronometric unit.
[0357] For example, the second chronometric unit of the
domain-specific chronometry dataset may be similar to the week
chronometric unit of the canonical chronometry, except as described
herein or otherwise clear from context, and the descriptor for the
instance of the day chronometric unit with respect to an ordered
contiguous sequence of day chronometric units corresponding to, or
within, an instance of a second chronometric unit of the
domain-specific chronometry dataset may be a day-of-week-name. The
respective descriptors for the instances of the day chronometric
unit with respect to the ordered contiguous sequence of day
chronometric units corresponding to, or within, an instance of a
week chronometric unit of the canonical chronometry are "Monday",
"Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", and
"Sunday". The descriptors for the instances of the day chronometric
unit with respect to the ordered contiguous sequence of day
chronometric units corresponding to, or within, the second
chronometric unit of the domain-specific chronometry dataset may be
similar to, or may differ from, the respective descriptors for the
instances of the day chronometric unit with respect to the ordered
contiguous sequence of day chronometric units corresponding to, or
within, an instance of a week chronometric unit of the canonical
chronometry.
[0358] In some implementations, automatically generating the
primary chronometry configuration data based on the chronometry
boundary configuration data, as described herein, may include
automatically generating the descriptor for the instance of the day
chronometric unit with respect to an ordered contiguous sequence of
day chronometric units corresponding to, or within, an instance of
a second chronometric unit of the domain-specific chronometry
dataset as primary chronometry configuration data, such as based on
the chronometry boundary configuration data.
[0359] The chronometric instance data describing the instance of
the day chronometric unit may include a descriptor for an instance
of a third chronometric unit. The instance of the third
chronometric unit may have a temporal duration that is greater than
or equal to the temporal duration of the day chronometric unit. The
instance of the third chronometric unit may include the instance of
the day chronometric unit. The third chronometric unit may differ
from the day chronometric unit and the second chronometric
unit.
[0360] For example, the third chronometric unit of the
domain-specific chronometry dataset may be similar to the month
chronometric unit of the canonical chronometry, except as described
herein or otherwise clear from context, and the descriptor for the
instance of the third chronometric unit may be a month-name. The
respective descriptors for the instances of the month chronometric
unit with respect to the ordered contiguous sequence of month
chronometric units corresponding to, or within, an instance of a
year chronometric unit of the canonical chronometry are "January",
"February", "March", "April", "May", "June", "July", "August",
"September", "October", "November", and "December". The descriptors
for the instances of the third chronometric unit with respect to
the ordered contiguous sequence of third chronometric units
corresponding to, or within, a fourth chronometric unit of the
domain-specific chronometry dataset may be similar to, or may
differ from, the respective descriptors for the instances of the
month chronometric unit with respect to the ordered contiguous
sequence of month chronometric units corresponding to, or within,
an instance of a year chronometric unit of the canonical
chronometry.
[0361] In some implementations, automatically generating the
primary chronometry configuration data based on the chronometry
boundary configuration data, as described herein, may include
automatically generating the descriptor for the instance of the
third chronometric unit as primary chronometry configuration data,
such as based on the chronometry boundary configuration data.
[0362] The chronometric instance data describing the instance of
the day chronometric unit may include a descriptor for an instance
of the fourth chronometric unit. The instance of the fourth
chronometric unit may have a temporal duration that is greater than
or equal to the temporal duration of the day chronometric unit. The
instance of the fourth chronometric unit may include the instance
of the day chronometric unit. The fourth chronometric unit may
differ from the day chronometric unit, the second chronometric
unit, and the third chronometric unit. The descriptor for the
instance of the fourth chronometric unit may include, for example,
a concatenation of a descriptor for the fourth chronometric unit
and a value, such as an integer value, of the instance of the
fourth chronometric unit, such as an ordinal value of the instance
of the fourth chronometric unit in an ordered contiguous sequence
of fourth chronometric units corresponding to, or included in, a
fifth chronometric unit.
[0363] For example, the fourth chronometric unit of the
domain-specific chronometry dataset may be similar to the quarter
chronometric unit of the canonical chronometry, except as described
herein or otherwise clear from context, and the descriptor for the
instance of the fourth chronometric unit may be a quarter-name. The
respective descriptors for the instances of the quarter
chronometric unit with respect to the ordered contiguous sequence
of quarter chronometric units corresponding to, or within, an
instance of the year chronometric unit of the canonical chronometry
are "Q1", "Q2", "Q3", and "Q4". The descriptors for the instances
of the fourth chronometric unit with respect to the ordered
contiguous sequence of fourth chronometric units corresponding to,
or within, a fifth chronometric unit of the domain-specific
chronometry dataset may be similar to, or may differ from, the
respective descriptors for the instances of the quarter
chronometric unit with respect to the ordered contiguous sequence
of quarter chronometric units corresponding to, or within, an
instance of the year chronometric unit of the canonical
chronometry.
[0364] In some implementations, automatically generating the
primary chronometry configuration data based on the chronometry
boundary configuration data, as described herein, may include
automatically generating the descriptor for the instance of the
fourth chronometric unit as primary chronometry configuration data,
such as based on the chronometry boundary configuration data.
[0365] The chronometric instance data describing the instance of
the day chronometric unit may include a descriptor for an instance
of the fifth chronometric unit. The instance of the fifth
chronometric unit may have a temporal duration that is greater than
or equal to the temporal duration of the day chronometric unit. The
instance of the fifth chronometric unit may include the instance of
the day chronometric unit. The fifth chronometric unit may differ
from the day chronometric unit, the second chronometric unit, the
third chronometric unit, and the fourth chronometric unit. The
descriptor for the instance of the fifth chronometric unit may
include, for example, a concatenation of the descriptor data for
the chronometric unit indicated in the chronometry boundary
configuration data and a value, such as and integer value, of the
instance of the fifth chronometric unit. For example, the value of
the instance of the fifth chronometric unit may be an ordinal value
of the instance of the fifth chronometric unit in an ordered
ordered contiguous sequence of fifth chronometric units
corresponding to, or included in, the era. For example, the fifth
chronometric unit of the domain-specific chronometry dataset may be
similar to the year chronometric unit of the canonical chronometry,
except as described herein or otherwise clear from context, and the
descriptor for the instance of the fifth chronometric unit may be a
year-name. In some implementations, automatically generating the
primary chronometry configuration data based on the chronometry
boundary configuration data, as described herein, may include
automatically generating the descriptor for the instance of the
fifth chronometric unit as primary chronometry configuration data,
such as based on the chronometry boundary configuration data.
[0366] The chronometric instance data describing the instance of
the day chronometric unit may include an ordinal value for the
instance of the day chronometric unit with respect to the ordered
contiguous sequence of day chronometric units corresponding to, or
within, the instance of the second chronometric unit of the
domain-specific chronometry dataset. For example, the second
chronometric unit of the domain-specific chronometry dataset may be
similar to the week chronometric unit of the canonical chronometry,
except as described herein or otherwise clear from context, and the
ordinal value for the instance of the day chronometric unit with
respect to the ordered contiguous sequence of day chronometric
units corresponding to, or within, the instance of the second
chronometric unit of the domain-specific chronometry dataset may be
a day-number-of-week. In some implementations, automatically
generating the primary chronometry configuration data based on the
chronometry boundary configuration data, as described herein, may
include automatically generating the ordinal value for the instance
of the day chronometric unit with respect to the ordered contiguous
sequence of day chronometric units corresponding to, or within, the
instance of the second chronometric unit of the domain-specific
chronometry dataset as primary chronometry configuration data, such
as based on the chronometry boundary configuration data.
[0367] The chronometric instance data describing the instance of
the day chronometric unit may include an ordinal value for the
instance of the second chronometric unit with respect to the
ordered contiguous sequence of second chronometric units
corresponding to, or within, the instance of the third chronometric
unit of the domain-specific chronometry dataset. For example, the
second chronometric unit of the domain-specific chronometry dataset
may be similar to the week chronometric unit of the canonical
chronometry, except as described herein or otherwise clear from
context, the third chronometric unit of the domain-specific
chronometry dataset may be similar to the month chronometric unit
of the canonical chronometry, except as described herein or
otherwise clear from context, and the ordinal value for the
instance of the second chronometric unit with respect to the
ordered contiguous sequence of second chronometric units
corresponding to, or within, the instance of the third chronometric
unit of the domain-specific chronometry dataset may be a
week-number-of-month. In some implementations, automatically
generating the primary chronometry configuration data based on the
chronometry boundary configuration data, as described herein, may
include automatically generating the ordinal value for the instance
of the second chronometric unit with respect to the ordered
contiguous sequence of second chronometric units corresponding to,
or within, the instance of the third chronometric unit of the
domain-specific chronometry dataset as primary chronometry
configuration data, such as based on the chronometry boundary
configuration data.
[0368] The chronometric instance data describing the instance of
the day chronometric unit may include an ordinal value for the
instance of the second chronometric unit with respect to the
ordered contiguous sequence of second chronometric units
corresponding to, or within, the instance of the fourth
chronometric unit of the domain-specific chronometry dataset. For
example, the second chronometric unit of the domain-specific
chronometry dataset may be similar to the week chronometric unit of
the canonical chronometry, except as described herein or otherwise
clear from context, the fourth chronometric unit of the
domain-specific chronometry dataset may be similar to the quarter
chronometric unit of the canonical chronometry, except as described
herein or otherwise clear from context, and the ordinal value for
the instance of the second chronometric unit with respect to the
ordered contiguous sequence of second chronometric units
corresponding to, or within, the instance of the fourth
chronometric unit of the domain-specific chronometry dataset may be
a week-number-of-quarter. In some implementations, automatically
generating the primary chronometry configuration data based on the
chronometry boundary configuration data, as described herein, may
include automatically generating the ordinal value for the instance
of the second chronometric unit with respect to the ordered
contiguous sequence of second chronometric units corresponding to,
or within, the instance of the fourth chronometric unit of the
domain-specific chronometry dataset as primary chronometry
configuration data, such as based on the chronometry boundary
configuration data.
[0369] The chronometric instance data describing the instance of
the day chronometric unit may include an ordinal value for the
instance of the second chronometric unit with respect to the
ordered contiguous sequence of second chronometric units
corresponding to, or within, the instance of the fifth chronometric
unit of the domain-specific chronometry dataset. For example, the
second chronometric unit of the domain-specific chronometry dataset
may be similar to the week chronometric unit of the canonical
chronometry, except as described herein or otherwise clear from
context, the fifth chronometric unit of the domain-specific
chronometry dataset may be similar to the year chronometric unit of
the canonical chronometry, except as described herein or otherwise
clear from context, and the ordinal value for the instance of the
second chronometric unit with respect to the ordered contiguous
sequence of second chronometric units corresponding to, or within,
the instance of the fifth chronometric unit of the domain-specific
chronometry dataset may be a week-number-of-year. In some
implementations, automatically generating the primary chronometry
configuration data based on the chronometry boundary configuration
data, as described herein, may include automatically generating the
ordinal value for the instance of the second chronometric unit with
respect to the ordered contiguous sequence of second chronometric
units corresponding to, or within, the instance of the fifth
chronometric unit of the domain-specific chronometry dataset as
primary chronometry configuration data, such as based on the
chronometry boundary configuration data.
[0370] Obtaining the chronometry configuration data may include
automatically generating a portion of the chronometry configuration
data, such as secondary chronometric instance data (SCID) at 6150.
The chronometry configuration data may include respective
chronometric instance data describing the respective instances of
the chronometric unit, such as the day chronometric unit, for the
era of the domain-specific chronometry dataset, and generating the
secondary chronometric instance data may include generating
respective secondary chronometric instance data describing the
respective instances of the chronometric unit, such as the day
chronometric unit, for the era of the domain-specific chronometry
dataset
[0371] The secondary chronometric instance data describing the
instance of the day chronometric unit may include a descriptor for
the instance of the third chronometric unit of the domain-specific
chronometry dataset with respect to the instance of the fifth
chronometric unit of the domain-specific chronometry dataset. For
example, the third chronometric unit of the domain-specific
chronometry dataset may be similar to the month chronometric unit
of the canonical chronometry, except as described herein or
otherwise clear from context, the fifth chronometric unit of the
domain-specific chronometry dataset may be similar to the year
chronometric unit of the canonical chronometry, except as described
herein or otherwise clear from context, and the descriptor for the
instance of the third chronometric unit with respect to the
instance of the fifth chronometric unit of the domain-specific
chronometry dataset may be a month-and-year-name. The descriptor
for the instance of the third chronometric unit of the
domain-specific chronometry dataset with respect to the instance of
the fifth chronometric unit of the domain-specific chronometry
dataset may include, for example, a concatenation of the descriptor
for the third chronometric unit and the value of the instance of
the fifth chronometric unit. For example, the descriptor for the
instance of the third chronometric unit of the domain-specific
chronometry dataset may correspond with the `Monthly` column shown
in Table 1.
[0372] The secondary chronometric instance data describing the
instance of the day chronometric unit may include a descriptor for
the instance of the fourth chronometric unit of the domain-specific
chronometry dataset with respect to the instance of the fifth
chronometric unit of the domain-specific chronometry dataset. For
example, the fourth chronometric unit of the domain-specific
chronometry dataset may be similar to the quarter chronometric unit
of the canonical chronometry, except as described herein or
otherwise clear from context, the fifth chronometric unit of the
domain-specific chronometry dataset may be similar to the year
chronometric unit of the canonical chronometry, except as described
herein or otherwise clear from context, and the descriptor for the
instance of the fourth chronometric unit with respect to the
instance of the fifth chronometric unit of the domain-specific
chronometry dataset may be a quarter-and-year-name. The descriptor
for the instance of the fourth chronometric unit with respect to
the instance of the fifth chronometric unit of the domain-specific
chronometry dataset may include, for example, a concatenation of
the descriptor for the fourth chronometric unit and the value of
the instance of the fifth chronometric unit. For example, the
descriptor for the instance of the fourth chronometric unit of the
domain-specific chronometry dataset may correspond with the
`Quarterly` column shown in Table 1.
[0373] The secondary chronometric instance data describing the
instance of the day chronometric unit may include an ordinal value
for the instance of the first chronometric unit with respect to the
ordered sequence of first chronometric units corresponding to, or
within, the instance of the third chronometric unit of the
domain-specific chronometry dataset. For example, the third
chronometric unit of the domain-specific chronometry dataset may be
similar to the month chronometric unit of the canonical
chronometry, except as described herein or otherwise clear from
context, and the ordinal value for the instance of the first
chronometric unit with respect to the ordered sequence of first
chronometric units corresponding to, or within, the instance of the
third chronometric unit of the domain-specific chronometry dataset
may be a day-number-of-month. For example, the ordinal value for
the instance of the first chronometric unit with respect to the
ordered sequence of first chronometric units corresponding to, or
within, the instance of the third chronometric unit of the
domain-specific chronometry dataset may correspond with the
`day_number_of_month` column shown in Table 1.
[0374] The secondary chronometric instance data describing the
instance of the day chronometric unit may include an ordinal value
for the instance of the first chronometric unit with respect to the
ordered sequence of first chronometric units corresponding to, or
within, the instance of the fourth chronometric unit of the
domain-specific chronometry dataset. For example, the fourth
chronometric unit of the domain-specific chronometry dataset may be
similar to the quarter chronometric unit of the canonical
chronometry, except as described herein or otherwise clear from
context, and the ordinal value for the instance of the first
chronometric unit with respect to the ordered sequence of first
chronometric units corresponding to, or within, the instance of the
fourth chronometric unit of the domain-specific chronometry dataset
may be a day-number-of-quarter. For example, the ordinal value for
the instance of the first chronometric unit with respect to the
ordered sequence of first chronometric units corresponding to, or
within, the instance of the fourth chronometric unit of the
domain-specific chronometry dataset may correspond with the
`day_number_of_quarter` column shown in Table 1.
[0375] The secondary chronometric instance data describing the
instance of the day chronometric unit may include an ordinal value
for the instance of the first chronometric unit with respect to the
ordered sequence of first chronometric units corresponding to, or
within, the instance of the fifth chronometric unit of the
domain-specific chronometry dataset. For example, the fifth
chronometric unit of the domain-specific chronometry dataset may be
similar to the year chronometric unit of the canonical chronometry,
except as described herein or otherwise clear from context, and the
ordinal value for the instance of the first chronometric unit with
respect to the ordered sequence of first chronometric units
corresponding to, or within, the instance of the fifth chronometric
unit of the domain-specific chronometry dataset may be a
day-number-of-year. For example, the ordinal value for the instance
of the first chronometric unit with respect to the ordered sequence
of first chronometric units corresponding to, or within, the
instance of the fifth chronometric unit of the domain-specific
chronometry dataset may correspond with the `day_number_of_year`
column shown in Table 1.
[0376] The secondary chronometric instance data describing the
instance of the day chronometric unit may include an ordinal value
for the instance of the third chronometric unit with respect to the
ordered sequence of third chronometric units corresponding to, or
within, the instance of the fourth chronometric unit of the
domain-specific chronometry dataset. For example, the third
chronometric unit of the domain-specific chronometry dataset may be
similar to the month chronometric unit of the canonical
chronometry, the fourth chronometric unit of the domain-specific
chronometry dataset may be similar to the quarter chronometric unit
of the canonical chronometry, except as described herein or
otherwise clear from context, and the ordinal value for the
instance of the third chronometric unit with respect to the ordered
sequence of third chronometric units corresponding to, or within,
the instance of the fourth chronometric unit of the domain-specific
chronometry dataset may be a month-number-of-quarter. For example,
the ordinal value for the instance of the third chronometric unit
with respect to the ordered sequence of third chronometric units
corresponding to, or within, the instance of the fourth
chronometric unit of the domain-specific chronometry dataset may
correspond with the `month_number_of_quarter` column shown in Table
1.
[0377] The secondary chronometric instance data describing the
instance of the day chronometric unit may include an ordinal value
for the instance of the third chronometric unit with respect to the
ordered sequence of third chronometric units corresponding to, or
within, the instance of the fifth chronometric unit of the
domain-specific chronometry dataset. For example, the third
chronometric unit of the domain-specific chronometry dataset may be
similar to the month chronometric unit of the canonical
chronometry, the fifth chronometric unit of the domain-specific
chronometry dataset may be similar to the year chronometric unit of
the canonical chronometry, except as described herein or otherwise
clear from context, and the ordinal value for the instance of the
third chronometric unit with respect to the ordered sequence of
third chronometric units corresponding to, or within, the instance
of the fifth chronometric unit of the domain-specific chronometry
dataset may be a month-number-of-year. For example, the ordinal
value for the instance of the third chronometric unit with respect
to the ordered sequence of third chronometric units corresponding
to, or within, the instance of the fifth chronometric unit of the
domain-specific chronometry dataset may correspond with the
`month_number_of_year` column shown in Table 1.
[0378] The secondary chronometric instance data describing the
instance of the day chronometric unit may include an ordinal value
for the instance of the fourth chronometric unit with respect to
the ordered sequence of fourth chronometric units corresponding to,
or within, the instance of the fifth chronometric unit of the
domain-specific chronometry dataset. For example, the fourth
chronometric unit of the domain-specific chronometry dataset may be
similar to the quarter chronometric unit of the canonical
chronometry, the fifth chronometric unit of the domain-specific
chronometry dataset may be similar to the year chronometric unit of
the canonical chronometry, except as described herein or otherwise
clear from context, and the ordinal value for the instance of the
fourth chronometric unit with respect to the ordered sequence of
fourth chronometric units corresponding to, or within, the instance
of the fifth chronometric unit of the domain-specific chronometry
dataset may be a quarter-number-of-year. For example, the ordinal
value for the instance of the fourth chronometric unit with respect
to the ordered sequence of fourth chronometric units corresponding
to, or within, the instance of the fifth chronometric unit of the
domain-specific chronometry dataset may correspond with the
`quarter_number_of_year` column shown in Table 1.
[0379] The secondary chronometric instance data describing the
instance of the day chronometric unit may include a sequence value
for the instance of the second chronometric unit with respect to an
ordered contiguous sequence of second chronometric units
corresponding to, or within, the era of the domain-specific
chronometry dataset. For example, the second chronometric unit of
the domain-specific chronometry dataset may be similar to the week
chronometric unit of the canonical chronometry, and the sequence
value for the instance of the second chronometric unit with respect
to the ordered contiguous sequence of second chronometric units
corresponding to, or within, the era of the domain-specific
chronometry dataset may be an era-week-number. For example, the
sequence value for the instance of the second chronometric unit
with respect to an ordered contiguous sequence of second
chronometric units corresponding to, or within, the era of the
domain-specific chronometry dataset may correspond with the
`absolute_week_number` column shown in Table 1.
[0380] The secondary chronometric instance data describing the
instance of the day chronometric unit may include a second temporal
location expressed in accordance with the canonical chronometry.
The second temporal location may be expressed as an epoch value.
The second temporal location may correspond to a minimum day
chronometric unit with respect to the ordered sequence of day
chronometric units within the instance of the second chronometric
unit of the domain-specific chronometry dataset that includes the
instance of the day chronometric unit. For example, the second
temporal location may correspond with the start_of_week_epoch shown
in Table 1.
[0381] The secondary chronometric instance data describing the
instance of the day chronometric unit may include a third temporal
location expressed in accordance with the canonical chronometry.
The third temporal location may be expressed as an epoch value. The
third temporal location may correspond to a maximum day
chronometric unit with respect to the ordered sequence of day
chronometric units within the instance of the second chronometric
unit of the domain-specific chronometry dataset that includes the
instance of the day chronometric unit. For example, the third
temporal location may correspond with the `end_of_week_epoch`
column shown in Table 1.
[0382] The secondary chronometric instance data describing the
instance of the day chronometric unit may include a sequence value
for the instance of the third chronometric unit with respect to an
ordered contiguous sequence of third chronometric units
corresponding to, or within, the era of the domain-specific
chronometry dataset. For example, the third chronometric unit of
the domain-specific chronometry dataset may be similar to the month
chronometric unit of the canonical chronometry, and the sequence
value for the instance of the third chronometric unit with respect
to the ordered contiguous sequence of third chronometric units
corresponding to, or within, the era of the domain-specific
chronometry dataset may be an era-month-number. For example, the
sequence value for the instance of the third chronometric unit with
respect to an ordered contiguous sequence of third chronometric
units corresponding to, or within, the era of the domain-specific
chronometry dataset may correspond with the `absolute_month_number`
column shown in Table 1.
[0383] The secondary chronometric instance data describing the
instance of the day chronometric unit may include a fourth temporal
location expressed in accordance with the canonical chronometry.
The fourth temporal location may be expressed as an epoch value.
The fourth temporal location may correspond to a minimum day
chronometric unit with respect to the ordered sequence of day
chronometric units within the instance of the third chronometric
unit of the domain-specific chronometry dataset that includes the
instance of the day chronometric unit. For example, the fourth
temporal location may correspond with the `start of month epoch`
column shown in Table 1.
[0384] The secondary chronometric instance data describing the
instance of the day chronometric unit may include a fifth temporal
location expressed in accordance with the canonical chronometry.
The fifth temporal location may be expressed as an epoch value. The
fifth temporal location may correspond to a maximum day
chronometric unit with respect to the ordered sequence of day
chronometric units within the instance of the third chronometric
unit of the domain-specific chronometry dataset that includes the
instance of the day chronometric unit. For example, the fifth
temporal location may correspond with the `end_of_month_epoch`
column shown in Table 1.
[0385] The secondary chronometric instance data describing the
instance of the day chronometric unit may include a sequence value
for the instance of the fourth chronometric unit with respect to an
ordered contiguous sequence of fourth chronometric units
corresponding to, or within, the era of the domain-specific
chronometry dataset. For example, the fourth chronometric unit of
the domain-specific chronometry dataset may be similar to the
quarter chronometric unit of the canonical chronometry, and the
sequence value for the instance of the fourth chronometric unit
with respect to the ordered contiguous sequence of fourth
chronometric units corresponding to, or within, the era of the
domain-specific chronometry dataset may be an
era-quarter-number.
[0386] For example, the sequence value for the instance of the
fourth chronometric unit with respect to an ordered contiguous
sequence of fourth chronometric units corresponding to, or within,
the era of the domain-specific chronometry dataset may correspond
with the `absolute_quarter_number` column shown in Table 1.
[0387] The secondary chronometric instance data describing the
instance of the day chronometric unit may include a sixth temporal
location expressed in accordance with the canonical chronometry.
The sixth temporal location may be expressed as an epoch value. The
sixth temporal location may correspond to a minimum day
chronometric unit with respect to the ordered sequence of day
chronometric units within the instance of the fourth chronometric
unit of the domain-specific chronometry dataset that includes the
instance of the day chronometric unit. For example, the sixth
temporal location may correspond with the `start_of_quarter_epoch`
column shown in Table 1.
[0388] The secondary chronometric instance data describing the
instance of the day chronometric unit may include a seventh
temporal location expressed in accordance with the canonical
chronometry. The seventh temporal location may be expressed as an
epoch value. The seventh temporal location may correspond to a
maximum day chronometric unit with respect to the ordered sequence
of day chronometric units within the instance of the fourth
chronometric unit of the domain-specific chronometry dataset that
includes the instance of the day chronometric unit. For example,
the seventh temporal location may correspond with the
`end_of_quarter_epoch` column shown in Table 1.
[0389] The secondary chronometric instance data describing the
instance of the day chronometric unit may include a sequence value
for the instance of the fifth chronometric unit with respect to an
ordered contiguous sequence of fifth chronometric units
corresponding to, or within, the era of the domain-specific
chronometry dataset. For example, the fifth chronometric unit of
the domain-specific chronometry dataset may be similar to the year
chronometric unit of the canonical chronometry, and the sequence
value for the instance of the fifth chronometric unit with respect
to the ordered contiguous sequence of fifth chronometric units
corresponding to, or within, the era of the domain-specific
chronometry dataset may be an era-year-number. For example, the
sequence value for the instance of the fifth chronometric unit with
respect to an ordered contiguous sequence of fifth chronometric
units corresponding to, or within, the era of the domain-specific
chronometry dataset may correspond with the `absolute_year_number`
column shown in Table 1.
[0390] The secondary chronometric instance data describing the
instance of the day chronometric unit may include an eighth
temporal location expressed in accordance with the canonical
chronometry. The eighth temporal location may be expressed as an
epoch value. The eighth temporal location may correspond to a
minimum day chronometric unit with respect to the ordered sequence
of day chronometric units within the instance of the fifth
chronometric unit of the domain-specific chronometry dataset that
includes the instance of the day chronometric unit. For example,
the eighth temporal location may correspond with the
`start_of_year_epoch` column shown in Table 1.
[0391] The secondary chronometric instance data describing the
instance of the day chronometric unit may include a ninth temporal
location expressed in accordance with the canonical chronometry.
The ninth temporal location may be expressed as an epoch value. The
ninth temporal location may correspond to a maximum day
chronometric unit with respect to the ordered sequence of day
chronometric units within the instance of the fifth chronometric
unit of the domain-specific chronometry dataset that includes the
instance of the day chronometric unit. For example, the ninth
temporal location may correspond with the `end_of_year_epoch`
column shown in Table 1.
[0392] The secondary chronometric instance data describing the
instance of the day chronometric unit may include a Boolean or
binary value, such as a flag or bit, which may be an integer
datatype including the values zero (0) and one (1), indicating
whether the instance of the day chronometric unit is identified as
having a `weekend` property or a `weekday` property. For example,
the secondary chronometric instance data describing the instance of
the day chronometric unit may include a bit having the value zero
(0) indicating that the instance of the day chronometric unit has
the `weekday` property or the secondary chronometric instance data
describing the instance of the day chronometric unit may include a
bit having the value one (1) indicating that the instance of the
day chronometric unit has the `weekend` property. For example, the
value indicating whether the instance of the day chronometric unit
is identified as having a `weekend` property or a `weekday`
property may correspond with the `is_weekend` column shown in Table
1.
[0393] In some implementations, obtaining the chronometry
configuration data may include storing, such as in a data storage
unit of the low-latency data access and analysis system, the
chronometric instance data, the secondary chronometric instance
data, or a combination thereof.
[0394] The domain-specific chronometry dataset may be generated at
6200 in accordance with the chronometry configuration data obtained
at 6100. The domain-specific chronometry dataset may be stored in
the low-latency data access and analysis system at 6300. Generating
the domain-specific chronometry dataset at 6200 and storing the
domain-specific chronometry dataset at 6300 may be combined.
[0395] Generating the domain-specific chronometry dataset may be
similar to generating the canonical chronometry dataset as shown at
5110 in FIG. 5, except as described herein or otherwise clear from
context. The domain-specific chronometry dataset describes a
chronometric unit for the domain-specific chronometry such that a
temporal location expressed with reference to the chronometric unit
and indicative of an epoch value differs from a temporal location
expressed in accordance with the canonical chronometry and
indicative of the epoch value.
[0396] Generating the domain-specific chronometry dataset at 6200,
storing the domain-specific chronometry dataset at 6300, or a
combination thereof, may include generating, storing or both, a
chronometry table for the domain-specific chronometry, such as in a
distributed in-memory database of the low-latency data access and
analysis system, such as the distributed in-memory database 3300
shown in FIG. 3. Generating the domain-specific chronometry dataset
at 6200, storing the domain-specific chronometry dataset at 6300,
or a combination thereof, may include populating the chronometry
table for the domain-specific chronometry based on the chronometry
configuration data obtained at 6100. For example, a record or row
may be generated, populated, or both, in the chronometry table for
the domain-specific chronometry corresponding to a respective
chronometric instance indicated in the chronometry configuration
data. The chronometry table for the domain-specific chronometry may
be similar to the chronometry table for the canonical chronometry
as shown in Table 1, except as described herein or otherwise clear
from context. For example, the chronometry table for the
domain-specific chronometry may include one or more domain-specific
columns that differ from the columns shown in Table 1, such as a
`season` column.
[0397] Generating the domain-specific chronometry dataset at 6200,
storing the domain-specific chronometry dataset at 6300, or a
combination thereof, may include generating an object representing
the domain-specific chronometry dataset and storing the object in a
distributed in-memory ontology of the low-latency data access and
analysis system. Generating the domain-specific chronometry dataset
at 6200, storing the domain-specific chronometry dataset at 6300,
or a combination thereof, may include generating edge data
representing a relationship between a date column in the
low-latency data access and analysis system and the domain-specific
chronometry dataset and storing the edge data in the distributed
in-memory ontology of the low-latency data access and analysis
system.
[0398] Generating the domain-specific chronometry dataset at 6200,
storing the domain-specific chronometry dataset at 6300, or a
combination thereof, may include generating and sending, or
otherwise making available, ontological data representing the
domain-specific chronometry dataset to the relational search unit
of the low-latency data access and analysis system for indexing.
For example, the ontological data representing the domain-specific
chronometry dataset sent, or otherwise made available, to the
relational search unit may include ontological data representing
the calendar object, ontological data representing respective
chronometric units, or a combination thereof. The relational search
unit may include data corresponding to the ontological data
representing the domain-specific chronometry dataset in a
previously generated index of the low-latency data access and
analysis system. In some implementations, the relational search
unit may generate an index based on the ontological data
representing the domain-specific chronometry dataset.
[0399] In some implementations, the relational search unit may
include the name or descriptor for the domain-specific chronometry
dataset, such as obtained from the ontological data representing
the domain-specific chronometry dataset, in an index of chronometry
names or descriptors, in association with a unique identifier of
the domain-specific chronometry dataset, such as an identifier of
the chronometry table, such that the relational search unit may
identify the domain-specific chronometry dataset, such as the
chronometry table, by traversing the index based on a value, such
as a string corresponding to the name or descriptor, or a portion
thereof. The relational search unit may identify the chronometry
datasets, such as the canonical chronometry, the domain-specific
chronometry, or a combination thereof. For example, the relational
search unit may identify the chronometry datasets available in the
low-latency data access and analysis system by traversing the index
of chronometry. The relational search unit may output, send,
transmit, or otherwise make available data indicating the
chronometry datasets.
[0400] Generating the domain-specific chronometry dataset at 6200,
storing the domain-specific chronometry dataset at 6300, or a
combination thereof, may include generating a finite state machine
(FSM) based on the domain-specific chronometry dataset. For
example, the relational search unit of the low-latency data access
and analysis system may generate the finite state machine based on
the ontological data representing the domain-specific chronometry
dataset.
[0401] FIG. 7 is a flow diagram of a method of chronometry
determination for data-query generation for analytical data access
with available domain-specific chronometry 7000 in a low-latency
data access and analysis system. Chronometry determination for
data-query generation for analytical data access with available
domain-specific chronometry 7000 may be implemented in one or more
components of a low-latency data access and analysis system, such
as the low-latency data access and analysis system 3000 shown in
FIG. 3, such as in a semantic interface unit of the low-latency
data access and analysis system, such as the semantic interface
unit 3600 shown in FIG. 3.
[0402] Chronometry determination for data-query generation for
analytical data access with available domain-specific chronometry
7000 includes obtaining a current expression at 7100. For example,
a relational search unit of the low-latency data access and
analysis system, such as the relational search unit 3700 shown in
FIG. 3, may obtain a resolved-request, such as shown at 5300 in
FIG. 5, and may send, or otherwise make available, a
resolved-request to the semantic interface unit. The semantic
interface unit may receive, or otherwise access, the
resolved-request, the semantic interface unit may obtain an answer
object including a expression representing results data responsive
to the resolved-request, which may be identified as the current
expression at 7100.
[0403] Chronometry determination for data-query generation for
analytical data access with available domain-specific chronometry
7000 includes determining whether chronometric data is requested at
7200. Determining whether chronometric data is requested at 7200
may include determining whether chronometric data is requested
based on the current expression. Determining whether chronometric
data is requested at 7200 may include determining whether the
current expression includes a chronometric operator expression. For
example, the semantic interface unit may determine whether the
current expression includes a chronometric operator expression. The
current expression may include one or more expressions, and the
semantic interface unit may traverse each expression, such as
hierarchically, such as in depth-first order, to determine whether
an expression from the current expression includes a chronometric
operator.
[0404] Determining whether chronometric data is requested at 7200
may include determining that the current expression omits a request
for chronometric data (e.g., omits a chronometric operator). In
response to determining that the current expression omits a request
for chronometric data, chronometry determination for data-query
generation for analytical data access with available
domain-specific chronometry 7000 may be otherwise omitted with
respect to the current expression. Analytical data access, other
than chronometry determination for data-query generation for
analytical data access with available domain-specific chronometry
7000 may be otherwise performed for the current expression.
[0405] Chronometry determination for data-query generation for
analytical data access with available domain-specific chronometry
7000 may include traversing the current expression at 7300. For
example, the current expression obtained at 7100 may be a complex
expression including two or more expressions. Traversing the
complex expression at 7300 may include identifying the expressions,
or nodes, of the complex expression, and identifying, such as on a
per-expression basis, a respective expression from the complex
expression as the current expression. For example, the current
expression may be traversed on a depth-first basis. The semantic
interface unit may perform chronometry determination for data-query
generation for analytical data access with available
domain-specific chronometry 7000, or a portion thereof, for
respective expressions serially or in parallel, as indicated by the
double directional arrow at 7310.
[0406] Chronometry determination for data-query generation for
analytical data access with available domain-specific chronometry
7000 may include determining whether the current expression
includes a complex expression at 7400. For example, the current
expression may include an operator expression and the current
expression may be identified as a complex expression. In another
example, the current expression may include a column expression or
a constant expression, and the current expression may be identified
as a simple expression.
[0407] In response to a determination that the current expression
includes a complex expression, the semantic interface unit may
perform chronometry determination for data-query generation for
analytical data access with available domain-specific chronometry
7000 for the current expression as indicated by the broken
directional arrow at 7410.
[0408] Chronometry determination for data-query generation for
analytical data access with available domain-specific chronometry
7000 may include determining whether the current expression
includes an express indication of a chronometry dataset at 7500.
For example, in response to a determination that the current
expression omits a complex expression, the semantic interface unit
may determine whether the current expression includes an express
indication of a chronometry dataset, such as a constant expression
indicating an express chronometry dataset, at 7500.
[0409] Chronometry determination for data-query generation for
analytical data access with available domain-specific chronometry
7000 may include identifying the chronometry dataset expressly
indicated in the current expression as the chronometry dataset for
the current expression at 7510. For example, in response to a
determination that the current expression includes an express
indication of a chronometry dataset at 7500, the semantic interface
unit may identify the chronometry dataset expressly indicated in
the current expression as the chronometry dataset for the current
expression at 7510.
[0410] In response to the determination that the current expression
includes the express indication of the chronometry dataset at 7500,
the semantic interface unit may omit performing chronometry
determination for data-query generation for analytical data access
with available domain-specific chronometry 7000 for other
expressions from the current expression identified at 7100 for the
answer object. In response to the determination that the current
expression includes the express indication of the chronometry
dataset at 7500, the semantic interface unit may omit using a
chronometry dataset identified for other expressions from the
current expression identified at 7100 for the answer object.
[0411] Chronometry determination for data-query generation for
analytical data access with available domain-specific chronometry
7000 may include determining whether the current expression
includes a complex object at 7600. For example, in response to a
determination that the current expression omits an express
indication of a chronometry dataset at 7500, the semantic interface
unit may determine whether the current expression includes a
complex object at 7600.
[0412] The current expression may include a constant expression,
other than a constant expression indicating express indication of a
chronometry dataset, and the current expression may be identified
as including a complex object at 7600. In response to a
determination that the current expression includes a complex object
at 7600, chronometry determination for data-query generation for
analytical data access with available domain-specific chronometry
7000 may be otherwise omitted with respect to the current
expression.
[0413] Chronometry determination for data-query generation for
analytical data access with available domain-specific chronometry
7000 may include determining whether the current expression is
associated with a chronometry dataset at 7700. For example, in
response to a determination that the current expression omits a
complex object at 7600, the current expression may be identified as
indicating a current object, such as a column object, and the
semantic interface unit may determine whether data associating the
current object with a chronometry dataset is available at 7700.
[0414] Chronometry determination for data-query generation for
analytical data access with available domain-specific chronometry
7000 may include identifying the chronometry dataset associated
with the current object as the chronometry dataset for the current
expression at 7710. For example, in response to a determination
that data associating the current object with a chronometry dataset
is available at 7700, the semantic interface unit may identify the
chronometry dataset associated with the current object as the
chronometry dataset for the current expression at 7710.
[0415] Chronometry determination for data-query generation for
analytical data access with available domain-specific chronometry
7000 may include determining whether a domain-specific chronometry
dataset is associated with a domain for the current expression at
7800. For example, in response to a determination that data
associating the current object with a chronometry dataset is
unavailable at 7700, the semantic interface unit may determine
whether data indicating a domain-specific chronometry dataset
associated with a domain for the current expression is available at
7800.
[0416] Chronometry determination for data-query generation for
analytical data access with available domain-specific chronometry
7000 may include identifying the domain-specific chronometry
dataset as the chronometry dataset for the data-query at 7810. For
example, in response to a determination that data indicating the
domain-specific chronometry dataset associated with the domain for
the current expression is available, the semantic interface unit
may identify the domain-specific chronometry dataset as the
chronometry dataset for the current expression at 7810.
[0417] Chronometry determination for data-query generation for
analytical data access with available domain-specific chronometry
7000 may include identifying the canonical chronometry dataset as
the chronometry dataset for the current expression at 7820. For
example, in response to a determination that data indicating the
domain-specific chronometry dataset associated with the domain for
the current expression is unavailable, the semantic interface unit
may identify the canonical chronometry dataset as the chronometry
dataset for the current expression at 7820.
[0418] In some implementations, the complex expression traversed at
7300 may include an expression, or expressions, subsequent to the
current expression, such as in the depth-first traversal order, and
the semantic interface unit may perform chronometry determination
for data-query generation for analytical data access with available
domain-specific chronometry 7000, or a portion thereof, for the
subsequent expressions, as indicated by the broken directional
arrow at 7900.
[0419] As used herein, the terminology "computer" or "computing
device" includes any unit, or combination of units, capable of
performing any method, or any portion or portions thereof,
disclosed herein.
[0420] As used herein, the terminology "processor" indicates one or
more processors, such as one or more special purpose processors,
one or more digital signal processors, one or more microprocessors,
one or more controllers, one or more microcontrollers, one or more
application processors, one or more central processing units
(CPU)s, one or more graphics processing units (GPU)s, one or more
digital signal processors (DSP)s, one or more application specific
integrated circuits (ASIC)s, one or more application specific
standard products, one or more field programmable gate arrays, any
other type or combination of integrated circuits, one or more state
machines, or any combination thereof.
[0421] As used herein, the terminology "memory" indicates any
computer-usable or computer-readable medium or device that can
tangibly contain, store, communicate, or transport any signal or
information that may be used by or in connection with any
processor. For example, a memory may be one or more read only
memories (ROM), one or more random access memories (RAM), one or
more registers, low power double data rate (LPDDR) memories, one or
more cache memories, one or more semiconductor memory devices, one
or more magnetic media, one or more optical media, one or more
magneto-optical media, or any combination thereof.
[0422] As used herein, the terminology "instructions" may include
directions or expressions for performing any method, or any portion
or portions thereof, disclosed herein, and may be realized in
hardware, software, or any combination thereof. For example,
instructions may be implemented as information, such as a computer
program, stored in memory that may be executed by a processor to
perform any of the respective methods, algorithms, aspects, or
combinations thereof, as described herein. Instructions, or a
portion thereof, may be implemented as a special purpose processor,
or circuitry, that may include specialized hardware for carrying
out any of the methods, algorithms, aspects, or combinations
thereof, as described herein. In some implementations, portions of
the instructions may be distributed across multiple processors on a
single device, on multiple devices, which may communicate directly
or across a network such as a local area network, a wide area
network, the Internet, or a combination thereof.
[0423] As used herein, the terminology "determine," "identify,"
"obtain," and "form" or any variations thereof, includes selecting,
ascertaining, computing, looking up, receiving, determining,
establishing, obtaining, or otherwise identifying or determining in
any manner whatsoever using one or more of the devices and methods
shown and described herein.
[0424] As used herein, the term "computing device" includes any
unit, or combination of units, capable of performing any method, or
any portion or portions thereof, disclosed herein.
[0425] As used herein, the terminology "example," "embodiment,"
"implementation," "aspect," "feature," or "element" indicates
serving as an example, instance, or illustration. Unless expressly
indicated, any example, embodiment, implementation, aspect,
feature, or element is independent of each other example,
embodiment, implementation, aspect, feature, or element and may be
used in combination with any other example, embodiment,
implementation, aspect, feature, or element.
[0426] As used herein, the terminology "or" is intended to mean an
inclusive "or" rather than an exclusive "or." That is, unless
specified otherwise, or clear from context, "X includes A or B" is
intended to indicate any of the natural inclusive permutations.
That is, if X includes A; X includes B; or X includes both A and B,
then "X includes A or B" is satisfied under any of the foregoing
instances. In addition, the articles "a" and "an" as used in this
application and the appended claims should generally be construed
to mean "one or more" unless specified otherwise or clear from the
context to be directed to a singular form.
[0427] Further, for simplicity of explanation, although the figures
and descriptions herein may include sequences or series of steps or
stages, elements of the methods disclosed herein may occur in
various orders or concurrently. Additionally, elements of the
methods disclosed herein may occur with other elements not
explicitly presented and described herein. Furthermore, not all
elements of the methods described herein may be required to
implement a method in accordance with this disclosure. Although
aspects, features, and elements are described herein in particular
combinations, each aspect, feature, or element may be used
independently or in various combinations with or without other
aspects, features, and elements.
[0428] Although some embodiments herein refer to methods, it will
be appreciated by one skilled in the art that they may also be
embodied as a system or computer program product. Accordingly,
aspects of the present invention may take the form of an entirely
hardware embodiment, an entirely software embodiment (including
firmware, resident software, micro-code, etc.) or an embodiment
combining software and hardware aspects that may all generally be
referred to herein as a "processor," "device," or "system."
Furthermore, aspects of the present invention may take the form of
a computer program product embodied in one or more computer
readable mediums having computer readable program code embodied
thereon. Any combination of one or more computer readable mediums
may be utilized. The computer readable medium may be a computer
readable signal medium or a computer readable storage medium. A
computer readable storage medium may be, for example, but not
limited to, an electronic, magnetic, optical, electromagnetic,
infrared, or semiconductor system, apparatus, or device, or any
suitable combination of the foregoing. More specific examples (a
non-exhaustive list) of the computer readable storage medium
include the following: an electrical connection having one or more
wires, a portable computer diskette, a hard disk, a random access
memory (RAM), a read-only memory (ROM), an erasable programmable
read-only memory (EPROM or Flash memory), an optical fiber, a
portable compact disc read-only memory (CD-ROM), an optical storage
device, a magnetic storage device, or any suitable combination of
the foregoing. In the context of this document, a computer readable
storage medium may be any tangible medium that can contain or store
a program for use by or in connection with an instruction execution
system, apparatus, or device.
[0429] A computer readable signal medium may include a propagated
data signal with computer readable program code embodied therein,
for example, in baseband or as part of a carrier wave. Such a
propagated signal may take any of a variety of forms, including,
but not limited to, electro-magnetic, optical, or any suitable
combination thereof. A computer readable signal medium may be any
computer readable medium that is not a computer readable storage
medium and that can communicate, propagate, or transport a program
for use by or in connection with an instruction execution system,
apparatus, or device.
[0430] Program code embodied on a computer readable medium may be
transmitted using any appropriate medium, including but not limited
to CDs, DVDs, wireless, wireline, optical fiber cable, RF, etc., or
any suitable combination of the foregoing.
[0431] Computer program code for carrying out operations for
aspects of the present invention may be written in any combination
of one or more programming languages, including an object-oriented
programming language such as Java, Smalltalk, C++ or the like and
conventional procedural programming languages, such as the "C"
programming language or similar programming languages. The program
code may execute entirely on the user's computer, partly on the
user's computer, as a stand-alone software package, partly on the
user's computer and partly on a remote computer or entirely on the
remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider).
[0432] Attributes may comprise any data characteristic, category,
content, etc. that in one example may be non-quantifiable or
non-numeric. Measures may comprise quantifiable numeric values such
as sizes, amounts, degrees, etc. For example, a first column
containing the names of states may be considered an attribute
column and a second column containing the numbers of orders
received for the different states may be considered a measure
column.
[0433] Aspects of the present embodiments are described above with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems) and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer program
instructions. These computer program instructions may be provided
to a processor of a computer, such as a special purpose computer,
or other programmable data processing apparatus to produce a
machine, such that the instructions, which execute via the
processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer program instructions may also be stored in a
computer readable medium that can direct a computer, other
programmable data processing apparatus, or other devices to
function in a particular manner, such that the instructions stored
in the computer readable medium produce an article of manufacture
including instructions which implement the function/act specified
in the flowchart and/or block diagram block or blocks. The computer
program instructions may also be loaded onto a computer, other
programmable data processing apparatus, or other devices to cause a
series of operational steps to be performed on the computer, other
programmable apparatus or other devices to produce a computer
implemented process such that the instructions which execute on the
computer or other programmable apparatus provide processes for
implementing the functions/acts specified in the flowchart and/or
block diagram block or blocks. The flowcharts and block diagrams in
the figures illustrate the architecture, functionality, and
operation of possible implementations of systems, methods and
computer program products according to various embodiments of the
present invention. In this regard, each block in the flowchart or
block diagrams may represent a module, segment, or portion of code,
which comprises one or more executable instructions for
implementing the specified logical function(s). It should also be
noted that, in some alternative implementations, the functions
noted in the block may occur out of the order noted in the figures.
For example, two blocks shown in succession may, in fact, be
executed substantially concurrently, or the blocks may sometimes be
executed in the reverse order, depending upon the functionality
involved. It will also be noted that each block of the block
diagrams and/or flowchart illustration, and combinations of blocks
in the block diagrams and/or flowchart illustration, can be
implemented by special purpose hardware-based systems that perform
the specified functions or acts, or combinations of special purpose
hardware and computer instructions.
[0434] While the disclosure has been described in connection with
certain embodiments, it is to be understood that the disclosure is
not to be limited to the disclosed embodiments but, on the
contrary, is intended to cover various modifications and equivalent
arrangements included within the scope of the appended claims,
which scope is to be accorded the broadest interpretation so as to
encompass all such modifications and equivalent structures as is
permitted under the law.
* * * * *