U.S. patent application number 16/510389 was filed with the patent office on 2019-11-07 for system and method for facilitating queries via request-prediction-based temporary storage of query results.
The applicant listed for this patent is Zoomdata, Inc.. Invention is credited to Matthew Robert BARRETT, Ruhollah FARCHTCHI, Justin LANGSETH, Quan Cat LUU, Punit RATHORE.
Application Number | 20190342376 16/510389 |
Document ID | / |
Family ID | 61801342 |
Filed Date | 2019-11-07 |
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United States Patent
Application |
20190342376 |
Kind Code |
A1 |
LANGSETH; Justin ; et
al. |
November 7, 2019 |
SYSTEM AND METHOD FOR FACILITATING QUERIES VIA
REQUEST-PREDICTION-BASED TEMPORARY STORAGE OF QUERY RESULTS
Abstract
In certain embodiments, queries may be facilitated via
request-prediction-based temporary storage of query results. In
some embodiments, a request for query results may be predicted,
and, in response, a subset of results may be obtained. The subset
of results may be a portion of a set of results that would have
been obtained to respond to the request had the request been
obtained from a client device. The subset of results may be caused
to be stored in temporary data storage, and no storage of other
subsets of the set of results in the temporary data storage occurs
from the request prediction. The request may be obtained from the
client device subsequent to the storage. Based on the obtained
request, the subset of results may be obtained from the temporary
data storage, and the other subsets of the set of results may be
obtained via at least another data source.
Inventors: |
LANGSETH; Justin; (Great
Falls, VA) ; FARCHTCHI; Ruhollah; (Vienna, VA)
; LUU; Quan Cat; (Ashburn, VA) ; BARRETT; Matthew
Robert; (Catonsville, MD) ; RATHORE; Punit;
(Vienna, VA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Zoomdata, Inc. |
Reston |
VA |
US |
|
|
Family ID: |
61801342 |
Appl. No.: |
16/510389 |
Filed: |
July 12, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15855818 |
Dec 27, 2017 |
10375157 |
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16510389 |
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15382534 |
Dec 16, 2016 |
9942312 |
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15855818 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 11/3006 20130101;
G06F 11/3433 20130101; G06F 11/34 20130101; H04L 47/822 20130101;
H04L 67/1002 20130101; G06F 11/32 20130101; H04L 67/101 20130101;
G06F 2201/81 20130101; H04L 47/125 20130101 |
International
Class: |
H04L 29/08 20060101
H04L029/08; G06F 11/34 20060101 G06F011/34 |
Claims
1. A method of facilitating queries via request-prediction-based
temporary storage of query results, the method being implemented by
a computer system that comprises one or more processors executing
computer program instructions that, when executed, perform the
method, the method comprising: predicting a request for query
results; obtaining a subset of results responsive to the request
prediction, the subset of results being a portion of a set of
results that would have been obtained to respond to the request had
the request been obtained from a client device; causing the subset
of results to be stored in a temporary data storage, wherein no
storage of other subsets of the set of results in the temporary
data storage occurs from the request prediction; obtaining the
request from the client device subsequent to the storage of the
subset of results; obtaining, based on the request from the client
device, the subset of results from the temporary data storage and
the other subsets of the set of results via one or more other data
sources; and providing a presentation at the client device based on
the subset of results and the other subsets of the set of
results.
2. The method of claim 1, further comprising: performing queries
for at least some of the query results responsive to the request
prediction, the performed queries being a portion of a set of
queries that would have been performed to respond to the request
had the request been obtained from the client device, wherein
obtaining the subset of results comprises obtaining the subset of
results based on the performed queries.
3. The method of claim 2, wherein no performance of one or more
other queries of the set of queries occurs from the request
prediction.
4. The method of claim 1, wherein no obtainment of the other
subsets of the set of results occurs from the request
prediction.
5. The method of claim 1, wherein the subset of results comprises a
subset of data related to calculating a set of values, and the
other subsets of the set of results comprise other subsets of the
related data, the method further comprising: predicting the set of
values at a first time based on the subset of the related data; and
updating the predicted set of values at a second time based on the
other subsets of the related data, wherein providing the
presentation at the client device comprises: providing a
presentation of the predicted set of values that was predicted at
the first time; and updating the presentation of the predicted set
of values with a presentation of the updated predicted set of
values that was updated at the second time.
6. The method of claim 1, wherein the subset of results comprises a
subset of values of a set of values, and the other subsets of the
set of results comprise other subsets of values of the set of
values.
7. The method of claim 1, further comprising: determining frequency
information indicating a frequency of requests matching the
request, wherein the subset of results are stored in the temporary
data storage based on the frequency information, and wherein, based
on the frequency information, no storage of the other subsets of
the set of results in the temporary data storage occurs from the
request prediction.
8. The method of claim 1, further comprising: determining frequency
information indicating a frequency of requests matching the
request, wherein obtaining the subset of results comprises
obtaining the subset of results based on the frequency information,
and wherein, based on the frequency information, no obtainment of
the other subsets of the set of results occurs from the request
prediction.
9. The method of claim 1, further comprising: determining cost
information indicating a cost for storing data in the temporary
data storage, wherein the subset of results are stored in the
temporary data storage based on the cost information, and wherein,
based on the cost information, no storage of the other subsets of
the set of results in the temporary data storage occurs from the
request prediction.
10. The method of claim 1, further comprising: determining cost
information indicating a cost for storing data in the temporary
data storage, wherein obtaining the subset of results comprises
obtaining the subset of results based on the cost information, and
wherein, based on the cost information, no obtainment of the other
subsets of the set of results occurs from the request
prediction.
11. The method of claim 1, further comprising: determining
preference information indicating one or more preferences related
to a manner in which to present data, the one or more preferences
comprising a preference for a sorting order for presenting data, a
preference for an amount of data to be presented at a given time,
or a preference for a format in which to present data, wherein the
subset of results are stored in the temporary data storage based on
the preference information, and wherein, based on the preference
information, no storage of the other subsets of the set of results
in the temporary data storage occurs from the request
prediction.
12. The method of claim 1, further comprising: determining
preference information indicating one or more preferences related
to a manner in which to present data, the one or more preferences
comprising a preference for a sorting order for presenting data, a
preference for an amount of data to be presented at a given time,
or a preference for a format in which to present data, wherein
obtaining the subset of results comprises obtaining the subset of
results based on the preference information, and wherein, based on
the preference information, no obtainment of the other subsets of
the set of results occurs from the request prediction.
13. The method of claim 1, further comprising: predicting another
request for query results; determining frequency information
indicating a frequency of requests matching the other request; and
determining, based on the frequency information, not to obtain any
results related to the other request for storage in the temporary
data storage.
14. The method of claim 1, further comprising: predicting another
request for query results; determining cost information indicating
a cost for storing data in the temporary data storage; and
determining, based on the cost information, not to obtain any
results related to the other request for storage in the temporary
data storage.
15. A system for facilitating queries via request-prediction-based
temporary storage of query results, the system comprising: a
computer system comprising one or more processors executing
computer program instructions that, when executed, cause the
computer system to: predict a request for query results; obtain a
subset of results responsive to the request prediction, the subset
of results being a portion of a set of results that would have been
obtained to respond to the request had the request been obtained
from a client device; cause the subset of results to be stored in a
temporary data storage, wherein no storage of other subsets of the
set of results in the temporary data storage occurs from the
request prediction; obtain the request from the client device
subsequent to the storage of the subset of results; obtain, based
on the request from the client device, the subset of results from
the temporary data storage and the other subsets of the set of
results via one or more other data sources; and provide a
presentation at the client device based on the subset of results
and the other subsets of the set of results.
16. The system of claim 15, wherein the computer system is caused
to: perform queries for at least some of the query results
responsive to the request prediction, the performed queries being a
portion of a set of queries that would have been performed to
respond to the request had the request been obtained from the
client device, wherein obtaining the subset of results comprises
obtaining the subset of results based on the performed queries.
17. The system of claim 16, wherein no performance of one or more
other queries of the set of queries occurs from the request
prediction.
18. The system of claim 15, wherein no obtainment of the other
subsets of the set of results occurs from the request
prediction.
19. The system of claim 15, wherein the subset of results comprises
a subset of data related to calculating a set of values, and the
other subsets of the set of results comprise other subsets of the
related data, and wherein the computer system is caused to: predict
the set of values at a first time based on the subset of the
related data; and update the predicted set of values at a second
time based on the other subsets of the related data, wherein
providing the presentation at the client device comprises:
providing a presentation of the predicted set of values that was
predicted at the first time; and updating the presentation of the
predicted set of values with a presentation of the updated
predicted set of values that was updated at the second time.
20. The system of claim 15, wherein the subset of results comprises
a subset of values of a set of values, and the other subsets of the
set of results comprise other subsets of values of the set of
values.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a divisional of U.S. patent application
Ser. No. 15/855,818, filed on Dec. 27, 2017 and entitled "SYSTEM
AND METHOD FOR REDUCING DATA STREAMING AND/OR VISUALIZATION NETWORK
RESOURCE USAGE," which is a divisional of U.S. patent application
Ser. No. 15/382,534 (now U.S. Pat. No. 9,942,312), filed on Dec.
16, 2016 (issued on Apr. 10, 2018) and entitled "SYSTEM AND METHOD
FOR FACILITATING LOAD REDUCTION AT A LANDING ZONE," which both are
related to the following applications, filed on Dec. 16, 2016: (1)
U.S. patent application Ser. No. 15/382,510, (2) U.S. patent
application Ser. No. 15/382,249, (3) U.S. patent application Ser.
No. 15/382,513, and (4) U.S. patent application Ser. No.
15/382,522, each of which is hereby incorporated by reference
herein in its entirety.
FIELD OF THE INVENTION
[0002] The invention relates to facilitating queries via
request-prediction-based temporary storage of query results.
BACKGROUND OF THE INVENTION
[0003] In recent years, data visualization has become an
increasingly important part of data analysis. Visualization, for
example, enables companies and other organizations to meaningfully
present raw data to facilitate effective and efficient analysis of
the data. Obtaining and/or processing the raw data to produce a
visualization of the data, however, can be a challenge. When
visualizing "big data," for example, the costs related to obtaining
and/or processing all of the data may be substantial. While
advances in the performance of computer hardware have greatly
increased the capabilities of servers and networks to obtain and
process data as well as lowered the costs to do so, the amount of
data available to be obtained and processed has grown exponentially
in comparison to any advances in hardware performance. Moreover,
because obtaining and/or processing of such data may be time
consuming, users of typical data visualization systems generally
experience considerable delay before being provided with
substantive information related to their requests. These and other
drawbacks exist.
SUMMARY OF THE INVENTION
[0004] Aspects of the invention relate to methods, apparatuses,
and/or systems for facilitating queries via
request-prediction-based temporary storage of query results.
[0005] In some embodiments, prediction of requests and temporary
storage of query results related to the predicted requests,
selective obtainment or temporary storage of subsets of the query
results related to the predicted requests, or other techniques may
be performed. As an example, a request for query results may be
predicted, a subset of results may be obtained responsive to the
request prediction, and the subset of results may be stored at a
server cache, a web cache, memory cache, or other temporary data
storage. When the predicted request (or a future request matching
the predicted request) does occur, the subset of results may be
obtained from the temporary data storage and used to respond to the
occurred predicted request. In one use case, the subset of results
may be obtained responsive to the request prediction without
obtainment of one or more other subsets of results (relevant to the
predicted request) occurring from the request prediction. In
another use case, the subset of results may be stored at the
temporary data storage without the storage of one or more other
subsets of results (relevant to the predicted query) occurring from
the request prediction. As an example, the obtainment and/or
temporary storage of the subset of results (or the lack thereof
with respect to the other subsets of results) may be based on cost
information, frequency information, preference information, or
other information as described herein.
[0006] Various other aspects, features, and advantages of the
invention will be apparent through the detailed description of the
invention and the drawings attached hereto. It is also to be
understood that both the foregoing general description and the
following detailed description are exemplary and not restrictive of
the scope of the invention. As used in the specification and in the
claims, the singular forms of "a," "an," and "the" include plural
referents unless the context clearly dictates otherwise. In
addition, as used in the specification and the claims, the term
"or" means "and/or" unless the context clearly dictates
otherwise.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIGS. 1A and 1B show a system for facilitating data
streaming services, data visualization services, or other data
access services, in accordance with one or more embodiments.
[0008] FIG. 2 illustrates a data visualization user interface, in
accordance with one or more embodiments.
[0009] FIG. 3 shows a flowchart of a method of facilitating
reduction of network resource usage with respect to writes to a
data storage corresponding to a landing zone, in accordance with
one or more embodiments.
[0010] FIG. 4 shows a flowchart of a method of facilitating load
reduction at a landing zone, in accordance with one or more
embodiments.
[0011] FIGS. 5-7 show flowcharts of methods of facilitating queries
via request-prediction-based temporary storage of query results, in
accordance with various embodiments.
DETAILED DESCRIPTION OF THE INVENTION
[0012] In the following description, for the purposes of
explanation, numerous specific details are set forth in order to
provide a thorough understanding of the embodiments of the
invention. It will be appreciated, however, by those having skill
in the art that the embodiments of the invention may be practiced
without these specific details or with an equivalent arrangement.
In other instances, well-known structures and devices are shown in
block diagram form in order to avoid unnecessarily obscuring the
embodiments of the invention.
[0013] FIG. 1A shows a system 100 for facilitating data streaming
services, data visualization services, or other data access
services, in accordance with one or more embodiments. As shown in
FIG. 1A, system 100 may include server(s) 102, client device 104
(or client devices 104a-104n), or other components. Server 102 may
include query subsystem 112, data prediction subsystem 114, update
subsystem 116, request prediction subsystem 118, landing zone
subsystem 120, temporary storage subsystem 122, presentation
subsystem 124, or other components. Each client device 104 may
include any type of mobile terminal, fixed terminal, or other
device. By way of example, client device 104 may include a desktop
computer, a notebook computer, a tablet computer, a smartphone, a
wearable device, or other client device. Users may, for instance,
utilize one or more client devices 104 to interact with one
another, one or more servers, or other components of system
100.
[0014] FIG. 1B shows another representation of system 100, in
accordance with one or more embodiments. As shown in FIG. 1B,
system 100 may further include one or more landing zones 162 (e.g.,
landing zones 162a-162n), temporary data storage(s) 164, or other
components. It should be noted that, while one or more operations
are described herein as being performed by particular components of
server 102, those operations may, in some embodiments, be performed
by other components of server 102 or other components of system
100. As an example, while one or more operations are described
herein as being performed by components of server 102, those
operations may, in some embodiments, be performed by components of
client device 104.
[0015] As discussed, obtaining and/or processing data to produce a
visualization of the data can be a challenge. While advances in the
performance of computer hardware have greatly increased the
capabilities of servers and networks to obtain and process data as
well as lowered the cost to do so, the amount of data available to
be obtained and processed has grown exponentially in comparison to
any advances in hardware performance. Moreover, because obtaining
and/or processing of such data may be time consuming, users of
typical data visualization systems generally experience
considerable delay before being provided with substantive
information related to their requests. A number of solutions have
been implemented to address one or more of the foregoing issues,
including those described in U.S. patent application Ser. No.
14/634,680 (filed Feb. 27, 2015, entitled "Prioritized Retrieval
and/or Processing of Data," and issued as U.S. Pat. No. 9,251,276
on Feb. 2, 2016) and U.S. patent application Ser. No. 14/698,421
(filed Apr. 28, 2015, entitled "Prioritized Execution of Plans for
Obtaining and/or Processing Data," and issued as U.S. Pat. No.
9,389,909 on Jul. 12, 2016), each of which is incorporated herein
by reference in its entirety.
[0016] In some embodiments, system 100 may facilitate the reduction
of network resource usage, for example, via an architecture
designed to provide a lower frequency of updates to a set of
results stored at a given data storage corresponding to a landing
zone (e.g., via which a client device obtains the updated set of
results or interacts to have its requests related to the updated
set of results handled) or via other techniques. As an example,
predicted or other calculated values, representations of those
calculated values, raw data for calculating those values, or other
results (of the set of results) stored at the data storage
(corresponding to the landing zone) may be updated less frequency
than a frequency at which the set of results stored at another data
storage is updated. In some embodiments, raw data relevant to a
client-initiated query may be continuously streamed and processed
to calculate updated results, and the updated results may be used
to update a prior-calculated version of such results stored at a
first data storage. The updated results stored at the first data
storage may be used to update a prior-calculated version of such
results stored at one or more other data storages (corresponding to
one or more landing zones) such that the set of results stored at
each of the other data storages is updated less frequently than the
corresponding set of results stored at the first data storage. In
this way, for example, the different frequencies at which the sets
of results are updated at the respective data storages may reduce
the cost of writing to a given data storage (e.g., the other data
storages corresponding to a landing zone) via which such results
are obtained to respond to a client request, while still allowing
for the results stored on at least one data storage to be updated
at a faster rate based on further data being obtained in a
continuous fashion.
[0017] In some embodiments, system 100 may facilitate reduction of
load at a landing zone and/or improve efficiency of request
handling, for example, via dynamic assignment of one or more
landing zones to handle specific requests (e.g., responsive to
request predictions or other triggers), via dynamic configuration
of client devices to cause the client devices to redirect their
requests and related subsequent requests to the respective assigned
landing zones, or via other techniques. As an example, the landing
zones may be assigned to handle specific requests based on
parameters of the request, such as search parameters of a query
(e.g., keywords for such search, attributes for which values are
sought, etc.), attributes (or their values) that are to be updated,
or other request parameters. If, for example, a requested query
(obtained at a first landing zone) from a client device matches one
of the queries to which a second landing zone is assigned to handle
(e.g., responsive to a prediction of the matching query or other
triggers), the client device may be caused to redirect the query
request to the second landing zone. Moreover, in some cases, the
client device may additionally be caused to transmit its subsequent
requests (for an update related to the requested query) to the
second landing zone without first transmitting the subsequent
requests to the first landing zone. As such, computer resources
usage and delay may be reduced, for example, by avoiding the
initial transmission of the subsequent requests to the first
landing zone, avoiding additional operations needed to cause the
client device to direct those subsequent requests following the
initial transmission to the first landing zone, etc. In addition,
because landing zones may be assigned to handle specific requests
(e.g., those having a certain set of search parameters), system 100
may improve the efficiency of handling such requests in some
scenarios (e.g., based on temporary storage and/or caching
techniques described herein or other techniques).
[0018] In some embodiments, system 100 may facilitate reduction of
delay for providing a sufficient response to a request and/or
improve efficiency of temporary data storage or other computer
resource usage, for example, via prediction of requests and
temporary storage of query results related to the predicted
requests, via selective obtainment or temporary storage of subsets
of the query results related to the predicted requests, or other
techniques. As an example, a request for query results may be
predicted, a subset of results may be obtained responsive to the
request prediction, and the subset of results may be stored at a
server cache, a web cache, memory cache, or other temporary data
storage. When the predicted request (or a future request matching
the predicted request) does occur, the subset of results may be
obtained from the temporary data storage (e.g., in lieu of having
to obtain the subset of results through other data storage with
significantly greater delay) and used to respond to the occurred
predicted request. In one use case, the subset of results may be
obtained responsive to the request prediction without obtainment of
one or more other subsets of results (relevant to the predicted
request) occurring from the request prediction (e.g., no queries
for the other subsets of results may be executed, queries being
performed may be stopped before the other subsets of results are
obtained, etc.). In another use case, the subset of results may be
stored at the temporary data storage without the storage of one or
more other subsets of results (relevant to the predicted query)
occurring from the request prediction (e.g., no caching of the
other subsets of results may be triggered by the request
prediction). As an example, the obtainment and/or temporary storage
of the subset of results (or the lack thereof with respect to the
other subsets of results) may be based on cost information,
frequency information, preference information, or other information
as described herein. In this way, for example, the selectiveness of
the obtainment and/or temporary storage of results (prior to
particular requests occurring) may significantly decrease latency
or other delays for sufficiently responding to requests and improve
efficiency of temporary data storage or other computer resource
usage.
[0019] Prediction of Values Based on a Data Subset
[0020] In some embodiments, query subsystem 112 may obtain data
related to calculating a set of values. Data prediction subsystem
114 may predict the set of values based on a subset of the related
data. As an example, the set of values may be associated with one
or more attributes. A request for those values may be obtained
(e.g., from a client device or other device), and an initial query
(or an initial set of "micro-queries") may be performed based on
the request to obtain an initial subset of data related to
calculating values associated with the attributes. The associated
values may then be predicted based on the initial data subset. As
an example, a value may be associated with one or more attributes
(e.g., an object, an entity, a property, a characteristic, etc.).
With respect to FIG. 2, for example, values represented by data
elements 206a-206y may each be associated with at least one
attribute represented in sections 202a-202e, at least one attribute
represented in sections 204a-204e, and at least one attribute
represented in sections 208a-208e.
[0021] In some embodiments, the predicted set of values may include
approximations of what the values of the set of values would be had
a complete set of data corresponding to the set of values been used
to calculate the values of the set of values. In one use case, for
instance, micro-query delegation may be performed to enable faster
real-time results of large streaming data by performing numerous
micro-queries on a distributed data store. An approximation of the
final data values may be performed based on results from a subset
of the micro-queries by extrapolating from the resulting data that
has been processed thus far to estimate the final data values
before all of the micro-queries are completed. Data elements may be
generated based on the approximated data values to present users
with approximated visualized information without having to wait for
all of the micro-queries to be completed. In this way, among other
benefits, user experience may be improved by providing users with
visualized information representing final data values without
having to wait for all of the results from the micro-queries to be
obtained by predicting what the final data values will likely be
based on results from a subset of the micro-queries that have been
obtained. Upon receipt of further data from the micro-queries, an
updated approximation of the final data values may be performed and
then utilized to generate updated data elements to replace the
previous version of the approximated data elements, thereby
"sharpening" the data elements.
[0022] In some embodiments, the predicted values may be utilized to
prioritize retrieval and/or processing of data related to one
subset of the attributes over retrieval and/or processing of data
related to other subsets of the attributes, for example, as
described in U.S. patent application Ser. No. 14/634,680, filed
Feb. 27, 2015 and entitled "Prioritized Retrieval and/or Processing
of Data," and issued as U.S. Pat. No. 9,251,276 on Feb. 2, 2016,
which is incorporated herein by reference in its entirety. In one
use case, for example, priority may be assigned to a particular
subset of attributes when the predicted values associated with the
subset of the attributes are greater than the predicted values
associated with other subsets of the attributes (e.g., if the
attributes and their associated values are to be presented in
descending order). In another use case, priority may be assigned to
a particular subset of attributes when the predicted values
associated with the subset of the attributes are less than the
predicted values associated with other subsets of the attributes
(e.g., if the attributes and their associated values are to be
presented in ascending order). In some embodiments, an "attribute"
may comprise an object, an entity, a property, a characteristic,
etc., of the entity-attribute-value model (which may also be
referred to as the "object-attribute-value model") or other data
model. As an example, with respect to the entity-attribute-value
model, a value associated with two attributes may comprise a value
associated with a characteristic of an entity where the
characteristic is a first attribute and the entity is a second
attribute.
[0023] Presentation subsystem 124 may effectuate presentation of a
data visualization user interface (e.g., at client devices 104).
The data visualization user interface may be programmed to obtain
user entry and/or selection of a first attribute group (associated
with first attributes), a second attribute group (associated with
second attributes), and/or other information. The user entry and/or
selection may correspond to a request for values. The data
visualization user interface may be programmed to present
(predicted, updated, and/or calculated) values associated with a
set of attributes (or a representation thereof) based on the
request.
[0024] In some embodiments, a data visualization user interface
effectuated by presentation subsystem 124 may be in the form of a
pivot table (or other display style). The data visualization user
interface may include rows, columns, data elements, other portions,
and/or other interface elements. The rows and columns may represent
respective ones of at least a first set of attributes (or first
attributes) and a second set of attributes (or second attributes).
Data elements may individually represent a value associated with a
respective one of the first attributes and a respective one of the
second attributes. For example, a data visualization user interface
may include a construction of an N by M matrix where N represents a
series of first attributes in rows and M represents a series of
second attributes as columns. The intersection of N and M may
represent the data elements representing the values associated with
the first and second attributes. With respect to FIG. 2, for
example, user interface 200 may be such a data visualization user
interface. It is noted that the depiction of user interface 200 and
accompanying descriptions are intended for illustrative purposes
only. For example, in some embodiments, the procedures and/or
processes described in connection with user interface 200 may
instead be performed without the presentation of values to a user.
Instead, for instance, predicted values may simply be stored in a
database for later use, placed into a queue for further processing,
etc. In some embodiments, other data visualization user interfaces
(e.g., effectuated by presentation subsystem 124 or other
components) may be provided.
[0025] As an example, in FIG. 2, portion 210 may represent a first
attribute group (e.g., State, Gender, Zip Code, City, Income,
Product Category, or other attribute group). Portion 202 may
include sections 202a-202e that may represent at least a subset of
attributes (e.g., Alabama, Alaska, Arizona, and/or other states;
Male, Female, or other characteristic; 92007, 92014, 92024, and/or
other zip codes; 0-$25 k, $25 k-$50 k, and/or other income or
range; Electronics, Books, Jewelry, Accessories, Arts, Audio,
Men's, Women's, and/or other product categories; and/or other
attributes) included in the first attribute group. Portion 210 may
include a first control element 211. The first control element 211
may facilitate setting of a sorting preference for the attributes
included in the first attribute group (e.g., thereby affecting the
order in which the attributes may be presented). Sorting may be
based on a description (e.g., name, numerical range, and/or other
description) of respective ones of the attributes of the first
attribute group, the values associated with the attributes of the
first attribute group, or other criteria. For example, the first
control element 211 may be used to sort the attributes of the first
attributes group in ascending order based on their respective
values, descending order based on their respective values,
alphabetically based on the names of the first attributes, etc. As
an example, the sorting may modify the subset of attributes that
are displayed in sections 202a-202e.
[0026] In FIG. 2, portion 212 may represent a second attribute
group (e.g., State, Gender, Zip Code, City, Income, Product
Category, and/or other attribute group). Portion 204 may include
sections 204a-204e that may represent a subset of attributes (e.g.,
Alabama, Alaska, Arizona, and/or other states; Male or Female;
92007, 92014, 92024, and/or other zip codes; $0-$25 k, $25 k-$50 k,
and/or other incomes or ranges; Electronics, Books, Jewelry,
Accessories, Arts, Audio, Men's, Women's, and/or other product
categories; and/or other attributes) included in the second
attribute group. Portion 212 may include a second control element
213. The second control element 213 may facilitate setting a
sorting preference for the attributes included in the second
attribute group. The sorting may, for example, affect the
attributes of the second attribute group that may be included in
the subset of attributes displayed in sections 204a-204e.
[0027] Portion 206 may include data elements 206a-206y that may
represent at least some values associated with the attributes of
the first and/or second attribute groups. The values conveyed by
the data elements 206a-206y may be associated with respective ones
of the attributes of the first attribute group in sections
202a-202e and respective ones of the attributes of the second
attribute group in sections 204a-204e. For example, data element
206a may be associated with a first attribute in section 202a and a
second attribute in section 204a.
[0028] Control elements 224, 226, and/or 228 may facilitate user
modifications to user interface 200. As an example, control element
224 may correspond to configuring and/or setting the attribute
groups represented by the columns and rows of the pivot table.
Control element 226 may be selected to apply filters to explore
other available data elements 206a-206y that may not currently be
in the viewable area of portion 206, or may initiate requests for
other values. Control element 228 may be selected to change the
display style for depicting the attribute groups, attributes,
values, and/or other information. Display styles may comprise a
pivot table, a bar chart, a bubble graph, a pie chart, a scatter
plot, a word cloud, a zoomable map, and/or other di splay
style.
[0029] Still referring to FIG. 2, portion 214 may include a
pause/play button 216 for pausing or playing the data
visualization, an indicator 218 specifying that the data
visualization is live data, and a sliding-based control element 220
having a movable control element 222 for rewinding to visualize
historical data, fast-forwarding back to visualize live data,
fast-forwarding to visualize predictions of future data, etc. As
shown by indicator 218, the data visualization that is depicted in
portion 206 may represent live data. A user may, for example, slide
movable control element 222 to the left on sliding-based control
element 220 to switch the data visualization of the live data to a
data visualization of historical data.
[0030] In one scenario, with respect to FIG. 2, data elements
206a-206y may represent values associated with attributes displayed
in sections 202a-202e (and/or with value types displayed in section
208a-208e). The associated values may, for example, include the
most up-to-date values for a certain time period thus far (e.g., a
current day, week, month, year, etc.). Upon the moving of movable
control element 222 toward the left along sliding-based control
element 220 onto a point associated with a particular time, a
request for data elements associated with the displayed attribute
(with respect to the particular time and/or time period) may be
initiated. As an example, responsive to the request, a historical
data store may be determined and used to obtain data necessary for
determining values that may be represented by the data elements
206a-206y.
[0031] Returning to FIG. 1A, based on the selection of a first
and/or a second attribute group, the query subsystem 112 may
perform queries for data relating to attributes of the first
attribute group, attributes of the second attribute group, and/or
other attributes. The queries may be performed across a plurality
of data sources. The queries may be performed over one or more
query intervals (e.g., based on data sources, time periods,
geographic areas, etc.). As an example, responsive to a user
request for a data visualization of overall sales volume of various
product categories during a particular year, at least first and
second sets of queries may be performed (where each set comprises
one or more queries). The first and second sets of queries may be
performed in series or in parallel. When data from one set of
queries is obtained, the obtained data may be processed to predict
the values associated with the overall sales volume of the various
product categories during the requested year without having to wait
for data from the other set(s) of queries to be obtained. When data
from at least one remaining set of queries is obtained, this data
may then be processed to update the predicted values (e.g., if not
all of the queries have returned data) and/or calculate the final
values associated with the overall sales volume of the various
product categories during the requested year (e.g., if data from
all of the queries have been obtained).
[0032] In one use case, the first set of queries may comprise one
or more queries to a first subset of the relevant data sources to
be searched for the requested data, and the second set of queries
may comprise one or more queries to a second subset of the relevant
data sources for the requested data. In another use case, the first
set of queries may comprise one or more queries for such data
related to one or more time periods during the requested year, and
the second set of queries may comprise one or more queries for such
data related to one or more other time periods during the requested
year. In yet another use case, the first set of queries may
comprise one or more queries for such data related to one or more
geographic areas (e.g., a subset of zip codes per state, a subset
of cities per state, a subset of regions per country, etc.), and
the second set of queries may comprise one or more queries for such
data related to one or more other geographic areas.
[0033] In some embodiments, data prediction subsystem 114 may
predict values associated with a set of attributes based on a
subset of data obtained via an initial query that corresponds to an
initial query interval (e.g., a subset of relevant data sources, a
subset of a requested time period, a subset of a requested
geographic area, etc.). In one scenario, with respect to FIG. 2, a
first attribute group "Product Category" may include attributes
such as Books, Electronics, Jewelry, Men, Women, Toys, Pet
products, and/or other attributes. A second attribute group "User
Income" may include attributes such as $0 to $25,000, $25,000 to
$50,000, $50,000 or more, and/or other attributes. A third
attribute group "Sale Numbers" may include sales volume and/or
other attributes. As an example, Table 1 below may represent
predicted values associated with sale volumes (e.g., by unit),
respective ones of the "Product Category" attributes (e.g., rows),
and respective ones of the "User Income" attributes (e.g., columns)
that may be streamed in real-time.
TABLE-US-00001 TABLE 1 User Income $0 to $25,000 $25,000-$50,000
$50,000 or more Product Category Volume Volume Volume Books
107,460,342 107,476,521 71,653,491 Electronics 80,622,191
80,610,360 53,752,435 Jewelry 67,181,209 67,180,592 44,799,309 Men
26,858,564 26,871,587 17,906,847 Women 26,874,505 26,877,982
17,917,377
[0034] The values in Table 1 may be predicted sales volumes of the
respective "Product Category" and "User Income" attributes for a
certain time period (e.g., current day, week, month, year, etc.).
The predicted values may represent a first prediction based on a
first subset of data obtained from a first query. For example, the
above values may represent a prediction of sales volumes for a
1-hour period of a given day and/or other time period. From the
first prediction, it may be determined that Books, Electronics,
Jewelry, Men, and Women product categories occupy the top five
highest sales volumes with regard to all product categories. For
example, in Table 1, the highest predicted sales volume on a
product category basis for users in the income range of $0 to
$25,000 is Books at 107,460,342 units. Such values may be reflected
by data elements 206a-206y in the pivot table of user interface
200. The values may be updated continuously to represent the most
up-to-date prediction of sales volume values for each of the
attributes as more queries for data are performed.
[0035] In one use case, retrieval and/or processing of data related
to one subset of the attributes over retrieval and/or processing of
data related to one or more other subsets of the attributes may be
performed. Prioritizing retrieval and/or processing may be based on
a comparison of the predicted values with one another (e.g., based
on highest-to-lowest sorting, lowest to highest sorting, and/or
other considerations). With respect to FIG. 2, for example, the
retrieval and/or processing of data related to the attributes in
sections 202a-202e may be prioritized over attributes not included
in the sections 202a-202e (e.g., the "Product Category" attributes
not within the "top five" list). In the scenario of Table 1, the
retrieval and/or processing of data related to Books, Electronics,
Jewelry, Men, and Women may be prioritized over retrieval and/or
processing of data related to Toys, Pet products, and/or other
"Product Category" attributes.
[0036] Updating of Calculated Values Stored at Respective Data
Storages
[0037] In some embodiments, update subsystem 116 may cause one or
more calculated values to be stored on one or more data storages.
Upon obtaining an updated version of a calculated value, update
subsystem 116 may store the updated version of the calculated value
on the data storages. As an example, update subsystem 116 may cause
the updated version of the calculated value to replace a prior
version of the calculated value. In one scenario, for instance, the
calculated values may include one or more predicted values that are
approximations of what the values would be (had a complete set of
data corresponding to the values been used to calculate the
values). Update subsystem 116 may update a stored prior version of
the predicted values by replacing the stored prior version with an
updated version of the predicted values. In a further scenario,
update subsystem 116 may continuously update the stored predicted
values.
[0038] In some embodiments, update subsystem 116 may cause a
calculated set of values to be stored at multiple data storages,
where at least one of the data storages corresponds to a landing
zone. As an example, the landing zone may be the corresponding data
storage, a server (e.g., a physical server, a virtual server,
etc.), an area (e.g., a web page, a node, etc.) hosted at the
server, or other component via which a client device obtains data
(e.g., at least some values of the calculated set of values, raw
query results, or other data) or via which the client device
interacts to have its requests handled. As another example, the
data storages may be server data storages, such as areas on one or
more hard drives or other electronic storages (e.g., server cache,
web cache, other temporary data storage, or other storage) hosted
at a server, databases associated with a server, or other server
data storages. The server data storages may be associated with the
same server or different servers. It should be noted that, although
some embodiments are described herein with respect to predicted
values stored at multiple data storages and/or the updating of
those predicted values stored at the respective data storages, it
is understood that other types of calculated values (e.g., values
calculated based on complete sets of data corresponding to the
respective values) may be stored at the multiple data storages and
updating of the other types of calculated values (stored at the
respective data storages) may be performed via various techniques
described herein with respect to predicted values.
[0039] In some embodiments, upon obtaining a subset of data related
to calculating a set of values, the set of values may be predicted
based on the subset of the related data and stored on a first data
storage and a second data storage, where at least the second data
storage corresponds to a landing zone via which a client device
obtains data (e.g., at least some values of the predicted set of
values, raw query results, or other data) or via which the client
device interacts to have its requests handled. Update subsystem 116
may update the predicted set of values (stored at the first data
storage) based on further subsets of the related data. As an
example, update subsystem 116 may continuously update the predicted
set of values (stored at the first data storage) based on the
further subsets of the related data during a given time period such
that the predicted set of values (stored at the first data storage)
is updated at least a first number of times within the time period.
In one use case, for instance, the continuous updating during the
time period may include periodically updating the predicted set of
values (stored at the first data storage during the time period).
In another use case, the continuous updating (during the time
period) may include updating the predicted set of values (stored at
the first data storage) in real-time during the time period as the
further subsets of the related data are being obtained.
[0040] In some embodiments, update subsystem 116 may update the
predicted set of values (stored at the second data storage
corresponding to a landing zone) based on the predicted set of
values stored at the first data storage. As an example, update
subsystem 116 may continuously update the predicted set of values
(stored at the second data storage) such that the predicted set of
values (stored at the second data storage) is updated less than the
first number of times (that the predicted set of values stored at
the first data storage is updated) within the time period. In some
embodiments, the continuous updating of the predicted set of values
stored at the second data storage may include periodically
transmitting a version of the predicted set of values stored at the
first data storage to the second data storage (e.g., every 5
seconds, every minute, or other rate) to replace a version of the
predicted set of values stored at the second data storage. Each of
the periodically transmitted versions may, for instance, be more
recently updated than the respective replaced version of the
predicted set of values stored at the second data storage.
[0041] In one use case, for example, approximations of the overall
jewelry sales (in the United States for a given year) may be
obtained from the second data storage (to which a landing zone for
a client device corresponds) and provided to the client device to
respond to the client device's request for the overall jewelry
sales. Although data relevant to the overall jewelry sales (in the
United States for a given year) may be continuously streamed and
processed to calculate updated approximations of the actual overall
jewelry sales every second (or based on another time interval), the
approximations stored at the second data storage may only be
updated with the most recent updated approximations every five
seconds (or based on another other time interval greater than the
frequency in which the approximations available from another data
storage, such as the first data storage, are updated). In this way,
for example, the different frequencies at which the approximations
are updated at the respective data storages may reduce the cost of
writing to a given data storage (e.g., the second data storage)
from which the approximations are obtained to respond to a client
request, while still allowing for the approximations to be updated
and stored (on another component such as the first data storage) at
a faster rate. As an example, if the cost of each write to the
first data storage for each set of approximations is one unit, and
the cost of each write to the second data storage for each set of
approximations is 100 units (e.g., in monetary or computer
resources costs), then the cost savings of updating each set of
approximations at the second data storage every five seconds (as
opposed to every second) is about 7 million units per day (i.e., 24
hours/day*60 minutes/hour*60 seconds/minute*(4/5 in savings for
each update)*100 units). As another example, when this cost savings
is extrapolated out to 20,000 different requested sets of
approximations, the cost savings is about 140 billion units per day
(or about 50 trillion units per year).
[0042] In some embodiments, update subsystem 116 may determine an
update rate for updating a predicted set of values stored at a
given data storage. In some embodiments, the update rate may be
based on cost information, such as information indicating one or
more costs associated with writing to the data storage or other
information. As an example, an update rate for updating the
predicted set of values stored at a first data storage may be a
first update rate, and an update rate for updating the predicted
set of values stored at a second data storage (e.g., to which a
landing zone for client requests may correspond) may be a second
update rate. In one scenario, the first update rate may be
determined (as an update rate for updating the predicted set of
values to the first data storage) based on the costs associated
with writing to the first data storage. In another scenario, the
second update rate may be determined (as an update rate for
updating the predicted set of values to the second data storage)
based on the costs associated with writing to the second data
storage. Such associated costs may, for instance, comprise a
monetary cost for writing to the respective data storage, a
computer resource cost for writing to the respective data storage
(e.g., bandwidth or other network resource usage amount or other
computer resource cost), or other costs.
[0043] In some embodiments, landing zone subsystem 120 may obtain
compatibility information associated with a client device. Landing
zone subsystem 120 may select, based on the compatibility
information, a given data storage from a plurality of data storages
is a data storage for storage of versions of calculated values
requested by the client device. As an example, a predicted set of
values stored at a first data storage may be continuously updated
at a first update rate, and the predicted set of value stored at a
second data storage may be continuously updated at a second update
rate. Landing zone subsystem 120 may select the second data storage
as a data storage for storing the predicted set of values based on
compatibility information associated with the client device
(requesting the set of values) indicating that the client device is
compatible with the second data storage. In one scenario, for
instance, the compatibility information may comprise information
indicating one or more database management systems with which the
client device is compatible or other information. The second data
storage may be selected (for storage of versions of the predicted
set of values) based on a determination that the second data
storage uses at least one of the compatible database management
systems (indicated by the compatibility information).
[0044] Landing Zone Assignments
[0045] In some embodiments, one or more landing zones may be
assigned to handle requests, such as a query (or a submission of
such query), an update request related to the query, a request to
perform one or more other actions (e.g., send a message, upload or
publish content, etc.), or other request. A landing zone may, for
instance, include a data storage (e.g., a database or other data
storage), a server (e.g., a physical server, a virtual server,
etc.), an area (e.g., a web page, a node, etc.) hosted at the
server, or other component via which a client device obtains data
or interacts to have its requests handled. As an example, the
landing zones may be assigned based on geographic region, carrier
(e.g., Internet Service Providers or other carriers), device type
(e.g., mobile devices, iOS devices, Android devices, or other
device types), IP address, MAC address, or other criteria. As
another example, the landing zones may be assigned to handle
specific requests based on parameters of the requests, such as
search parameters of a query (e.g., keywords for such search,
attributes for which values are sought, etc.), attributes (or their
values) that are to be updated, or other request parameters.
[0046] In some embodiments, a client device may be caused to
redirect a request (that the client device initially provided to
one landing zone) to another landing zone based on geographic
region, carrier, device type, IP address, MAC address, or other
criteria. As an example, if a request originating from a particular
geographic region is obtained at one landing zone, a client device
(from which the request was obtained) may be redirected to another
landing zone assigned to handle requests originating from the
geographic region. As another example, if a request originating
from a client device (associated with a particular carrier) is
obtained at one landing zone, the client device may be redirected
to another landing zone assigned to handle requests originating
from client devices associated with the carrier. As another
example, if a request originating from a client device (of a
particular device type) is obtained at one landing zone, the client
device may be redirected to another landing zone assigned to handle
requests originating from client devices of the device type. As
another example, if a request originating from a client device
associated with an address (e.g., IP address, MAC address, etc.,
with a particular address range) is obtained at one landing zone,
the client device may be redirected to another landing zone
assigned to handle requests originating from client devices
associated with addresses having the address range. As yet another
example, if a request with a particular set of request parameters
is obtained at one landing zone, the client device may be
redirected to another landing zone assigned to handle requests with
the same or similar sets of request parameters.
[0047] In some embodiments, a landing zone may be assigned to
handle one or more specific predicted requests (or requests that
are the same or similar to such predicted requests). In some
embodiments, request prediction subsystem 118 may predict a request
related to a client-initiated query prior to the query-related
request being obtained from a client device at a first landing
zone. Landing zone subsystem 120 may assign one or more other
landing zones to handle responding to the query-related request
such that, when the query-related request is obtained from the
client device at the first landing zone, the client device may be
caused to redirect the query-related request to at least one of the
other landing zones. As an example, the query-related request may
comprise a query submission related to the client-initiated query,
an update request related to the client-initiated query, or other
request. In some embodiments, the first landing zone may be a
default landing zone, and the other landing zones may each be a
query-specific landing zone. In this way, for example, system 100
may reduce the load on a given landing zone by assigning other
landing zones to handle respective sets of requests and causing
client devices to redirect their requests (that they initially
transmitted to the given landing zone) to the other landing zones
respectively assigned to handle their requests. For example, with
respect to the "micro-queries" and "data sharpening" context
described herein, a single client device may initiate (for each
client-initiated query) numerous requests to a landing zone for new
updates to approximated results. If, for instance, the client
device initiates a request every five seconds, this would be over
17,000 requests obtained from the client device alone per day. When
thousands of client devices (or hundreds of thousands or millions
of client devices) are each initiating numerous requests to a
single landing zone, the landing zone may become overloaded and
unable to handle those requests. Thus, the assignment of other
landing zones to handle respective requests and the redirection
based thereon may avoid the overloading of any given landing zone
(e.g., a default landing zone or other landing zone). In addition,
because landing zones may be assigned to handle specific requests
(e.g., those having a certain set of search parameters), system 100
may improve the efficiency of handling such requests in some
scenarios (e.g., based on temporary storage and/or caching
techniques described herein or other techniques).
[0048] In some embodiments, request prediction subsystem 118 may
determine request history information comprising information
indicating one or more prior queries, information indicating
respective frequencies of requests (e.g., a frequency of each of
the prior queries, update requests related to the prior queries,
etc.), information regarding users or client devices that initiated
prior requests, or other information. Request prediction subsystem
118 may predict one or more requests to occur in the future based
on the request history information (e.g., the prior query
indications, the frequency indications, the user and/or client
device information, etc.).
[0049] As an example, based on obtaining an initial query
submission from a client device, request prediction subsystem 118
may determine that it is likely that an update request related to
the initial query submission will be obtained (e.g., from the
client device or other component). In one use case, with respect to
applications configured for "data sharpening" based on
micro-queries, an approximation of the final data values (relevant
to the client query) may be performed based on results from a
subset of the micro-queries by extrapolating from the resulting
data that has been processed thus far to estimate the final data
values before all of the micro-queries are completed. Data elements
may be generated based on the approximated data values to present
users with approximated visualized information without having to
wait for all of the micro-queries to be completed. If such
applications are configured to automatically update the generated
data elements based on an updated approximation of the final data
values (e.g., to "sharpen" the data visualization when further data
from the micro-queries are obtained), request prediction subsystem
118 may predict that the update request (related to the initial
query submission) will be obtained in the near future (e.g., based
on the known frequencies of such updates or other information).
[0050] As another example, a request including one or more
parameters may be predicted to occur in the future based on the
frequency of prior requests that include those parameters. The
request parameters may include search parameters of a query (e.g.,
keywords for such search, attributes for which values are sought,
etc.), attributes (or their values) that are to be updated, or
other request parameters. In one use case, requests that include
search parameters A, B, and C (e.g., requests with search
parameters A, B, and C as well as other search parameters, where A,
B, and C are "women's watches," "year to date," "United States," or
other search parameters) may occur at a frequency rate that
satisfies a certainty threshold indicating a high likelihood of
such a request occurring again within a predetermined time period
(e.g., in next the few seconds, in the next few minutes, in the
next few hours, or other predetermined time period). Based on this
frequency rate, request prediction subsystem 118 may predict that a
request including the search parameters A, B, and C will occur
within the predetermined time period.
[0051] In some embodiments, subsequent to a request related to a
client-initiated query being predicted and a second landing zone
being assigned to handle such predicted request, the query-related
request may be obtained at a first landing zone from a client
device. Based on the assignment to the second landing zone, landing
zone subsystem 120 may cause the client device to redirect the
query-related request to the second landing zone such that the
client device transmits the query-related request, subsequent
requests (for an update related to the client-initiated query), or
further requests to the second landing zone without first
transmitting the subsequent requests or the further requests to the
first landing zone. In some embodiments, landing zone subsystem 120
may provide a redirection instruction, a reference associated with
the second landing zone (e.g., an identifier or address associated
with the second landing zone), or other information to the client
device. The providing of such information causes the client device
to transmit the query-related request, the subsequent requests, or
the further requests to the second landing zone without first
transmitting the subsequent requests or the further requests to the
first landing zone. As an example, responsive to being provided the
redirection instruction and the second landing zone reference, the
client device may process the redirection instruction and use the
second landing zone reference to transmit the query-related request
and the subsequent requests to the second landing zone (e.g.,
without first transmitting the subsequent requests to the first
landing zone). As another example, the processing of the
redirection instruction may cause the client device to modify one
or more of its configuration settings such that the client device
transmits the query-related request and the subsequent requests to
the second landing zone without first transmitting the subsequent
requests to the first landing zone.
[0052] In one scenario, with respect to FIG. 1B, responsive to a
prediction that a request will occur in the future, landing zone
162b may be assigned to handle requests matching the predicted
request. As indicated in FIG. 1B, landing zone 162a may obtain a
request from client device 104a. If, for example, landing zone
subsystem 120 determines that the obtained request matches the
predicted request, landing zone subsystem 120 may provide client
device 104a with a redirection instruction and an address
associated with landing zone 162b, where the redirection
instruction indicates that client device 104a should redirect its
request to landing zone 162b using the associated address. Based on
the redirection instruction and the associated address, client
device 104a may redirect its request to landing zone 162b. In a
further scenario, the redirection instruction may indicate to
client device 104a that it should transmit subsequent requests
related to its redirected request (e.g., subsequent update requests
or other requests) to landing zone 162b without first transmitting
the subsequent requests to landing zone 162a. The processing of the
redirection instruction may, for instance, cause client device 104a
to modify one or more of its configuration settings such that
client device 104a transmits its request and the related further
requests to landing zone 162b without first transmitting the
related further requests to landing zone 162a.
[0053] In another scenario, where the redirected request of client
device 104a is a first client-initiated query, landing zone 162a
may obtain a subsequent request related to a second
client-initiated query. If, for example, landing zone subsystem 120
determines that the obtained subsequent request (related to the
second client-initiated query) matches the earlier predicted
request, landing zone subsystem 120 may provide client device 104a
with a redirection instruction and the address associated with
landing zone 162b, where the redirection instruction indicates that
client device 104a should redirect its request to landing zone 162b
using the associated address. Based on the redirection instruction
in the associated address client device 104a may redirect its
request to landing zone 162b. In a further scenario, the
redirection instruction may indicate to client device 104a that it
should transmit further requests related to the redirected request
(related to the second client-initiated query) to landing zone 162b
without first transmitting the further requests to landing zone
162a.
[0054] In another scenario, landing zone 162a may obtain a request
from client device 104b. If, for example, landing zone subsystem
120 determines that the request (obtained from client device 104b)
matches the earlier predicted request, landing zone subsystem 120
may also provide client device 104b with a redirection instruction
and the address associated with landing zone 162b where the
redirection address indicates that client device 104b should
redirect its request to landing zone 162b using the associated
address. Like client device 104a, client device 104b may redirect
its request to landing zone 162b based on the redirection
instruction in the associated address. In a further scenario, like
with client device 104a, the redirection instruction may indicate
to client device 104b that it should transmit subsequent requests
relating to its redirected request to landing zone 162b without
first transmitting the subsequent requests to landing zone
162a.
[0055] In yet another scenario, if landing zone 162a obtains a
request from client device 104a, and landing zone subsystem 120
determines that the request (obtained from client device 104a) does
not match any earlier predicted request (assigned to some landing
zone to handle), landing zone subsystem 120 may not provide a
redirection instruction to client device 104a to cause client
device 104a to redirect its request to another landing zone. As an
example, if the parameters of the obtained request do not match any
of the respective sets of request parameters assigned to another
landing zone to handle, no redirection instruction may be provided
to client device 104 to cause client device 104 to redirect its
request to another landing zone. As a further example, the search
parameters of the obtained request do not include at least one
predefined set of search parameters assigned to another landing
zone to handle, no redirection instruction may be provided to
client device 104 to cause client device 104 to redirect its
request to another landing zone.
[0056] In some embodiments, multiple requests may be predicted to
occur in the future, and each of the requests may subsequently be
obtained at a first landing zone (e.g., landing zone 162a). A
second landing zone (e.g., landing zone 162b) may be assigned to
handle responding to one of the predicted requests, a third landing
zone (e.g., landing zone 162n) may be assigned to handle responding
to another one of the predicted requests, and so on. In one use
case, when a given query request is obtained at the first landing
zone from a first client device (e.g., client device 104a), landing
zone subsystem 120 may determine that the query request matches a
predicted request for which the second landing zone is assigned to
handle. Responsive to such determination, landing zone subsystem
120 may cause the first client device to redirect its query
request, subsequent requests for an update related to the requested
query, or other requests related to the requested query to the
second landing zone. As an example, landing zone subsystem 120 may
cause the first client device to transmit the query request, the
subsequent requests, or the other requests to second landing zone
without first transmitting the subsequent requests or the other
requests to the first landing zone.
[0057] In a further use case, when another query request is
obtained at the first landing zone from a client device (e.g.,
client device 104a, client device 104b, etc.), landing zone
subsystem 120 may determine that the query request matches a
predicted request for which the third landing zone is assigned to
handle. Responsive to such determination, landing zone subsystem
120 may cause the client device to redirect this other query
request, subsequent requests for an update related to the other
requested query, or other requests related to the other requested
query to the third landing zone. As an example, landing zone
subsystem 120 may cause the client device to transmit the other
query request, the subsequent requests (for an update related to
the other requested query), or the other requests (related to the
other requested query) to third landing zone without first
transmitting the subsequent requests or the other request to the
third landing zone.
[0058] Request-Prediction-Based Temporary Storage of Query
Results
[0059] In some embodiments, based on a prediction that a given
request (or requests similar to the predicted request) will occur
in the future, one or more actions may be initiated to prepare for
the predicted request or handle the predicted request prior to such
request occurring in the future. As an example, with respect to a
prediction of a client request for query results, the request may
comprise a query submission (or a client-initiated query), an
update request related to the client-initiated query, or other
request. The request prediction may, for instance, be based on
request history information, such as information indicating one or
more prior queries, information indicating respective frequencies
of requests (e.g., a frequency of each of the prior queries, update
requests related to the prior queries, etc.), information regarding
users or client devices that initiated prior requests, or other
information. In one scenario, at least some of the requested query
results may be obtained based on the request prediction prior to
the request being obtained from a client device in the future. The
obtained query results may be stored (e.g., in a temporary data
storage, such as a server cache, a web cache, memory cache, or
other temporary data storage) in anticipation of the request
occurring in the future so that the stored query results can be
utilized to respond to the future request upon its occurrence.
[0060] In some embodiments, request prediction subsystem 118 may
predict a request for query results, query subsystem 112 may obtain
a subset of results responsive to the request prediction, and
temporary storage subsystem 122 may cause the subset of results to
be stored in a temporary data storage. As an example, the subset of
results may be a portion of a set of results that would have been
obtained to respond to the request had the request been obtained
from a client device. In one use case, for instance, if the set of
results are all the results that would have been provided on a
first web page (e.g., a list of the most relevant results or other
presentation) returned to the client device (as a response to the
request), the subset of results may be a portion of those results
provided on the first web page. In another use case, the set of
results may be all the results that would have been obtained to
respond to the request had the request been obtained from the
client device.
[0061] In some embodiments, the set of results (including the
subset of results and other subsets of results) may be stored in
the temporary data storage. In some embodiments, temporary storage
subsystem 122 may cause the subset of results to be stored in the
temporary data storage responsive to the request prediction, and no
storage of other subsets of the set of results in the temporary
data storage occurs from the request prediction. As an example, no
performance of queries for the other subsets (of the set of
results) may occur from the request prediction. As another example,
the performance of queries (via which the subset of results is
obtained) may be stopped before the other subsets (of the set of
results) are obtained. As yet another example, even if the other
subsets are obtained (e.g., via one or more queries responsive to
the request prediction), a determination may be made not to store
the other subsets (of the set of results) in the temporary data
storage.
[0062] In some embodiments, the obtainment and/or storage of the
subset of results (and/or the determination not to obtain or store
the other subsets of results) may be based on frequency
information, cost information, preference information, or other
information. In some embodiments, the queries that are performed
(via which the subset of results is obtained) responsive to the
request prediction may be a subset of a set of queries that would
have been performed to respond to the request had the request been
obtained from a client device. These performed queries may be
selected to be performed over one or more other queries (of the set
of queries) such that no performance of the other queries occurs
from the request prediction. Query subsystem may perform the
selection of the subset of queries to be performed (over the other
queries) based on the frequency information, the cost information,
the preference information, or other information. The frequency
information may comprise information indicating a frequency of
requests matching the request or other information. The cost
information may comprise information indicating costs for storing
data in the temporary data storage, information indicating costs
for performing respective queries, or other information. Such costs
may, for instance, comprise a monetary cost, a computer resource
cost (e.g., bandwidth or other network resource usage amount or
other computer resource cost), or other costs. The preference
information may comprise information indicating one or more
preferences related to a manner in which to present data or other
information. The preferences (related to a manner in which to
present data) may comprise a preference for a sorting order for
presenting data, a preference for an amount of data to be presented
at a given time, a preference for a format in which to present
data, or other preferences.
[0063] A cost/benefit analysis may, for instance, be performed to
determine which and/or the amount of results to be obtained and/or
stored responsive to the request prediction. In one use case, query
subsystem 112 may determine whether to perform queries (and/or
which queries to perform) based on their respective costs (e.g., a
cost to query a data source for data), the respective benefits of
results obtained from those queries (e.g., a frequency of requests
matching the predicted request, which of the results have priority
over other results based on a requester's preference information,
etc.), the respective costs for storing those results at the
temporary data storage, or other criteria. In a further use case,
scores may be assigned to respective queries (before they are
executed) based on their respective costs, the respective benefits
of results obtained from those queries, the respective costs for
storing those results at the temporary data storage, or other
criteria. As an example, a lower cost to query a data source for
data may influence a higher assigned score for a corresponding
query (compare to scores for other queries). A greater frequency of
requests matching the predicted request may influence higher
assigned scores for the queries related to the predicted request. A
greater likelihood that results derived from one query will be
presented to a requester (e.g., on a user interface over other
results derived from other queries based on the requester's
preferences) may influence a higher score for the query (compare to
scores for the other queries). Based on their respective assigned
scores, query subsystem 112 may determine whether and/or which of
one or more of the queries are to be performed. As an example,
query subsystem 112 may select a subset of the queries to be
performed based on the subset of queries having greater scores than
the other subsets of queries.
[0064] In another use case, even if obtained, temporary storage
subsystem 122 may determine whether to store results (and/or the
amount of results to be stored) based on the respective costs for
storing those results at the temporary data storage, the respective
benefits of those results, or other criteria. In a further use
case, scores may be assigned to respective results (e.g., subsets
of results) based on the respective costs for storing those results
at the temporary data storage, the respective benefits of those
results, or other criteria. As an example, a lower cost to store
certain subsets of results may influence higher assigned scores for
the subset of results. A greater frequency of requests matching the
predicted request may influence higher assigned scores for the
results related to the predicted request. A greater likelihood that
certain subsets of results will be presented to a requester (e.g.,
on a user interface over other results based on the requester's
preferences) may influence a higher score for the subsets of
results. Based on their respective assigned scores, temporary
storage subsystem 122 may determine whether and/or which of the
results are to be stored at the temporary data storage. Temporary
storage subsystem 122 may, for instance, select a subset of the
results (e.g., obtained from the performed queries) to be stored
based on the subset of results having greater scores than the other
subsets of results.
[0065] As indicated, in some embodiments, a determination of which
and/or the amount of results to be obtained and/or stored
(responsive to a prediction of a request) may be based on
preference information indicating one or more preferences related
to a manner in which to present data or other information. The
preferences may be inferred based on one or more prior actions with
respect to the manner in which to present data, explicitly defined
by a customer with respect to the manner in which data is to be
presented for that customer, etc.
[0066] As an example, Table 1 above may represent calculated values
associated with sale volumes (e.g., by unit), respective ones of
the "Product Category" attributes (e.g., rows), and respective ones
of the "User Income" attributes (e.g., columns) that are first
presented at a client device (e.g., prior to other values
associated with the foregoing attributes or other attributes)
responsive to the client device's request for values associated
with the foregoing attributes. The manner in which the values and
their attributes are presented may reflect a predetermined sorting
order for presenting data (e.g., highest to lowest), an amount of
data to be presented at a given top (e.g., the top five values for
the respective attributes), a format in which to present the data,
or other manner in which to present the data.
[0067] In one scenario, if the manner in which the data is
presented in Table 1 reflects the most likely manner in which a
given customer will view a request for the values associated with
the foregoing attributes (shown in Table 1), then more weight may
be given to certain queries (from which values that will first be
presented at a client device prior to other values associated with
the foregoing attributes or other attributes) over other queries
when determining whether and/or which queries are to be performed
to obtain results relevant to a predicted request (for values
associated with the foregoing attributes). Similarly, in another
scenario, more weight may be given to the values that will first be
presented at a client device (or data for calculating such values)
over other values (or other data for calculating such other values)
when determining whether and/or which results are to be stored in a
temporary data storage in anticipation of the predicted request
occurring in the future so that the stored results can be utilized
to respond to the future request upon its occurrence. In this way,
for example, the values likely to be presented first (before other
values are to be presented) may quickly be provided without the
delays typically associated with having to wait until those values
(to be presented first) are obtained via queries to one or more
other data sources. If, for instance, data for calculating such
values are stored in a temporary data storage that enables faster
data retrieval (e.g., where the temporary data storage is a server
cache, web cache, memory cache, or other temporary data storage
that enables faster data retrieval, in comparison to obtaining data
from the other data sources), such data may be quickly obtained and
processed to calculate such values, which may then be provided to
respond to the request. If the calculated values are stored in such
temporary data storage, the calculated values may be obtained and
then provided to respond to the request.
[0068] In some embodiments, although results may be obtained and/or
stored responsive to a prediction of one or more requests (as
described herein), no results may be obtained and/or stored
responsive to a prediction of certain other requests (e.g., even if
the probabilities of those other requests occurring each satisfies
a certainty threshold). As an example, query subsystem 112 may
determine not to perform any queries responsive to a prediction of
a request based on a cost/benefit analysis performed with respect
to the predicted request (e.g., based on frequency information,
cost information, preference information, or other information). As
another example, temporary storage subsystem 122 may determine not
to store any results obtained from the request prediction based on
a cost/benefit analysis performed with respect to the predicted
request (e.g., based on frequency information, cost information,
preference information, or other information).
[0069] Request-Prediction-Based Temporary Storage of Predicted
Values
[0070] In some embodiments, where a subset of results (obtained
responsive to a prediction of a request) comprises a predicted set
of values, query subsystem 112 may perform queries for data related
to calculating a set of values (of which the subset of values is a
part) responsive to the request prediction. Query subsystem 112 may
obtain a subset of the related data (for calculating the set of
values) based on the performed queries. Data prediction subsystem
114 may predict the set of values based on the subset of the
related data (e.g., without using other subsets of the related
data). Temporary storage subsystem 122 may cause the predicted set
of values to be stored in a temporary data storage (e.g., a server
cache, a web cache, memory cache, or other temporary data storage)
in anticipation of a future request for the set of values (or
approximation thereof). Upon obtaining the request (e.g., a request
matching the predicted request) from a client device, query
subsystem 112 may process the request and determine that at least
some results satisfying the request are already stored in the
temporary data storage (e.g., in the form of the stored predicted
set of values). As such, query subsystem 112 may obtain the
predicted values from the temporary data storage based on the
request from the client device. Presentation subsystem may then
provide the predicted set of values for presentation at the client
device. As an example, the predicted set of values may be provided
to the client device, which may generate a data visualization based
on the provided predicted set of values. As another example,
presentation subsystem 124 may generate a data visualization
comprising representations of the predicted set of values and
provide the data visualization at the client device.
[0071] In some embodiments, query subsystem 112 may cause the
performance of the queries to stop prior to obtaining the other
subsets of the related data. The performance of the queries may be
stopped after at least the subset of the related data is obtained.
Data prediction subsystem 114 may thus predict the set of values
based on the subset of the related data without the other subsets
of the related data. Temporary data storage subsystem 112 may store
this predicted set of values (which are predicted without use of
the other subsets of the related data) in the temporary data
storage in anticipation of a future request for the set of values
(or approximation thereof). As indicated, in some embodiments, the
performance of the queries may be caused to stop (prior to
obtaining the other subsets of the related data) based on frequency
information, cost information, preference information, or other
information.
[0072] As an example, query subsystem 112 may determine whether an
amount of data obtained from the performance of the queries
satisfies a predetermined amount threshold. If the obtained amount
of data does not satisfy the predetermined amount threshold, query
subsystem 112 may continue the performance of the queries to obtain
additional data. If the obtained amount of data satisfies the
predetermined amount threshold, query subsystem may stop the
performance of the queries (e.g., so that computer resources saved
from the stopping of the performance of the queries may be utilized
to handle other tasks, such as performing queries related to other
predicted requests, responding to client requests, etc.). In one
scenario, query subsystem 112 may determine the predetermined
amount threshold based on the frequency information, the cost
information, the preference information, or other information. For
example, a greater frequency of requests matching the predicted
request may influence a greater amount threshold (e.g., so that
more data may be obtained to provide more accurate value prediction
given the likely higher number of requests for the predicted values
or the higher number of customers to which the predicted values
will likely be presented). A lower cost to continue to query a data
source for data (e.g., monetary or computer resource cost) may
influence a greater amount of threshold.
[0073] As another example, multiple predetermined amount thresholds
may be utilized to determine whether to stop the performance of the
queries, where a first predetermined threshold may be used to
determine whether to stop the performance of a first subset of the
queries, a second predetermined threshold may be used to determine
whether to stop the performance of a second subset of queries, and
so on. In one use case, a greater likelihood that certain results
derived from one query will be presented to a requester (e.g., on a
user interface over other results derived from other queries based
on the requester's preferences) may influence a higher amount
threshold to be used to determine whether to stop the query
(compare to amount thresholds to be used to determine whether to
stop other queries). For example, if the manner in which the data
is presented in Table 1 above reflects the most likely manner in
which a given customer will view a request for the values
associated with the foregoing attributes (shown in Table 1), then a
higher amount threshold may be given to certain queries (from which
values that will first be presented at a client device prior to
other values associated with the foregoing attributes or other
attributes) over other queries for use in determining when to stop
the performance of the queries. In this way, for example, more data
may be obtained to predict the values that will likely be presented
first (e.g., the top five highest sales volumes) to facilitate more
accurate prediction for those values. By quickly providing the
presentation of the more accurate predicted values, it may appear
to the requester that there is minimal delay from the time of a
client request being initiated at a client device and the time at
which the presentation of the "final" values are provided at the
client device (e.g., if the predicted values are the same or
similar to the actual final values when those final values are
calculated).
[0074] In some embodiments, upon obtaining a request (e.g., a
request matching a predicted request) from a client device, query
subsystem 112 may process the request and determine that a
predicted set of values satisfying the request are already stored
in a temporary data storage. Query subsystem 112 may obtain the
predicted set of values from the temporary data storage, and
presentation subsystem 124 may provide the predicted set of values
for presentation at the client device. Query subsystem 112 may also
obtain data related to calculating the predicted set of values from
one or more other data sources, and data prediction subsystem 114
may generate an updated predicted set of values based on the
obtained related data. As an example, the predicted set of values
obtained from the temporary data storage may have been predicted
based on a subset of the related data without one or more other
subsets of the related data. The updated predicted set of values,
however, may be based on additional or alternative subsets of the
related data (e.g., based on the subset of the related data and the
other subsets of the related data, based on the other subsets of
the related data without the subset of the related data, etc.).
Upon the updated prediction, presentation subsystem 124 may provide
the updated predicted set of values for presentation at the client
device. As an example, the updated predicted set of values may be
provided to the client device, which may generate a data
visualization based on the provided predicted set of values. As
another example, presentation subsystem 124 may generate a data
visualization comprising representations of the updated predicted
set of values and provide the data visualization at the client
device.
[0075] Presentation of Temporarily-Stored Results and Updating
Thereof
[0076] As discussed, in some embodiments, based on a request
obtained from a client device, query subsystem 112 may obtain one
or more results from one or more data sources (e.g., local
temporary data storage, other local data sources, external data
sources, etc.). Presentation subsystem 124 may provide a
presentation at the client device based on the obtained results. As
an example, the obtained results may be provided to the client
device, which may generate a data visualization based on the
provided results. As another example, presentation subsystem 124
may generate a data visualization comprising representations of the
obtained results and provide the data visualization at the client
device.
[0077] In some embodiments, where a subset of results (relevant to
a request from a client device) is stored in a temporary data
storage, query subsystem 112 may obtain the subset of results from
the temporary data storage responsive to the request from the
client device. If one or more other subsets of results (relevant to
the request) are not stored in the temporary data storage, query
subsystem 112 may obtain the other subsets of results via one or
more other data sources. Presentation subsystem 124 may provide a
presentation at the client device based on the subset of results
and the other subset of results. In some embodiments, presentation
subsystem 124 may provide a presentation at the client device based
on the subset of results prior to completing the obtainment of the
other subsets of results from the other data sources. Upon
obtaining the other subsets of results, update subsystem 116 may
direct presentation subsystem 124 to update the presentation at the
client device based on the subset of results, the other subsets of
results, or other results. In this way, for example, a presentation
related to the client request may quickly be provided based on the
temporarily-stored subset of results, for example, in scenarios
which the temporary data storage (from which the subset of results
are obtained) enable faster data retrieval (e.g., where the
temporary data storage is a server cache, web cache, memory cache,
or other temporary data storage enables faster data retrieval, in
comparison to obtaining data from the other data sources). As such,
for instance, the user experience of the requester (that initiated
the request at the client device) may be improved given that the
requestor need not experience the delays typically associated with
having to wait until one or more of the other subsets of the
related data are obtained from the other data sources and/or
processed.
[0078] In some embodiments, where a subset of data (related to
calculating a set of values requested by a client device) is stored
on a temporary data storage, query subsystem 112 may obtain the
subset of the related data from the temporary data storage
responsive to the request from the client device. Data prediction
subsystem 114 may predict the set of values at a first time based
on the subset of related data (obtained from the temporary data
storage). As an example, data prediction subsystem 114 may
extrapolate from the subset of the related data (that has been
processed thus far) to estimate the final values (of the set of
values) prior to the other subsets of the related data being
obtained and/or processed. Presentation subsystem 124 may provide a
presentation of the predicted set of values (that was predicted at
the first time) at the client device prior to the other subsets of
the related data being obtained and/or processed. At a second time
(subsequent to the first time and after the other subsets of the
related data is obtained), update subsystem 116 may direct data
prediction subsystem 114 to update the predicted set of values
based on the subset of the related data, the other subsets of the
related data, or other data. Update subsystem 116 may direct
presentation subsystem 116 to update the presentation of the
predicted set of values with a presentation of the updated
presented set of values (that was updated at the second time).
Thus, the requester may be quickly provided with an approximation
of what the requested final values are without the delays typically
associated with having to wait until one or more of the other
subsets of the related data are obtained from the other data
sources and/or processed. Moreover, given that the predicted set of
values (predicted without the other subsets of the related data)
may reflect the final values (or close to the final values),
quickly providing the presentation with the initially predicted set
of values may provide the requester with the appearance of minimal
delay from the time of the client request being initiated at the
client device and the time at which the presentation of the "final"
values are provided at the client device.
[0079] In some embodiments, where a predicted set of values (that
are approximations of a set of values requested by a client device)
is stored on a temporary data storage, query subsystem 112 may
obtain the predicted set of values from the temporary data storage
responsive to the request from the client device. Presentation
subsystem 124 may provide the predicted set of value for
presentation at the client device. In some embodiments, where the
predicted set of values (obtained from the temporary data storage)
was predicted based on a subset of data (related to calculating the
set of values) without one or more other subsets of the related
data, and where the other subsets of the related data are not
stored on the temporary data storage, query subsystem 112 may
perform queries to one or more other data sources to obtain the
other subsets of the related data. Update subsystem 116 may direct
data prediction subsystem 114 to update the predicted set of values
based on the subset of the related data, the other subsets of the
related data, or other data. Update subsystem 116 may direct
presentation subsystem 116 to update the presentation of the
predicted set of values with a presentation of the updated
presented set of values. In this way, for example, the requester
may be quickly provided with an approximation of what the requested
final values are without the delays typically associated with
having to wait for an initial prediction of those final values
(e.g., since such initially predicted values were obtained from the
temporary data storage) and/or until one or more of the other
subsets of the related data are obtained from the other data
sources and/or processed. In addition, as indicated above, given
that the predicted set of values (predicted without the other
subsets of the related data) may reflect the final values (or close
to the final values), quickly providing the presentation with the
initially predicted set of values may provide the requester with
the appearance of minimal delay from the time of the client request
being initiated at the client device and the time at which the
presentation of the "final" values are provided at the client
device.
[0080] Examples Flowcharts
[0081] FIGS. 3-7 are example flowcharts of processing operations of
methods that enable the various features and functionality of the
system as described in detail above. The processing operations of
each method presented below are intended to be illustrative and
non-limiting. In some embodiments, for example, the methods may be
accomplished with one or more additional operations not described,
and/or without one or more of the operations discussed.
Additionally, the order in which the processing operations of the
methods are illustrated (and described below) is not intended to be
limiting.
[0082] In some embodiments, the methods may be implemented in one
or more processing devices (e.g., a digital processor, an analog
processor, a digital circuit designed to process information, an
analog circuit designed to process information, a state machine,
and/or other mechanisms for electronically processing information).
The processing devices may include one or more devices executing
some or all of the operations of the methods in response to
instructions stored electronically on an electronic storage medium.
The processing devices may include one or more devices configured
through hardware, firmware, and/or software to be specifically
designed for execution of one or more of the operations of the
methods.
[0083] FIG. 3 shows a flowchart of a method 700 of facilitating
reduction of network resource usage with respect to writes to a
data storage corresponding to a landing zone, in accordance with
one or more embodiments.
[0084] In an operation 302, data related to calculating a set of
values may be obtained. As an example, the related data may be
obtained based on one or more queries. The queries may be performed
to obtain respective portions of the related data from one or more
data sources (e.g., local temporary data storage, other local data
sources, external data sources, etc.). Operation 302 may be
performed by a query subsystem that is the same as or similar to
query subsystem 112, in accordance with one or more
embodiments.
[0085] In some embodiments, with respect to operation 302, a
request for at least some values (of the set of values) may be
obtained from a client device. A plurality of queries for the
related data may be generated based on the request from the client
device. The plurality of queries may be performed to obtain the
related data. The obtainment of the request and/or the generation
of the queries may be performed by a query subsystem that is the
same as or similar to query subsystem 112, in accordance with one
or more embodiments.
[0086] In an operation 304, the set of values may be predicted
based on a subset of the related data. As an example, the predicted
set of values may comprise approximations of what the values of the
set of values would be had a complete set of data corresponding to
the set of values been used to calculate the values of the set of
values. As a further example, the related data may be the complete
set of data for calculating the values of the set of values. The
subset of the related data may be obtained responsive to performing
one or more queries, and the subset of the related data may
comprise data obtained prior to one or more other subsets of the
related data being obtained. The set of values may, for instance,
be predicted based on the subset of the related data without the
other subsets of the related data. Operation 304 may be performed
by a data prediction subsystem that is the same as or similar to
data prediction subsystem 114, in accordance with one or more
embodiments.
[0087] In an operation 306, the predicted set of values may be
caused to be stored at a first data storage and a second data
storage, where the second data storage corresponds to a landing
zone. As an example, the landing zone may be the second data
storage, a server (e.g., a physical server, a virtual server,
etc.), an area (e.g., a web page, a node, etc.) hosted at the
server, or other component via which a client device obtains data
(e.g., at least some values of the predicted set of values, raw
query results, or other data) or via which the client device
interacts to have its requests handled. As another example, the
first and second data storages may be server data storages, such as
areas on one or more hard drives or other electronic storages
(e.g., server cache, web cache, other temporary data storage, or
other storage) hosted at a server, databases associated with a
server, or other server data storages. The server data storages may
be associated with the same server or different servers. Operation
306 may be performed by an update subsystem that is the same as or
similar to update subsystem 116, in accordance with one or more
embodiments.
[0088] In some embodiments, with respect to operation 306,
compatibility information associated with the client device (that
will interact with the landing zone) may be obtained, and the
second data storage may be selected (for storage of versions of the
predicted set of values) from among a plurality of data storages
based on the compatibility information. As an example, the
compatibility information may comprise information indicating one
or more database management systems with which the client device is
compatible or other information. The second data storage may be
selected (for storage of versions of the predicted set of values)
based on a determination that the second data storage uses at least
one of the compatible database management systems (indicated by the
compatibility information). The obtainment of the compatibility
information and/or the selection of the second data storage may be
performed by a landing zone subsystem that is the same as or
similar to landing zone subsystem 120, in accordance with one or
more embodiments.
[0089] In an operation 308, the predicted set of values stored at
the first data storage may be updated based on further subsets of
the related data at least a first number of times within a time
period. As an example, the predicted set of values may initially be
based on the subset of the related data (obtained prior to one or
more other subsets of the related data being obtained) without the
other subsets of the related data. As some of the other subsets of
the related data are obtained, the initially-obtained subset of
data and the obtained ones of the other subsets (of the related
data) may be utilized to update the predicted set of values (e.g.,
to update the approximations of what the values of the set of
values would be had a complete set of data corresponding to the set
of values been used to calculate the values of the set of values).
As another example, the predicted set of values stored at the first
data storage may be periodically updated (e.g., every second or
other rate) during the time period. As yet another example, the
predicted set of values stored at the first data storage may be
updated in real-time during the time period as the further subsets
of the related data are being obtained. Operation 308 may be
performed by prediction and/or update subsystems that are the same
as or similar to prediction subsystem 114 and/or update subsystem
116, in accordance with one or more embodiments. As an example,
prediction subsystem 114 may predict the set of values based on the
initially-obtained subset of the related data and the obtained
other subsets of the related data. As a further example, update
subsystem 116 may cause the most recent version of the predicted
set of values to be stored at the first data storage to replace the
prior version of the predicted set of values stored at the first
data storage.
[0090] In an operation 310, the predicted set of values stored at
the second data storage (corresponding to a landing zone) may be
updated (i) less than the first number of times within the time
period and (ii) based on the predicted set of values stored at the
first data storage. As an example, a version of the predicted set
of values stored at the first data storage may be periodically
transmitted to the second data storage (e.g., every 5 seconds,
every minute, or other rate) to replace a version of the predicted
set of values stored at the second data storage. Each of the
periodically-transmitted versions may, for instance, be more
recently updated than the respective replaced version of the
predicted set of values stored at the second data storage.
Operation 310 may be performed by an update subsystem that is the
same as or similar to update subsystem 116, in accordance with one
or more embodiments.
[0091] In some embodiments, with respect to operation 310, the
number of times that the predicted set of values stored at the
second data storage is updated within a given time period may be
based on cost information, such as information indicating one or
more costs associated with writing to the second data storage or
other information. As an example, an update rate for updating the
predicted set of values stored at the first data storage may be a
first update rate, and an update rate for updating the predicted
set of values stored at the second data storage may be a second
update rate. The second update rate may be determined (as an update
rate for updating the predicted set of values stored at the second
data storage) based on the costs associated with writing to the
second data storage. Such associated costs may, for instance,
comprise a monetary cost for writing to the second data storage, a
computer resource cost for writing the second data storage (e.g.,
bandwidth or other network resource usage amount or other computer
resource cost), or other costs.
[0092] FIG. 4 shows a flowchart of a method of facilitating load
reduction at a landing zone, in accordance with one or more
embodiments.
[0093] In an operation 402, a request related to a client-initiated
query may be predicted. As an example, the query-related request
may comprise a query submission related to the client-initiated
query, an update request related to the client-initiated query, or
other request. As another example, the query-related request may be
predicted based on request history information, such as information
indicating one or more queries initiated prior to the
client-initiated query, information indicating a frequency of
requests related to queries matching the client-initiated query, or
other information. Operation 402 may be performed by a request
prediction subsystem that is the same as or similar to request
prediction subsystem 118, in accordance with one or more
embodiments.
[0094] In an operation 404, a second landing zone may be assigned
to handle responding to the query-related request, where the second
landing zone is different from a first landing zone at which the
query-related request is subsequently obtained. As an example, the
first landing zone may be a default landing zone, and the second
landing zone may be a query-specific landing zone. The first and
second landing zones may each be a data storage (e.g., a database
or other data storage), a server (e.g., a physical server, a
virtual server, etc.), an area (e.g., a web page, a node, etc.)
hosted at the server, or other component via which a client device
obtains data or interacts to have its requests handled. Operation
404 may be performed by a landing zone subsystem that is the same
as or similar to landing zone subsystem 120, in accordance with one
or more embodiments.
[0095] In an operation 406, the query-related request may be
obtained at the first landing zone from a client device. As an
example, the client device may transmit the query-related request
to the first landing zone based on the first landing zone being a
prior landing zone with which the client device interacted, the
first landing zone being a default landing zone, or other criteria.
Operation 406 may be performed by a landing zone subsystem that is
the same as or similar to landing zone subsystem 120, in accordance
with one or more embodiments.
[0096] In an operation 408, the client device may be caused to
redirect the query-related request to the second landing zone based
on the assignment to the second landing zone. As an example,
responsive to the redirection, the client device may transmit the
query-related request, subsequent requests for an update related to
the client-initiated query, or other requests to the second landing
zone. As a further example, responsive to the redirection, the
client device may transmit the query-related request and the
subsequent requests to the second landing zone without first
transmitting the subsequent requests to the first landing zone.
Operation 408 may be performed by a landing zone subsystem that is
the same as or similar to landing zone subsystem 120, in accordance
with one or more embodiments.
[0097] In some embodiments, with respect to operation 408, the
client device may be caused to redirect the query-related request
to the second landing zone by providing, to the client device, a
redirection instruction, a reference associated with the second
landing zone (e.g., an identifier or address associated with the
second landing zone), or other information. As an example,
responsive to being provided the redirection instruction and the
second landing zone reference, the client device may process the
redirection instruction and use the second landing zone reference
to transmit the query-related request and the subsequent requests
to the second landing zone (e.g., without first/initially
transmitting the subsequent requests to the first landing
zone).
[0098] In an operation 410, another request related to another
client-initiated query (matching the client-initiated query) may be
obtained at the first landing zone from another client device. As
an example, the other client device may transmit the other
query-related request to the first landing zone based on the first
landing zone being a prior landing zone with which the other client
device interacted, the first landing zone being a default landing
zone, or other criteria. Operation 410 may be performed by a
landing zone subsystem that is the same as or similar to landing
zone subsystem 120, in accordance with one or more embodiments.
[0099] In an operation 412, the other client device may be caused
to redirect the other query-related request to the second landing
zone based on the assignment to the second landing zone and the
matching. As an example, responsive to the redirection, the other
client device may transmit the other query-related request, other
subsequent requests for an update related to the other
client-initiated query, or other requests to the second landing
zone. As a further example, responsive to the redirection, the
other client device may transmit the other query-related request
and the other subsequent requests to the second landing zone
without first transmitting the other subsequent requests to the
first landing zone. Operation 412 may be performed by a landing
zone subsystem that is the same as or similar to landing zone
subsystem 120, in accordance with one or more embodiments.
[0100] In some embodiments, with respect to operation 412, the
other client device may be caused to redirect the query-related
request to the second landing zone by providing, to the other
client device, a redirection instruction, a reference associated
with the second landing zone (e.g., an identifier or address
associated with the second landing zone), or other information. As
an example, responsive to being provided the redirection
instruction and the second landing zone reference, the other client
device may process the redirection instruction and use the second
landing zone reference to transmit the other query-related request
and the other subsequent requests to the second landing zone (e.g.,
without first/initially transmitting the other subsequent requests
to the first landing zone).
[0101] FIG. 5 shows a flowchart of a method of facilitating queries
via request-prediction-based temporary storage of query results, in
accordance with one or more embodiments.
[0102] In an operation 502, a request for query results may be
predicted. As an example, the request may comprise a query
submission (or a client-initiated query), an update request related
to the client-initiated query, or other request. As another
example, the query-related request may be predicted based on
request history information, such as information indicating one or
more queries initiated prior to the client-initiated query,
information indicating a frequency of requests related to queries
matching the client-initiated query, or other information.
Operation 502 may be performed by a request prediction subsystem
that is the same as or similar to request prediction subsystem 118,
in accordance with one or more embodiments.
[0103] In an operation 504, a subset of results (of a set of
results) may be obtained responsive to the request prediction. As
an example, the set of results (of which the obtained results is a
subset) may comprise results that would have been obtained to
respond to the request had the request been obtained from a client
device. One or more queries may be performed responsive to the
request prediction to obtain the subset of results. Operation 504
may be performed by a query subsystem that is the same as or
similar to query subsystem 112, in accordance with one or more
embodiments.
[0104] In an operation 506, the subset of results may be caused to
be stored in a temporary data storage without storage of other
subsets of the set of results in the temporary data storage
occurring from the request prediction. The temporary data storage
may comprise a server cache, a web cache, memory cache, or other
temporary data storage. As an example, no performance of queries
for the other subsets (of the set of results) may be triggered by
the request prediction. As another example, the performance of
queries via which the subset of results is obtained may be stopped
before the other subsets (of the set of results) are obtained. As
yet another example, even if the other subsets are obtained (e.g.,
via one or more queries responsive to the request prediction), a
determination may be made not to store the other subsets (of the
set of results) in the temporary data storage. Operation 506 may be
performed by a temporary storage subsystem that is the same as or
similar to temporary storage subsystem 122, in accordance with one
or more embodiments.
[0105] In some embodiments, with respect to operations 504 and 506,
the obtainment and/or storage of the subset of results may be
performed based on frequency information, cost information,
preference information, or other information. The frequency
information may comprise information indicating a frequency of
requests matching the request or other information. The cost
information may comprise information indicating costs for storing
data in the temporary data storage, information indicating costs
for performing respective queries, or other information. The
preference information may comprise information indicating one or
more preferences related to a manner in which to present data or
other information. The preferences (related to a manner in which to
present data) may comprise a preference for a sorting order for
presenting data, a preference for an amount of data to be presented
at a given time, a preference for a format in which to present
data, or other preferences. The foregoing obtainment and/or storage
of the subset of results (and/or determination of the information
on which the obtainment and/or storage are based) may be performed
by a query subsystem or a temporary storage subsystem that are the
same as or similar to query subsystem 112 or temporary storage
subsystem 122, in accordance with one or more embodiments.
[0106] In an operation 508, the request may be obtained from the
client device subsequent to the storage of the subset of results.
Operation 508 may be performed by a query subsystem that is the
same as or similar to query subsystem 112, in accordance with one
or more embodiments.
[0107] In an operation 510, the subset of results may be obtained
from the temporary data storage based on the request from the
client device. As an example, the temporary data storage may first
be checked to determine whether results relevant to the request
exist in the temporary data storage. As another example, the subset
of results may be assigned one or more attributes associated with
the request, and the attributes of the request may be compared
against a collection of assigned attribute sets to determine
whether a match exists between the attributes of the request and
one of the assigned attributes sets. If, for instance, the
attributes of the request matches an assigned attribute set, the
assigned attribute set may be used to obtain the subset of results
stored in the temporary data storage. Operation 510 may be
performed by a query subsystem and/or a temporary storage subsystem
that are the same as or similar to query subsystem 112 and/or
temporary storage subsystem 122, in accordance with one or more
embodiments. As an example, query subsystem 112 may direct
temporary storage subsystem 118 to check for whether results
relevant to the request is stored in the temporary data storage
(e.g., responsive to query subsystem 112 identifying a match
between the attributes of the request and one of the assigned
attribute sets or responsive to one or more other conditions).
[0108] In an operation 512, the other subsets of the set of results
from one or more other data sources. As an example, responsive to a
determination that the other subsets are not stored in the
temporary data storage, one or more queries to the other data
sources may be performed to obtain the other subsets (of the set of
results) from the other data sources. Operation 512 may be
performed by a query subsystem that is the same as or similar to
query subsystem 112, in accordance with one or more
embodiments.
[0109] In an operation 514, a presentation may be provided at the
client device based on the subset of results and the other subsets
of the set of results. As an example, the presentation provided at
the client device may comprise a representation of the subset of
results, a representation of the other subsets of the set of
results, or other representation of results. Operation 512 may be
performed by a presentation subsystem that is the same as or
similar to presentation subsystem 124, in accordance with one or
more embodiments.
[0110] FIG. 6 shows a flowchart of a method of facilitating queries
via request-prediction-based temporary storage of query results, in
accordance with one or more embodiments.
[0111] In an operation 602, a request for query results may be
predicted. As an example, the request may comprise a query
submission (or a client-initiated query), an update request related
to the client-initiated query, or other request. As another
example, the query-related request may be predicted based on
request history information, such as information indicating one or
more queries initiated prior to the client-initiated query,
information indicating a frequency of requests related to queries
matching the client-initiated query, or other information.
Operation 602 may be performed by a request prediction subsystem
that is the same as or similar to request prediction subsystem 118,
in accordance with one or more embodiments.
[0112] In an operation 604, queries for at least some of the query
results may be performed responsive to the request prediction
without performance of one or more other queries (of a set of
queries related to the request) occurring from the request
prediction. As an example, the set of queries may comprise queries
that would have been performed to respond to the request had the
request been obtained from a client device, and no performance of
the other queries (of the set of queries) may be triggered by the
request prediction. Operation 604 may be performed by a query
subsystem that is the same as or similar to query subsystem 112, in
accordance with one or more embodiments.
[0113] In an operation 606, results may be obtained based on the
performed queries. As an example, the obtained results may be a
subset of a set of results that would have been obtained to respond
to the request had the request been obtained from the client
device. As another example, no obtainment of other subsets of the
set of results may be triggered by the request prediction.
Operation 604 may be performed by a query subsystem that is the
same as or similar to query subsystem 112, in accordance with one
or more embodiments.
[0114] In some embodiments, with respect to operations 604 and 606,
cost information may be determined. As an example, the cost
information may indicate costs for performing respective queries
(of the set of queries that would have been performed to respond to
the request had the request been obtained from the client device).
The queries to be performed may be selected from among the set of
queries based on the cost information. The results may be obtained
based on the performance of the selected queries.
[0115] In an operation 608, the results (obtained based on the
performed queries) may be caused to be stored in a temporary data
storage. In one use case, no storage of other subsets of the set of
results (that would have been obtained to respond to the request
had the request been obtained from the client device) in the
temporary data storage may occur from the request prediction. As an
example, no obtainment of the other subsets of the set of results
may be triggered by the request prediction. As another example,
even if the other subsets are obtained (e.g., via one or more
queries responsive to the request prediction), a determination may
be made not to store the other subsets (of the set of results) in
the temporary data storage. Operation 608 may be performed by a
temporary storage subsystem that is the same as or similar to
temporary storage subsystem 122, in accordance with one or more
embodiments.
[0116] In an operation 610, the request may be obtained from the
client device subsequent to the storage of the results. Operation
610 may be performed by a query subsystem that is the same as or
similar to query subsystem 112, in accordance with one or more
embodiments.
[0117] In an operation 612, the results may be obtained from the
temporary data storage based on the request from the client device.
As an example, the temporary data storage may first be checked to
determine whether results relevant to the request exist in the
temporary data storage. As another example, the subset of results
may be assigned one or more attributes associated with the request,
and the attributes of the request may be compared against a
collection of assigned attribute sets to determine whether a match
exists between the attributes of the request and one of the
assigned attributes sets. If, for instance, the attributes of the
request matches an assigned attribute set, the assigned attribute
set may be used to obtain the subset of results stored in the
temporary data storage. Operation 612 may be performed by a query
temporary subsystem and/or a temporary storage subsystem that are
the same as or similar to query subsystem 112 and/or temporary
storage subsystem 122, in accordance with one or more
embodiments.
[0118] In an operation 614, a presentation may be provided at the
client device based on the results (obtained from the temporary
data storage). As an example, the presentation provided at the
client device may comprise a representation of the results
(obtained from the temporary data storage) or other representation
of other results. Operation 614 may be performed by a presentation
subsystem that is the same as or similar to presentation subsystem
124, in accordance with one or more embodiments.
[0119] FIG. 7 shows a flowchart of a method of facilitating queries
via request-prediction-based temporary storage of query results, in
accordance with one or more embodiments.
[0120] In an operation 702, a request for query results may be
predicted. As an example, the request may comprise a query
submission (or a client-initiated query), an update request related
to the client-initiated query, or other request. As another
example, the query-related request may be predicted based on
request history information, such as information indicating one or
more queries initiated prior to the client-initiated query,
information indicating a frequency of requests related to queries
matching the client-initiated query, or other information.
Operation 702 may be performed by a request prediction subsystem
that is the same as or similar to request prediction subsystem 118,
in accordance with one or more embodiments.
[0121] In an operation 704, responsive to the request prediction,
queries for data related to calculating a set of values may be
performed. As an example, one or more other queries of a set of
queries related to the request may also be performed responsive to
the request prediction. The set of queries may, for instance,
comprise queries that would have been performed to respond to the
request had the request been obtained from a client device. As
another example, no performance of the other queries (of the set of
queries) may be triggered by the request prediction. Operation 704
may be performed by a query subsystem that is the same as or
similar to query subsystem 112, in accordance with one or more
embodiments.
[0122] In some embodiments, with respect to operation 704, the
queries to be performed may be selected from among a set of queries
that would have been performed to respond to the request had the
request been obtained from the client device. As an example, the
queries to be performed may be selected from among the set of
queries based on frequency information, cost information,
preference information, or other information. The frequency
information may comprise information indicating a frequency of
requests matching the request or other information. The cost
information may comprise information indicating costs for
performing respective queries of the set of queries or other
information. The preference information may comprise information
indicating one or more preferences related to a manner in which to
present data or other information. The preferences (related to a
manner in which to present data) may comprise a preference for a
sorting order for presenting data, a preference for an amount of
data to be presented at a given time, a preference for a format in
which to present data, or other preferences. The foregoing
selection and/or determination of the information (on which the
selection is based) may be performed by a query subsystem that is
the same as or similar to query subsystem 112, in accordance with
one or more embodiments.
[0123] In an operation 706, a subset of the related data may be
obtained based on the performed queries. As an example, the related
data may be the complete set of data for calculating the values of
the set of values. As another example, the subset of the related
data may comprise data obtained prior to one or more other subsets
of the related data being obtained. Operation 706 may be performed
by a query subsystem that is the same as or similar to query
subsystem 112, in accordance with one or more embodiments.
[0124] In an operation 708, the performance of the queries may be
caused to stop prior to obtaining one or more other subsets of the
related data. In one scenario, the performance of the queries may
be caused to stop (prior to obtaining the other subsets of the
related data) based on cost information. As an example, the cost
information may comprise information indicating costs for
performing respective ones of the queries, information indicating
costs for storing data in the temporary data storage, or other
information. Operation 708 may be performed by a query subsystem
that is the same as or similar to query subsystem 112, in
accordance with one or more embodiments.
[0125] In an operation 710, the set of values may be predicted
based on the subset of the related data. As an example, the
predicted set of values may comprise approximations of what the
values of the set of values would be had a complete set of data
corresponding to the set of values been used to calculate the
values of the set of values. As a further example, the related data
may be the complete set of data for calculating the values of the
set of values. The subset of the related data may comprise data
obtained prior to one or more other subsets of the related data
being obtained. The set of values may, for instance, be predicted
based on the subset of the related data without the other subsets
of the related data. Operation 710 may be performed by a data
prediction subsystem that is the same as or similar to data
prediction subsystem 114, in accordance with one or more
embodiments.
[0126] In an operation 712, the predicted set of values may be
caused to be stored in a temporary data storage. Operation 712 may
be performed by a data prediction subsystem that is the same as or
similar to data prediction subsystem 114, in accordance with one or
more embodiments.
[0127] In an operation 714, the request may be obtained from the
client device subsequent to the storage of the predicted set of
values. Operation 714 may be performed by a query subsystem that is
the same as or similar to query subsystem 112, in accordance with
one or more embodiments.
[0128] In an operation 716, the predicted set of values may be
obtained from the temporary data storage based on the request from
the client device. As an example, the temporary data storage may
first be checked to determine whether results relevant to the
request exist in the temporary data storage. As another example,
the predicted set of values may be assigned one or more attributes
associated with the request, and the attributes of the request may
be compared against a collection of assigned attribute sets to
determine whether a match exists between the attributes of the
request and one of the assigned attributes sets. If, for instance,
the attributes of the request matches an assigned attribute set,
the assigned attribute set may be used to obtain the predicted set
of values stored in the temporary data storage. Operation 716 may
be performed by a query temporary subsystem and/or a temporary
storage subsystem that are the same as or similar to query
subsystem 112 and/or temporary storage subsystem 122, in accordance
with one or more embodiments.
[0129] In an operation 718, the predicted set of values may be
provided for presentation at the client device. As an example, the
presentation at the client device may comprise a representation of
the predicted set of values. Operation 718 may be performed by a
presentation subsystem that is the same as or similar to
presentation subsystem 124, in accordance with one or more
embodiments.
[0130] In some embodiments, the various computers and subsystems
illustrated in FIG. 1 may include one or more computing devices
that are programmed to perform the functions described herein. The
computing devices may include one or more electronic storages
(e.g., prediction and visualization database(s) 132 or other
electric storages), one or more physical processors programmed with
one or more computer program instructions, and/or other components.
The computing devices may include communication lines or ports to
enable the exchange of information with a network (e.g., network
150) or other computing platforms via wired or wireless techniques
(e.g., Ethernet, fiber optics, coaxial cable, WiFi, Bluetooth, near
field communication, or other technologies). The computing devices
may include a plurality of hardware, software, and/or firmware
components operating together. For example, the computing devices
may be implemented by a cloud of computing platforms operating
together as the computing devices.
[0131] The electronic storages may include non-transitory storage
media that electronically stores information. The electronic
storage media of the electronic storages may include one or both of
(i) system storage that is provided integrally (e.g., substantially
non-removable) with servers or client devices or (ii) removable
storage that is removably connectable to the servers or client
devices via, for example, a port (e.g., a USB port, a firewire
port, etc.) or a drive (e.g., a disk drive, etc.). The electronic
storages may include one or more of optically readable storage
media (e.g., optical disks, etc.), magnetically readable storage
media (e.g., magnetic tape, magnetic hard drive, floppy drive,
etc.), electrical charge-based storage media (e.g., EEPROM, RAM,
etc.), solid-state storage media (e.g., flash drive, etc.), and/or
other electronically readable storage media. The electronic
storages may include one or more virtual storage resources (e.g.,
cloud storage, a virtual private network, and/or other virtual
storage resources). The electronic storage may store software
algorithms, information determined by the processors, information
obtained from servers, information obtained from client devices, or
other information that enables the functionality as described
herein.
[0132] The processors may be programmed to provide information
processing capabilities in the computing devices. As such, the
processors may include one or more of a digital processor, an
analog processor, a digital circuit designed to process
information, an analog circuit designed to process information, a
state machine, and/or other mechanisms for electronically
processing information. In some embodiments, the processors may
include a plurality of processing units. These processing units may
be physically located within the same device, or the processors may
represent processing functionality of a plurality of devices
operating in coordination. The processors may be programmed to
execute computer program instructions to perform functions
described herein of subsystems 112-124 or other subsystems. The
processors may be programmed to execute computer program
instructions by software; hardware; firmware; some combination of
software, hardware, or firmware; and/or other mechanisms for
configuring processing capabilities on the processors.
[0133] It should be appreciated that the description of the
functionality provided by the different subsystems 112-124
described herein is for illustrative purposes, and is not intended
to be limiting, as any of subsystems 112-124 may provide more or
less functionality than is described. For example, one or more of
subsystems 112-124 may be eliminated, and some or all of its
functionality may be provided by other ones of subsystems 112-124.
As another example, additional subsystems may be programmed to
perform some or all of the functionality attributed herein to one
of subsystems 112-124.
[0134] Although the present invention has been described in detail
for the purpose of illustration based on what is currently
considered to be the most practical and preferred embodiments, it
is to be understood that such detail is solely for that purpose and
that the invention is not limited to the disclosed embodiments,
but, on the contrary, is intended to cover modifications and
equivalent arrangements that are within the scope of the appended
claims. For example, it is to be understood that the present
invention contemplates that, to the extent possible, one or more
features of any embodiment can be combined with one or more
features of any other embodiment.
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