U.S. patent application number 15/337880 was filed with the patent office on 2017-05-11 for space-time-node engine signal structure.
The applicant listed for this patent is Space Time Insight, Inc.. Invention is credited to Krishna Kumar.
Application Number | 20170132263 15/337880 |
Document ID | / |
Family ID | 47090960 |
Filed Date | 2017-05-11 |
United States Patent
Application |
20170132263 |
Kind Code |
A1 |
Kumar; Krishna |
May 11, 2017 |
SPACE-TIME-NODE ENGINE SIGNAL STRUCTURE
Abstract
Example methods, apparatuses, or articles of manufacture are
disclosed that may be implemented using one or more computing
devices or platforms to facilitate or otherwise support one or more
processes or operations associated with a space-time-node engine
signal structure.
Inventors: |
Kumar; Krishna; (San Mateo,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Space Time Insight, Inc. |
San Mateo |
CA |
US |
|
|
Family ID: |
47090960 |
Appl. No.: |
15/337880 |
Filed: |
October 28, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14714217 |
May 15, 2015 |
9495099 |
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15337880 |
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14294925 |
Jun 3, 2014 |
9047353 |
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14714217 |
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13100212 |
May 3, 2011 |
8768873 |
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14294925 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 3/0671 20130101;
G06Q 10/06 20130101; G06F 3/0638 20130101; H04L 41/145 20130101;
G06F 3/0604 20130101; G06F 2212/702 20130101; G06F 16/258 20190101;
G06F 16/2246 20190101; Y02P 90/86 20151101; Y02P 90/80 20151101;
G06F 16/2264 20190101; G06F 12/0276 20130101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; G06F 12/02 20060101 G06F012/02 |
Claims
1. A method comprising: processing one or more digital signals in a
spatial-temporal-nodal format; and organizing said processed one or
more digital signals based, at least in part, on said format.
2. The method of claim 1, wherein said processing said one or more
digital signals in said spatial-temporal-nodal format comprises:
electronically identifying one or more dominant attributes among a
number of attributes associated with said one or more digital
signals; and electronically applying a memory sweep operation to
said one or more identified dominant attribute.
3. The method of claim 2, wherein said one or more dominant
attribute is identified based, at least in part, on an application
of a dominance principle.
4. The method of claim 3, wherein said dominance principle is
based, at least in part, on determining a degree of cardinality of
said number of attributes associated with said one or more digital
signals.
5. The method of claim 2, wherein said electronically applying said
memory sweep operation comprises applying said operation to one or
more clusters of said one or more digital signals in said
spatial-temporal-nodal format.
6. The method of claim 5, wherein said one or more clusters
comprises at least one cluster generated based, at least in part,
on at least one of the following: an R-tree-type indexing; a
KD-tree-type indexing; or any combination thereof.
7. The method of claim 2, wherein said memory sweep operation
comprises transforming said one or more digital signals in said
spatial-temporal-nodal format from an n-dimensional representation
into a reduced-dimensional representation.
8. The method of claim 7, wherein said reduced-dimensional
representation comprises a two-dimensional representation.
9. The method of claim 7, wherein said transforming is based, at
least in part, on an application of a dominant metric weighting
factor.
10. The method of claim 7, and further comprising applying one or
more linearization operations to said transformed one or more
digital signals.
11. The method of claim 10, wherein said one or more linearization
operations are based, at least in part, on one or more distance
calculations with respect to said transformed one or more digital
signals.
12. The method of claim 2, wherein said memory sweep operation
comprises transforming said one or more digital signals in said
spatial-temporal-nodal format from a reduced-dimensional
representation into a two-dimensional representation.
13. The method of claim 2, wherein said memory sweep operation
comprises transforming said one or more digital signals in said
spatial-temporal-nodal format from a two-dimensional representation
into a one-dimensional representation.
14. The method of claim 2, wherein said applying said memory sweep
operation to said one or more identified dominant attributes
further comprises electronically generating a transitioning curve
to locate said one or more identified dominant attributes.
15. The method of claim 14, wherein said transitioning curve is
generated based, at least in part, on electronically specifying an
incremental sweep angle and an incremental radius.
16. The method of claim 15, and further comprising electronically
specifying a radius to form a resulting circle having a central
point corresponding to said one or more dominant attributes located
by said transitioning curve.
17. The method of claim 16, and further comprising electronically
performing at least one distance calculation with respect to at
least one signal located within said formed circle.
18. The method of claim 1, wherein said organizing said processed
one or more digital signals comprises arranging said processed
signals as a function of mutually relative distance.
19-38. (canceled)
39. An article comprising: a storage medium having instructions
stored thereon executable by a special purpose computing platform
to: process one or more digital signals in a spatial-temporal-nodal
format; and organize said processed one or more digital signals
based, at least in part, on said format.
40-48. (canceled)
49. An apparatus comprising: a computing platform having a
capability to: acquire one or more sampled signals comprising
digital signal vectors having one or more attributes; and format
said digital signal vectors based, at least in part, on features of
said one or more attributes.
50-53. (canceled)
Description
BACKGROUND
[0001] 1. Field
[0002] The present disclosure relates generally to an in-memory
organization or architecture and, more particularly, to a
space-time-node engine signal structure for use in or with an
in-memory organization or architecture.
[0003] 2. Information
[0004] Information is everywhere. A wide variety of information,
such as, for example, business information, social information,
service information, scientific information, environmental
information, or the like is continually being generated, accessed,
shared, collected, communicated, stored, or analyzed. Information
databases or warehouses including, for example, relational,
multi-dimensional, transactional, hierarchical, or other like
information repositories are becoming more common place as well as
related communications networks or computing resources that provide
access to various types of information.
[0005] Today, a variety of information from a variety of sources
may be used in some manner to analyze, visualize, forecast,
leverage, etc. various social, political, geographical, regulatory,
business, or like segments to facilitate or support intelligent
approaches for business decision-making, performance management,
market research, situational awareness, or the like. For example,
information may be used by project or performance management
applications to deliver tailored approaches helping to gain a
competitive advantage by improving workflow or operating
procedures, acquiring business insights, assessing risks or
opportunities, creating or maintaining regulatory compliance
infrastructure, or the like.
[0006] With an overabundance of diverse information being available
or otherwise accessible, information processing tools or techniques
continue to evolve or improve. At times, however, processing or
organizing information may prove to be a computationally complex,
time-consuming, or otherwise resource-demanding task, which may
present a number of challenges, such as increased processing time,
complexity, cost, or the like. Accordingly, how to process or
organize diverse information in an effective or efficient manner
continues to be an area of development.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] Non-limiting or non-exhaustive aspects are described with
reference to the following figures, wherein like reference numerals
refer to like parts throughout the various figures unless otherwise
specified.
[0008] FIG. 1 is a flow diagram of an implementation of an example
process for performing multi-source signal processing.
[0009] FIG. 2 is a flow diagram of an example process for
performing multi-source signal processing in connection with a
space-time-node engine signal structure.
[0010] FIG. 3A is schematic illustration of an implementation of an
example information structure in a space-time-nodal grain (STING)
format.
[0011] FIG. 3B is schematic illustration of an implementation of an
example information structure in a STING format having one or more
dominant attributes.
[0012] FIG. 4 is an implementation of an example manifest file.
[0013] FIGS. 4A and 4B are respective enlarged areas A and B of the
manifest file of FIG. 4.
[0014] FIG. 5 illustrates an implementation of an example
application of a dominance principle.
[0015] FIG. 6 illustrates an implementation of an example
application of a Gaussian filter.
[0016] FIG. 7 is a flow diagram of an implementation of an example
process that may be performed in connection with a memory sweep
operation.
[0017] FIG. 8 illustrates an implementation of example STING
clusters.
[0018] FIG. 9 is a flow diagram of an implementation of an example
process that may be performed in connection with an example
linearization operation.
[0019] FIG. 10 illustrates an implementation of an example centroid
of a STING cluster.
[0020] FIG. 11 illustrates an implementation of an example
transitioning curve.
[0021] FIG. 12 illustrates an implementation of an example STING
cluster and memory array in connection with an example
linearization operation.
[0022] FIG. 13 illustrates an implementation of an example
graph.
[0023] FIG. 14 is a schematic representation of an implementation
of an example space-time-node engine signal structure.
[0024] FIG. 15 is a schematic representation of an implementation
of an example class or UML-type diagram.
[0025] FIGS. 15A, 15B, 15C, and 15D are respective enlarged areas
A, B, C, and D of the diagram of FIG. 15.
[0026] FIG. 16 is a schematic diagram illustrating an
implementation of a computing environment associated with one or
more special purpose computing devices.
DETAILED DESCRIPTION
[0027] In the following detailed description, numerous specific
details are set forth to provide a thorough understanding of
claimed subject matter. However, it will be understood by those
skilled in the art that claimed subject matter may be practiced
without these specific details. In other instances, methods,
apparatuses, or systems that would be known by one of ordinary
skill have not been described in detail so as not to obscure
claimed subject matter.
[0028] Some example methods, apparatuses, or articles of
manufacture are disclosed herein that may be implemented, in whole
or in part, to facilitate or support one or more operations or
techniques associated with a space-time-node engine signal
structure. As described below, a space-time-node engine signal
structure may be implemented using one or more signal processing
operations or techniques, such as, for example, one or more
space-time-node engine-facilitated or supported signal processing
operations or techniques. In this context, a space-time-node engine
may refer to a special purpose computing environment, platform, or
construct capable of formulating or executing tailored approaches
that may be based, at least in part, on processing signals as a
function of spatially, temporally, or nodally-dominant metrics or
attributes associated with these signals. In some instances,
space-time-node engine-facilitated or supported approaches may
comprise, for example, hardware, firmware, or software-implemented
approaches, or any combination thereof, and signals may comprise,
for example, multi-source signals, though claimed subject matter is
not so limited.
[0029] As used herein, "multi-source signals" may refer to one or
more digital signals, including multi-speed digital signals, for
example, that may be sampled or otherwise acquired, in real-time or
otherwise, from a variety of information sources in a variety of
formats. In this context, "real time" may refer to an amount of
timeliness of content or information, which may have been delayed
by an amount of time attributable to electronic communication as
well as other information or signal processing. A format may refer
to any suitable representation, medium, type, form, protocol, or
the like of any quantity, value, property, phenomenon, condition,
etc, associated with a signal and capable of being captured,
converted, communicated, conveyed, or otherwise sampled in some
manner in connection with one or ore space-time-node
engine-facilitated or supported signal processing operations or
techniques. Multi-source signals may be representative of any type
or category of information, such as, for example, electric grid
information, smart meter information, social media information,
environment information, business intelligence (BI) information,
navigation or positioning information, radio-frequency
identification (RFID) or credit card information, resource planning
or asset management information, enterprise performance
information, traffic congestion or toll collection information, or
the like. As will be seen, multi-source signals may be sampled or
acquired using any suitable communications framework that may
employ one or more special purpose computing platforms, software
application programming interfaces (API), communication protocols,
subscriptions or feeds, such as Open Database Connectivity (ODBC)
interface, Real Simple Syndication (RSS) or Atom Syndication
(Atom)-based subscription feeds, or the like. Of course, details
relating to multi-source signals are merely examples, and claimed
subject matter is not limited in this regard.
[0030] As described below, multi-source signals representative of
various information of interest may be used, in whole or in part,
to facilitate or support better decision-making, business-related
or otherwise, to perform desired-practice analyses, assess
regulatory exposure risks, leverage or manage enterprise
uncertainties, improve supply-demand economics, develop dynamic
pricing mechanisms, or the like. To illustrate, in the renewable
energy sector, utility companies, independent system operators
(ISO), regional transmission organizations (RTO), or the like may,
at times, face deadlines to integrate mandated amounts of renewable
energy, such as wind or solar energy, for example, into an
electricity transmission grid. Integration difficulties may
include, for example, unpredictability or intermittency of
renewable power, such as changing wind or solar patterns, balancing
conventional and renewable energy sources, or the like. This may,
for example, produce supply-demand shortfalls or lead to
regulation, reliability, or market stability issues, among
others.
[0031] To address these or other potential issues, information of
interest, such as, for example, environment or enterprise-related
information or the like may be gathered or acquired in some manner.
In some instances, it may be desirable to gather or acquire
information from a variety of sources in a variety of formats, for
example, so as to broaden or expand an assessment field, analytical
or business insights, sector or domain applicability, or the like,
as previously mentioned. Information of interest may be gathered or
processed in a certain manner and may be used, for example, to
provide location-specific, contextually-intelligent, or otherwise
tailored approaches so as to reduce imbalances attributed to
environmental intermittency, increase predictability of renewable
energy production, enhance renewable energy reliability, or the
like. In addition, in some instances, it may be desirable to
provide a timely analysis so as to facilitate or support, for
example, preventive maintenance, condition-based or otherwise,
selecting suitable environmental sites for future wind or solar
energy farms, or the like. It should be noted, however, that
claimed subject matter is not limited to these particular examples,
of course.
[0032] As was indicated, sampling or processing multi-speed signals
originating from a variety of sources in a variety of formats may,
however, present a number of challenges. In some instances,
challenges may include, for example, increased complexity or
processing time, computational or storage cost, requisite
processing power, or the like. As a way of illustration, locating
or retrieving information to address multi-dimensional or
analytical queries having, for example, n-dimensional attributes
may typically, although not necessarily, involve performing a
number of relatively complex or otherwise time-consuming
calculations. For example, at times, processing n-dimensional query
attributes may involve Boolean pruning by drilling/rolling
operators in connection with distance calculations for
multi-dimensional space. By way of example but not limitation, a
query may include "How much revenue from greeting cards was
generated last Christmas around the San-Francisco Bay Area?" In
this illustrated example, "greeting cards" may represent one
dimension (e.g., node-related or nodal, etc.), "Christmas" may
represent another dimension (e.g., time-related or temporal, etc.),
and "San-Francisco" may represent yet another dimension (e.g.,
location-related or spatial, etc.) within a multi-dimensional
relationship characterized by the query. User preferences are
typically, although not necessarily, dynamic, meaning that
user-specified dimensions may not be known until a query time;
thereby, pre-computing or pre-organizing information from potential
user preferences may be an expensive task, computationally or
storage-wise. Accordingly, it may be desirable to develop one or
more methods, systems, or apparatuses that may implement efficient
or effective processing or organizing of information so as to at
least partially anticipate user preferences, for example, which may
facilitate or support faster information analysis, retrieval,
reporting, presentation, etc or any combination thereof. Of course,
description of various dimensions is merely an example, and claimed
subject matter is not so limited.
[0033] FIG. 1 is a flow diagram illustrating a summary of an
example process 100 that may be implemented in connection with one
or more signal processing techniques including, for example,
multi-source signal processing. As seen, multi-source signal
processing may be implemented, in whole or in part, using, for
example, one or more space-time-node engine-facilitated or
supported signal processing operations or techniques. As described
below, multi-source signal processing may enhance or improve
information organization by utilizing, for example, a
space-time-node engine signal structure, which may help in
information analysis, retrieval, reporting, presentation, or the
like. It should be noted that information acquired or produced,
such as, for example, input signals, applications, output signals,
operations, results, etc. associated with example process 100 may
be represented by one or more digital signals. It should also be
appreciated that even though one or more operations are illustrated
or described concurrently or with respect to a certain sequence,
other sequences or concurrent operations may also be employed. In
addition, although the description below references particular
aspects or features illustrated in certain other figures, one or
more operations may be performed with other aspects or
features.
[0034] As illustrated, at operation 102, one or more signals
representative of information of interest may be acquired or
sampled from a variety of sources in a variety of formats using one
or more signal acquisition devices, schematically represented
herein as Sensor 1, Sensor 2, Sensor 3, and so forth up through an
Nth Sensor, as referenced generally at 104. As used herein, the
term "sensor" is to be interpreted broadly and may refer to any
type of a device or system, including a special purpose computing
platform, for example, capable of measuring or registering a signal
sample value, quantity, phenomenon, condition, state, or like
property that may be associated with a signal or signal acquisition
point. In some instances, a sensor may, for example, condition or
convert an incoming or sampled signal into one or more digital
numeric signal sample values for processing or manipulating in some
manner by an associated computing platform. Also, at times, a
sensor may be capable of communicating a measured or registered
signal sample value, quantity, phenomenon, condition, state, etc.
to another sensor for further communicating, processing,
conditioning, converting, or the like in a suitable or desired
manner. By way of example but not limitation, a sensor may comprise
any signal acquisition point associated with, for example, an
electricity transmission system or grid, Global Positioning System
(GPS), supervisory control and data acquisition (SCADA) system,
environmental system, BI system, asset or work order management
system, etc. as well as various instrumentations associated with
structured or unstructured information, such as electronic
documents, RSS or Atom feeds, social media platforms or blogs,
customer or service systems, smart cards or meters, etc. or any
combination thereof. It should be appreciated that a signal
acquisition point may also include an embedded reference or
hyperlink to images, audio or video files, or other documents. For
example, one type of reference that may be embedded in a document
and used to identify or locate other documents may comprise a
Uniform Resource Locator (URL). As a way of illustration, a signal
acquisition point may sample or communicate a signal in, connection
with a status update, an e-mail, an Extensible Markup Language
(XML) document, a web page, a blog, a media file, a page pointed to
by a URL, just to name a few examples.
[0035] It should be appreciated that in certain implementations one
or more signal acquisition devices 104 may comprise, for example,
post-signal acquisition devices, meaning that one or more
continuous sampled signals, if any, may be momentized in some
manner or broken into discrete or finite moments prior to
acquisition using appropriate techniques. One or more signal
acquisition devices 104 may also feature a time-keeping unit, such
as a GPS-enabled atomic clock, for example, to facilitate or
support suitable or desired event synchronization, just to
illustrate one possible implementation. For example, event
synchronization may help to identify when or where a sampled signal
was created, processed, acquired, etc., thus, allowing for
relatively accurate information acquisition in time, space, or
node. In some instances, such as in the absence of a
synchronization capability between a source and a recipient, for
example, a time or place of signal acquisition may be considered as
the time or place of actual event to be analyzed, forecasted,
leveraged, reported, or the like.
[0036] At operation 106, one or more sampled signals may be
processed in some manner by a signal pre-processor 108, for
example, to produce a suitable or desired signal input/output form,
if desired. For example, one or more sampled signals may be
converted, compressed, time-synchronized, or the like so as to
reduce or eliminate noise, transmission errors, distribution load,
etc. with respect to one or more signal acquisition channels or
links associated with devices 104, such as channels or links 110.
It should be appreciated that even though one pre-processor 108 is
illustrated herein, any number of pre-processors may be utilized to
facilitate or support one or more processes at operation 106. It
should also be noted that pre-processor 108 may be optional in
certain example implementations.
[0037] With regard to operation 112, sampled multi-source signals
may be collected or aggregated in some manner. As alluded to
previously, sampled signals may be communicated from a variety of
sources in a variety of formats using any suitable or desired
communication protocols, such as, for example, HyperText Transfer
Protocol (HTTP), Hypertext Transfer Protocol Secure (HTTPS),
Inter-Control Center Communications Protocol (ICCP), User Datagram
Protocol (UDP), File Transfer Protocol (FTP), Simple Mail Transfer
Protocol (SMTP), etc., or any combination thereof. It should be
noted that in certain implementations, such as in implementations
where a sequence or chronology of incoming information samples
(e.g., packets, etc.), if at all, may be relatively important or
otherwise useful, the Internet Protocol Suite, such as, for
example, Transmission Control Protocol/Internet Protocol (TCP/IP)
or like multi-layer protocol may be employed. Accordingly, to
account for particularities of signals originating from a variety
of information sources in a variety of formats, example process 100
may include an aggregation or concentration point, realized herein
as a signal aggregator or concentrator 114, for example. Signal
aggregator 114 may collect, summarize, normalize, de-normalize,
etc. incoming multi-source signals to facilitate or support
suitable or desired signal processing, such as same or similar
signal structure or format recognition, for example, or may perform
other formatting or filtering operations, depending, at least in
part, on an application. Of course, a description of signal
aggregator 114 is merely an example, and claimed subject matter is
not so limited.
[0038] Having aggregated multi-source signals in a suitable or
desired manner, at operation 116, a sampled signal may be
partitioned in some manner so as to facilitate or support further
processing, such as, for example, processing based, at least in
part, on a particular metric or attribute associated with a signal,
as will be seen. For example, a sampled signal may be formatted in
some manner to arrive at one or more information structures having
one or more attributes. In some instances, signal attributes may be
stored, at least in part, as signal sample values in associated
registers, though claimed subject matter is not so limited. Signal
attributes may be representative of one or more characteristics or
aspects associated with a sampled signal, such as, for example,
spatial, temporal, nodal, communication, security, or like
characteristics, or any combination thereof, as will also be seen.
In certain implementations, a register may, for example, comprise
an 8-bit wide register having signal sample values represented via
a byte code indicative of how a particular information structure is
to be processed or otherwise may be processed so as to be useful.
Particular examples of various information structures, associated
attributes, as well as sample register signal values will be
described in greater detail below. Register signal values may be
assigned to a sampled information structure, formatted or
otherwise, using any suitable or desired techniques, such as, for
example, by utilizing a function or class-type driver or adapter.
An adapter may provide, for example, a suitable or desired
interface between different levels or layers of an architecture or
a part of an architecture associated with example process 100. In
some instances, an adapter may be primarily utilized in connection
with a signal acquisition layer, for example, so as to facilitate
or support a common format, protocol, or the like, though claimed
subject matter is not so limited. An adapter may be written or
implemented, in whole or in part, by a vendor associated with a
signal acquisition point, such as one or more sensors 104, for
example, or by a special purpose computing platform associated with
a recipient of a sampled signal, or any combination thereof, as
illustrated below. A vendor may comprise, for example, an
individual person or entity (e.g., a customer, etc.) that may be
capable of participating in one or more operations associated with
example process 100 or may benefit, directly or indirectly, from
such participation. In some instances, a vendor may provide a
suitable level of interoperability between one or more systems,
platforms, device, etc. associated with example process 100, such
as a source system, a recipient system, a target system, etc., or
any combination thereof.
[0039] An incoming or sampled signal may be partitioned using any
suitable or desired signal decomposition process or technique. For
example, a sampled signal may be partitioned by a system of
registers for packet decomposition 118 based, at least in part, on
one or more attributes associated with the signal. In one
implementation, system of registers 118 may comprise a set of
digital logic gates, such as digital "OR" gates, for example,
implementing a number of logical disjunction operations, though
claimed subject matter is not so limited. In some instances,
logical operations may be expressed by a truth function utilizing a
valuation-type {true, false} logic or operator based, at least in
part, on a binary representation of register signal values
associated with incoming information structures. For example, a
sampled information structure comprising a certain register signal
value (e.g., [00001001] or [9], etc.), which may be pre-assigned or
otherwise dynamically specified or characterized in some manner,
may indicate that the structure includes a payload information and
a nodal attribute. Based, at least in part, on this register signal
value, a particular operator, such as a {9 OR 8=true} operator, for
example, may be triggered or invoked to process an incoming
structure accordingly, such as by respective payload and nodal
registers. Likewise, if a sample register signal value corresponds
to [2], it may be logically determined that an incoming signal may
benefit from spatial-type processing, as another possible example.
It should be noted that in certain implementations, a relation
instead of or in addition to an actual signal sample value (e.g.,
binary, real, etc.) may be used. For example, a relation may be
expressed as a literal like [t=t+1 min] to indicate that a temporal
stamp is to be increased by 1 minute, thus, allowing a system to
parse an initialization file or read contents accordingly.
[0040] Sample register signal values may, for example, be assigned
or specified in some manner, such as by a suitable or desired
adapter specifying classes or properties to be loaded to run (e.g.,
programmatically execute, etc.) a particular process or operation.
In certain implementations, applicable classes or properties may be
written into or otherwise dynamically specified or characterized in
some manner in any suitable or desired configuration or
initialization-type file. By way of example but not limitation, the
way a particular signal may be processed in connection with a
certain operation may be specified, at least in part, in a manifest
file (e.g., MANIFEST.MF, etc.) or INI-type file (e.g., ".INI",
".CFG", ".conf", ".TXT", etc.), though claimed subject matter not
so limited. A particular example of a manifest file that may be
utilized in connection with example process 100 may include one
illustrated in FIG. 4. Of course, details relating to signal
decomposition techniques, register signal values, or manifest
classes or properties are merely examples, and claimed subject
matter is not limited in this regard. Also, while illustrated in
this example as being separate from sensors 104, it should be
appreciated that system of registers 118 may be provided as part of
a signal acquisition point or, optionally or alternatively, as part
of a computing platform or environment associated with a recipient
of a sampled signal, such as a space-time-node engine, for
example.
[0041] At operation 120, one or more training models may be built
or applied, for example, so as create one or more trained signal
datasets. Trained signal datasets may be advantageously utilized in
connection with one or more signal processing layers or platforms,
such as analysis, organization, presentation, etc., for example, to
facilitate or support more effective or efficient information
indexing, analyzing, retrieving, etc., or any combination thereof.
As will be seen, training approaches nay include, for example,
clustering-based training, correlation-based training, evolutionary
or genetic algorithm or process-based training, just to name a few
examples. In some instances, training may employ one or more
stochastic learning techniques, such as regression, clustering,
frequency or population distribution, etc. using, at least in part,
historical samples, derivatives of historical samples,
degree-of-separation models, or the like. In one particular
implementation, prior to or concurrently with training, sampled
information structures may be pseudo-randomized in some manner,
such as via an application of a Brownian motion-type process, for
example, to obtain one or more statistically suitable sample sets
of signal data or information points.
[0042] As will also be seen, information points may, for example,
be descriptive of one or more dominant attributes associated with
sampled signals and may help to assess or learn relationships,
strengths of relationships, or lack thereof across information
structures of interest. In certain implementations, one or more
dominant attributes of sampled signals may, for example, be
determined based, at least in part, on an application of a
dominance principle. As used herein "dominance principle" may refer
to a process or technique of determining a degree of cardinality
with respect to one or more attributes of one or more information
structures associated with one or more sampled signals. By way of
example but not limitation, one or more dominant attributes may
comprise, for example, spatially, temporally, or nodally-dominated
value-based metrics that may influence one or more characteristics
of sampled signals and, as such, may affect signal processing,
organizing, storing, or the like. Particular examples of an
application of a dominance principle will be described in greater
detail below with reference to FIG. 5.
[0043] With respect to operation 122, information structures
representative of various sampled signals acquired or obtained from
a variety of sources in a variety of formats may be organized in
some manner so as to allow, for example, for more effective or
efficient information accessing, analyzing, retrieving, etc., or
any combination thereof. For example, information structures
reflecting possible or potential preferences (e.g., user
preferences, etc.) in connection with providing analytics or
addressing multidimensional analytical queries, among other
approaches, may be organized as a function of proximity or mutually
relative distance in a linearized in-memory array in at least one
embodiment. This may facilitate or support faster information
accessing, retrieving, presentation, etc., or any combination
thereof, as previously mentioned. More specifically, in an
implementation, multi-dimensional aspects of sampled signals may be
represented, for example, via a number of n-dimensional signal
vectors that may be mapped or transformed to, or otherwise
conditioned in some manner for a two-dimensional space, as an
example embodiment. Subsequently, sample signal values may be
linearized based, at least in part, on distance by performing a
memory sweep with respect to one or more dominant attributes of a
sampled signal identified or determined, for example, in accordance
with a dominance principle. Accordingly, based, at least in part,
on space-time-node-facilitated or supported signal processing,
information structures may be intelligently organized so as to
comprise, for example, a space-time-node engine signal structure,
as previously mentioned. Particular examples of one or more
operations in connection with a memory sweep will be described in
greater detail below. In addition, although not shown, at operation
122, information structures may be communicated to one or more
high-speed computing clusters or massive storage entities, such as
general-purpose graphics processing units (GP-GPU), MapReduce
clusters, or the like. Of course, details relating to
linearization, memory sweep, or possible approaches are merely
examples, and claimed subject matter is not so limited.
[0044] At operation 124, results with respect to information of
interest, such as trend analysis, performance management, market
research, situational awareness, supply-demand economics, or the
like may be presented in a suitable or desired visual
representation form. For example, information of interest obtained
from a variety of sources in a variety of formats may be correlated
in some manner to provide an ergonomic or easy-to-use interface
experience in the form of an analytical tool or framework helping
to visualize various spatia-temporal-nodal relationships. In some
instances, results may be provided so as to visually represent
certain spatia-temporal-nodal relationships in a manner that may
facilitate or support deriving or drawing particular inferences,
conclusions, or the like that may assessed, in whole or in part, in
connection with such a relationship, just to illustrate one
possible implementation. By way of example but not limitation,
various map-based interfaces, such as geo-spatial flow charts or
diagrams, dashboard-based presentations, or like may be employed in
connection with operation 124. Of course, claimed subject matter is
not limited to these presentation techniques.
[0045] Accordingly, in certain implementations, example process 100
may be conceptually represented as comprising, for example, a
number of space-time-node engine-facilitated or supported signal
processing layers, which may correlate with or correspond to one or
more operations described above, such as signal information
acquisition layer 126, training or preparation layer 128, internal
organization layer 130, or result presentation layer 132. Of
course, details relating to various layers, layer sequences, as
well as the number of layers shown in connection with example
process 100 are merely examples, and claimed subject matter is not
limited in this regard.
[0046] With this in mind, attention is now drawn to FIG. 2, which
comprises a flow diagram of an example process 200 that may be
implemented, in whole or in part, in connection with a
space-time-node engine signal structure. Again, it should be
appreciated that even though one or more operations are illustrated
or described with respect to a certain sequence, other sequences
including, for example, concurrent operations may also be employed.
In addition, although the description below references particular
aspects or features illustrated in certain other figures, such as
FIG. 1, 7, or 9, for example, one or more operations may be
performed with other aspects or features,
[0047] As illustrated, example process 200 may begin at operation
202 with sampling or acquiring one or more digital signals
representative of information originating from a variety of sources
in a variety of formats. As previously mentioned, sampled signals
may be momentized or broken into discrete or finite moments prior
to acquisition using appropriate techniques. As will be seen,
momentized signals may be characterized by a number of
n-dimensional signal vectors that may be used, at least in part, to
determine a cardinality of one or more dominant attributes
associated with the signals. As was also indicated, a sampled
signal may be formatted in some manner, such as by utilizing a
function or class-type driver or adapter, for example, to
facilitate or support a certain type of formatting, such as a
space-time-nodal-type formatting, though claimed subject matter is
not so limited. In certain implementations, such an adapter may
comprise, for example, a space-time-nodal adapter, and an
information structure may comprise, for example, a space-time-nodal
grain (STING).
[0048] As previously mentioned, a STING adapter may be implemented,
in whole or in part, by a vendor associated with a signal
acquisition point utilizing a class or Unified Modeling Language
(UML) type diagram that may be made available. Optionally or
alternatively, a STING adapter may be implemented, for example, by
a special purpose computing platform associated with a recipient of
a sampled signal, such as a space-time-node engine. For this
example, various classes or properties may be statically or
dynamically specified or characterized in some manner, such as via
an ecosystem file created by an ecosystem configuration manager.
More specifically, a designer-type entity, such as an ecosystem
designer, for example, which may or may not be associated with a
recipient of a sampled signal (e.g., a space-time-node engine,
etc.) may define or characterize via an applicable ecosystem file
one or more types of sources or attributes that may be used or
supported, business objects or artifacts that may be formatted,
constraints or indexing strategies that may be considered, etc., or
any combination thereof. Claimed subject matter is not so limited,
of course. For example, a STING adapter may comprise a product of
collaboration between any suitable parties associated with a source
or recipient of a sampled signal, for example, or any suitable
third party, depending, at least in part, on implementation. By way
of example but not limitation, a class or UML-type diagram that may
be utilized, in whole or in part, in connection with a
space-time-nodal-type formatting may include an example as
illustrated in FIG. 15. It should be appreciated that one or more
classes, properties, attributes, relationships, etc. shown are
merely examples to which claimed subject matter is not limited.
[0049] Turning now to FIG. 3A, a schematic illustration of an
implementation of an example information structure 300 in a
space-time-nodal grain or STING format is shown. In this context,
"STING," "STING cell," STING signal structure," or the plural form
of such terms may be used interchangeably and may refer to an
information structure comprising one or more attributes related to
or associated with a sampled signal. In one particular
implementation, an information structure, such as STING cell 300,
for example, may be conceptually represented as comprising a number
of stores or sections with signal-related aspects or attributes. As
illustrated, STING cell 300 may comprise, for example, spatial or
locational attributes 302, temporal attributes 304, nodal
attributes 306, payload or information section 308, communication
attributes 310, or security attributes 312. It should be
appreciated that even though a certain number or types of signal
related aspects or attributes are illustrated herein, any number or
type of signal-related aspects or attributes may comprise STING
cell 300.
[0050] For this example, spatial or locational attributes 302 may
represent one or more space or location-related characteristics
associated with a sampled signal. In some instances, spatial
attributes may include, for example, one or more latitude or
longitude signal sample values identifying a source or origin of a
sampled signal, a geo-coded reference or literal descriptive of a
signal acquisition point or source, or the like. One possible
format for a geo-coded reference may include, for example,
"Fremont, Calif., 94555," and an example of a literal may comprise
a geo-hash like "9q8yyk8yuv5xw." In an implementation where a
literal May be used to spatially annotate an information structure,
such as STING cell 300, for example, a pre-negotiation operation
between a sender of a signal and its recipient may establish a
geocode factory using appropriate techniques, for example, so as to
obtain source-related geographic coordinates. One possible example
of a geocode factory may include, for example, a gazetteer service,
though claimed subject matter is not so limited, of course. Geocode
factories are known and need not be described here in greater
detail.
[0051] Temporal attributes 304 may comprise, for example, one or
more time-related characteristics associated with a sampled signal.
For example, temporal attributes 304 may comprise a time stamp
denoting a time, date, etc. at which a signal was created, which
may typically, although not necessarily, be in the form of a range.
Here, a temporal attributes 304 may be relatively fine-grained,
meaning that associated sample signal values may support, for
example, up to a microsecond granularity, though claimed subject
matter is not so limited. As previously mentioned, a source and a
recipient of a sampled signal, such as a space-time-node engine,
for example, may utilize atomic clocks to facilitate or support
time-related synchronization to eliminate or reduce sampling
ambiguity. In one particular implementation, a source and a
recipient may feature GPS-enabled clocks capable of maintaining,
for example, microsecond-level synchronization, at least
approximately.
[0052] Nodal attributes 306 may comprise, for example, one or more
node-related characteristics associated with a sampled signal. For
example, nodal attributes 306 may comprise one or more signal
sample values descriptive of a relationship that is typically,
although not necessarily, relational rather than dimensional. By
way of example but not limitation, a nodal relationship may
comprise, for example, a sociogram or graph-type relationship.
Accordingly, nodal attributes 306 may describe or identify a
particular node in a graph associated with a sampled signal. For
purposes of explanation, a nodal relationship between information
sources of interest may be conceptualized, for example, via an
analytical query like "How many greeting cards were sold by
Hallmark Cards, Inc. stores in San Francisco and San Jose?" For
this example, different Hallmark stores in San Francisco and San
Jose may represent different nodes in a linked relationship (e.g.,
Hallmark stores, etc.) that may be advantageously captured by an
information structure, such as STING cell 300, for example. Nodal
attributes 306 may comprise, for example, a signal sample value
identifying a particular node (e.g., a particular store in San
Francisco, etc.) in an interlinked hierarchy of nodes (e.g.,
different Hallmark stores in different cities, etc.) associated
with a sampled signal. Of course, details relating to particular
nodes are merely examples, and claimed subject matter is not so
limited.
[0053] Payload 308 may comprise, for example, one or more signal
sample values representing information of interest, such as
information to be analyzed, visualized, forecasted, leveraged, etc.
or any combination thereof, associated with a signal acquisition
point or source. As previously mentioned, payload 308 may include,
for example, various performance, market, management, operations,
business-related information, or the like.
[0054] Communication attributes 310 may comprise, for example, one
or more signal sample values representative of communication or
network-related aspects of a sampled signal. For example,
communication attributes 310 may be descriptive of an underlying
communication infrastructure associated with a signal acquisition
point, may provide source or destination addresses, bandwidth,
latency, error detection checksums, transmission loss, channel
noise, sequencing information, or the like.
[0055] Security attributes 312 may comprise, for example, one or
more signal sample values representative of security-related
aspects associated with a sampled signal. For example, security
attributes 312 may be descriptive of sensitive or personal
information with respect to a signal acquisition point, such as an
identity of a source, source or signal ownership information,
whether information is to be protected, access privileges, or the
like. In other words, security attributes 312 may facilitate or
support protection of information associated with an information
structure, such as STING cell 300, for example, from unauthorized
access, use, disclosure; modification, interception, destruction,
or the like.
[0056] As was indicated, one or more attributes associated with
STING cell 300 may be optional in certain example implementations,
which may depend, at least in part, on a signal acquisition point
or source. As a way of illustration, if a source of a sampled
signal is associated with a navigation or positioning system, such
as a GPS device that may change its location sporadically,
frequently, or periodically, a STING cell may omit a nodal
attribute to comprise, for example, a spatial or temporal
attribute, among others (e.g., a payload, security, etc.).
Likewise, as an example, a signal acquisition point realized as a
thermostat may be useful for sampling information with respect to
space or time, for example, but less useful with respect to
node-related attributes since thermostats are typically, although
not necessarily, stationary devices. As another possible example,
since typically, although not necessarily, there is no spatial
dimension with respect to a signal acquisition point associated
with a BI system, for example, locational or spatial attributes of
a STING cell may be omitted, depending, at least in part, on an
embodiment.
[0057] In addition, as will be described in greater detail below,
such as in connection with an application of a dominance principle,
for example, one or more attributes associated with a STING cell
may dominate over one or more other one or more attributes.
Dominant attributes of sampled signals may, for example, be
dependent upon or attributable to, at least in part, a rate of
change in a payload of a sampled signal with respect to time,
space, or node, as will be seen. Accordingly, size or so-called
granularity of a store or section (e.g., temporal, nodal, spatial,
etc.) associated with a STING cell may be adjusted in some manner.
By way of example but not limitation, for slower-moving signals,
such as GPS-type signals, for example, at times, a nodal attribute
may be less useful than, for example, one or more spatial or
temporal attributes, as indicated above. As such, GPS-related STING
cells may omit nodal attributes to continually communicate spatial
or temporal attributes (or both), in which case, granularity of a
nodal store or section may be increased to reflect one or more
dominant attributes. As another example, higher-speed signals, such
as a fast Fourier' transform (FFT) of an alternating current (AC)
signal, for example, may exhibit temporal dominance, thus,
reserving or allocating higher granularity for a store or section
with time-related characteristics. Examples of STING cells having
one or more dominant attributes are illustrated in FIG. 3B. As
seen, stores or sections with a higher number of bytes may be
reserved for or allocated towards a particular dominant attribute,
if any, as schematically represented via a larger area of a section
or store. Of course, details relating to one or more dominant
attributes are merely examples and claimed subject matter is not so
limited. In addition, although not shown, STING cells of FIG. 3B
may comprise other signal-related attributes, such as security
attributes, for example.
[0058] In an implementation, sampled STING cells may be linked in
some manner so as to facilitate or support more efficient or
effective processing of a continual signal stream of STING cells.
For example, STING cells may be committed to one or more STING
clusters based, at least in part, on a payload-to-byte ratio,
average number of STING cells per payload, or the like. Any
suitable or desired format may be utilized. As a way of
illustration, a STING cluster may be formatted, for example, as an
information or signal data string (e.g., coma-separated, etc.) in
connection with an Extensible Markup Language (XML) document, or
the like, depending, at least in part, on an implementation. By way
of example but not limitation, one possible format suitable for
commitment of STING cells to a cluster may comprise an example
represented in Table 1 below. Consider, for example:
TABLE-US-00001 TABLE 1 Example STING cluster format. Data stream
-> y = x{circumflex over ( )}2; t = t + 1; {[2],
67.0009,-117.67,[4] 7.00am, [1]
{65,OFF,80%},{67,OFF,77%},{68,OFF,75%},{69,OFF,74%},
{70,OFF,78%},{71,OFF,80%},{72,OFF,82%},{72,ON,45%}, {73,ON,43%},
117.14.56.34 , 67kb/sec} + {[2], 67.0009,-117.67,[4] 7.00am, [1]
{65,OFF,80%},{67,OFF,77%},{68,OFF,75%},{69,OFF,74%},
{70,OFF,78%},{71,OFF,80%},{72,OFF,82%},{72,ON,45%}, {73,ON,43%},
117.14.56.34 , 67kb/sec} + {[2], 67.0009,-117.67,[4] 7.00am, [1]
{65,OFF,80%},{67,OFF,77%},{68,OFF,75%},{69,OFF,74%},
{70,OFF,78%},{71,OFF,80%},{72,OFF,82%},{72,ON,45%}, {73,ON,43%},
117.14.56.34 , 67kb/sec}
[0059] As seen, a suitable format may comprise, for example, a
string of STING cells clustered via a "+" notation, wherein a
string may comprise one or more signal sample values acquired or
sampled in connection with a signal acquisition point. For purposes
of illustration, a signal acquisition point may be realized as a
thermostat, though claimed subject matter is not so limited. For
example, a STING cell may comprise a spatial attribute
characterized via a register signal value [2] with latitude
longitude signal values of 67.0009, -117.67, a temporal attribute
characterized via a register signal value [4] of 7.00 am, a payload
characterized via a register signal value [1] represented as a
string of signal values with respect to a temperature (e.g., 65,
etc.), operating state (e.g., OFF, etc.), or energy efficiency
(e.g., 77%, etc.) of a thermostat, or:
{65,OFF,80%},{67,OFF,77%},{68,OFF,75%},{69,OFF,74%},{70,OFF,78%},
[0060] and a communication attribute comprising an IP address
(e.g., 117.14.56.34, et and an information transfer rate (e.g., 67
kb/sec, etc.).
[0061] As previously mentioned, instead of a signal value (e.g.,
binary, real, etc.) to indicate a type of processing to be applied
(e.g., spatial, temporal, etc.), such as, for example, at operation
116 of FIG. 1, a format may reference a relation, such as a
parabolic trajectory formulation y=x 2. This or other suitable
relations may be utilized, at least in part, in instances where
aspects of an incoming signal are sufficiently predictable, such as
signal deviations associated with a sine wave of an AC, for
example, which may be captured by an appropriate relation. If a
sampled signal is sufficiently predictable, a relation may be used
instead of or in addition to signal sample values, thus,
eliminating or reducing sampling a signal stream of STING cells in
a continual fashion. This may provide benefits in terms of reducing
a processing time or complexity, improving performance, etc., or
any combination thereof.
[0062] In addition, a script, such as, for example, <1++> may
also be utilized to indicate a suitable processing increment in
connection with sampling a signal, as illustrated. A script may be
useful for processing temporal attributes, for example, so as to
invoke or trigger processing in accordance with time increments,
such as 1, 2, 3, etc. seconds, minutes, etc., as was also
indicated. To illustrate, a temporal attribute, such as time stamp,
for example, may be increased by 1 second, minute, etc. based, at
least in part, on a [t=t+1______] formulation that may be specified
for an information stream. Accordingly, instead of or in addition
to {1997-07-16T19:20+01:00} notation, other formats may also be
considered: {1997-07-16T19:<1++>+01:00}.
[0063] As mentioned above, size of a STING cluster may be
characterized, at least in part, in a manifest file by specifying,
for example, a payload-to-byte ratio of sampled STING cells, just
to illustrate one possible implementation. FIG. 4 illustrates an
example manifest file 400 that may be utilized or otherwise
considered, in whole or in part, in connection with one or more
operations associated with example process 200. It should be
appreciated that manifest file 400, which is self-explanatory, is
provided herein by way of a non-limiting example and may comprise,
for example, information specifying or characterizing one or more
classes, extensions, packages, etc., or any combination thereof.
Briefly, in this illustrated example, manifest file 400 may
comprise a number of field name entrances which may be referenced
to facilitate or support one or more operations with respect to
signal structures, training, indexing, processing, or the like. For
purposes of explanation, one or more classes, extensions, packages,
etc. may be specified in some manner, such as by a vendor, device,
ecosystem configuration manager (e.g., in the ecosystem file,
etc.), or any combination thereof, as previously mentioned. In
addition, manifest file 400 may specify a type or format that may
be used in connection with one or more operations, description of
corresponding functions, types of signal characteristics that may
be supported, as well as signal values that may be allowed for
respective processing, which may, of course, depend, at least in
part, on a particular application. In one implementation, a
payload-to-byte ratio of 400:10 or 40 may be used, such that a
STING cluster would be created if a ratio has been quantified or
otherwise observed, for example. Of course, descriptions relating
to payload-to-byte ratios or manifest file are merely examples, and
claimed subject matter is not limited in this regard.
[0064] Referring back to FIG. 2, at operation 204, it may be
determined whether negotiation between a signal acquisition point
or source and a recipient of a sampled signal is appropriate.
Typically, although not necessarily, negotiation may comprise a
process in which a computing platform associated with a source, for
example, may inform or negotiate with a computing platform of a
recipient on processing of one or more attributes of an incoming or
sampled signal, such as one or more attributes discussed above in
connection with FIG. 3A. If it is determined that negotiation is
appropriate, at operation 206, one or more negotiation tasks may be
performed. For example, a source may negotiate with a recipient
using applicable terms of negotiation by referencing any suitable
file supporting a negotiation-related process, such as manifest
file 400 of FIG. 4, for example. Negotiation-related fields may
correspond or be mapped in some manner to a Universal Unique
Identifier (UUID) embedded in an information structure, such as
STING cell 300, for example, so as to identify a structure. By way
of example but not limitation, terms of negotiation may include
those listed in negotiation-related fields in manifest 400, though
claimed subject matter is not so limited. Source-recipient
negotiation techniques are generally known and need not be
described here in greater detail.
[0065] At operation 208, a determination may be made regarding
whether one or more sampled STING cells are to be trained. As
described below, based, at least in part, on training, one or more
trained signal datasets may be created and subsequently applied to
facilitate or support more effective or efficient information
indexing, organization, processing, visualization, reporting, or
the like. In an implementation, whether training is appropriate may
be determined, at least in part, by referencing training-related
fields, for example, in a manifest file, such as manifest file 400.
As was alluded to previously, training-related fields may be
specified by a vendor associated with a signal acquisition point or
source, for example, or otherwise dynamically specified or
characterized in some manner (e.g., by an ecosystem configuration
manager, etc.). If it is determined that training is appropriate or
otherwise may be useful, example process 200 may continue to
operation 210. On the other hand, if training is omitted, a process
may proceed to operation 234.
[0066] With regard to operation 210, a Gaussian filter may be
applied in some manner to a sampled stream of STING cells so as to
reduce or eliminate noise by attenuating, removing, or otherwise
ignoring, for example, one or more STING cells with spurious sample
signal values representative of incorrect or corrupted signals, for
example. As previously mentioned, a sampled signal may be
represented as:
S.sup.n.sub.i={x.sub.i,y.sub.i,z.sub.i,t.sub.i,N.sub.i,v1.sub.i,v2.sub.i
. . . }
where v1.sub.i, v2.sub.i denote value vectors, and i is a whole
number. Signal sample value vectors may account for properties of a
sampled signal associated with a signal acquisition point or source
and may comprise, for example, signal sample values representative
of temperature, pressure, etc., or any combination thereof. To
reduce or eliminate noise, a Gaussian filter may be applied such
that signal sample values with certain standard deviations (e.g.,
variations from average signal sample values) may be considered as
noise or error and, as such, may be eliminated, cut-off, or
attenuated. More specifically, for a signal value vector, standard
deviations .sigma..sub.i may be computed as:
.sigma. 1 = 1 n i = 1 n ( v 1 i - .mu. 1 ) 2 .sigma. 2 = 1 n i = 1
n ( v 2 i - .mu. 2 ) 2 ( 1 ) ##EQU00001##
where .mu..sub.1 and .mu..sub.2 denote respective means or
averages. By way of example but not limitation, in certain
simulation or experiments, a standard deviation value of 3 or
higher was used as a cut-off threshold and may prove beneficial in
educing or eliminating spurious values of a sampled signal.
Accordingly, as seen in FIG. 6, signal sample values behind
"suspicious zones" may be considered as noise or error and may be
cut-off, attenuated, or otherwise ignored. Of course, this is
merely an example of a threshold that may be used in connection
with an application of a Gaussian filter, and claimed subject
matter is not so limited.
[0067] At operation 212, one or more sample sets of information
points may be obtained or extracted for training. It should be
noted that any suitable extraction techniques may be employed. In
some instances, extraction techniques may, for example, depend, at
least in part, on a manner of how STING cells may be organized,
stored, processed, etc., or any combination thereof. For example,
in one implementation, a sample set may be extracted via a
pseudo-randomization technique, though claimed subject matter is
not so limited. A stream of STING cells may be directed into any
suitable in-memory appliance, such as a database, file memory
system, or like information repository, for example, and may be
stored as one or more sets of STING clusters, such as one or more
STING clusters illustrated in Table 1 above. A number of points
that may be extracted may depend, at least in part, on a total
number of available signal structures, such as, for example, STING
cells in a cluster. In some instances, a number of pseudo-random
points useful for training may, for example, be specified or
characterized in a manifest file as a percentage of a total sample
set. For example, based, at least in part, on a total number of
rows in a particular STING cluster, a number of sample information
points in a set may be computed by dividing a percentage of points
available for training by a number of rows in a cluster. By way of
example but not limitation, if there are 10 million in-memory rows,
5% of a total sample set may indicate that 500,000 rows may be
utilized for training. In one implementation, a database extraction
technique via pseudo-randomization may be performed by way of a
SELECT statement executed against a sample set stored in a
database, such that:
SELECT TOP 10000 UUID, NewID( ) as Random FROM STING_CLUSTER ORDER
BY Random
[0068] Accordingly, as an illustrated example, 10,000 random STING
cells would be extracted for training. Of course, this is merely
one possible example. Many extraction techniques other than
pseudo-randomization may also be utilized in connection with
operation 212.
[0069] With regard to operation 214, a dominance principle may be
applied in some manner to a set of STING cells representative of a
sampled signal. As previously mentioned, a dominance principle may
refer to a process of determining a degree of cardinality with
respect to one or more attributes associated with one or more
sampled STING cells. In some instances, a dominance principle may
be applied, for example, to understand the nature or relationships
between various incoming information structures associated with
sampled signals. In one implementation, a dominance principle may
be applied, for example, to determine which one or more attributes
among a number of attributes associated with sampled STING cells,
such as attributes discussed above in connection with FIG. 3A, for
example, may be dominant.
[0070] Attention is now drawn to FIG. 5, illustrating an
implementation of an example application of a dominance principle.
As previously mentioned, an incoming stream of STING cells
representative of a sampled signal may be momentized, for example,
into a number of n-dimensional signal vectors. For an example
implementation, momentized signal vectors may comprise, for
example, signal vectors S1 through S7, as illustrated generally at
502. As seen, a signal vector may be specified, for example, by a
number of dimensions or planes, indicated generally at 504,
corresponding to one or more attributes associated with a sampled
signal. For example, X, Y, and Z dimensions may correspond to
locational or spatial attributes, such as latitude, longitude, and
altitude, respectively; T dimension may correspond to temporal or
time-related attributes; and N dimension may correspond to nodal
attributes. As previously mentioned, a nodal relationship may
typically, although not necessarily, comprise relational features
rather than dimensional features, meaning that a nodal relationship
may be plotted or represented graphically, for example, as a
sociogram or graph-type relationship. Nodal attributes, thus, may
be specified or characterized, for example, in connection with a
nodal domain, such as nodal domain N, rather than a nodal
dimension. However, it should be noted that in this context the
term "nodal dimension" may be used interchangeably with the term
"nodal domain" so as to provide similar meaning. In an example, a
momentized sampled signal may be represented via a number of
n-dimensional vectors S(n)={X, Y, Z, T, N}. It should be noted that
Z or an altitude dimension may be optional in some example
implementations. Of course, these are merely examples of various
signal vectors, dimensions, or attributes that may be utilized in
connection with an application of a dominance principle, and
claimed subject matter is not so limited.
[0071] It may be observed that for S1, S2, and S3 signal vectors,
X, Y, Z, and N dimension or plane-related moments may not chance or
may change little. On the other hand, T plane-related moments may
change from t1 to t10, which may indicate that one or more
time-related attributes of one or more STiNG cells may dominate or
exhibit a higher degree of cardinality. Typically, although not
necessarily, a cardinality of a set may refer to a measure of a
number of elements of a set. For purposes of explanation, a set
A={2, 4, 6} includes 3 different elements and has a cardinality
score of 3. As also seen, signal vectors S4 to S7 may change in X
plane by a cardinality of a count of 3 (e.g., {x1, x3, x4, x3}),
and may also change twice or have a cardinality score of 2 (e.g.,
{t10, t15}) along T plane. Thus, a cardinality of a sampled vector
in space, time, or nodal dimensions may be determined, at least in
part, by counting a number of different elements represented via
changing moments in a set represented via dimensions or planes.
Accordingly, based, at least in part, on an application of a
dominance principle, it may be determined that signal vectors S1,
S2, and S3 may be at least temporally dominant (e.g., along T
plane), and that signal vectors S4, S5, S6, and S7 may be at least
spatially dominant along X plane. Of course, this is merely one
possible example of an application of a dominance principle to
which claimed subject matter is not limited. Any suitable
approaches, such as, for example, approaches utilizing bijective,
injective, surjective functions, cardinal numbers, etc. may also be
employed.
[0072] If it is determined that one or more attributes of a sampled
signal are dominant, at operation 216, a cardinality map in any
suitable format may be produced. Although claimed subject matter is
not so limited, a cardinality map may, for example, be initially
represented via a table, such as illustrated in FIG. 5 above.
Based, at least in part, on a representation, a suitable format for
a map to determine a degree or cardinality by identifying one or
more dominant attributes along, for example, spatial, temporal, or
nodal dimensions may include an example as illustrated in Table 2
below.
TABLE-US-00002 TABLE 1 Example format for a cardinality map.
X-cardinality = Select (distinct) X from Signal_samples where
signal vector between S1 and S7 Y-cardinality = Select (distinct) Y
from Signal_samples where signal vector between S1 and S7
Z-cardinality = Select (distinct) Z from Signal_samples where
signal vector between S1 and S7 T-cardinality = Select (distinct) T
from Signal_samples where signal vector between S1 and S7
N-cardinality = Select (distinct) graph_relationship(N) from
Signal_samples where signal vector between S1 and S
[0073] As previously mentioned, a nodal relationship may typically,
although not necessarily, comprise relational features rather than
dimensional features, and may be represented, for example, via a
sociogram or graph having a number of nodes connected together by
associational links or ties. To determine one or more dominant
attributes of a node or N-cardinality, samples of signals
originating from identifiable nodes of an existing graph, such as,
for example, a particular Hallmark store in San Francisco, as
discussed above, may be used. As seen in Table 2 above, in one
implementation, the notation "graph_relationship(N)" may be used to
specify a canonical relationship between nodes of a graph for an
N-related domain. In this context, a canonical relationship may
refer to some established or existing relationship in which nodes
are known, non-arbitrary, or otherwise identifiable. It should be
appreciated that a nodal relationship may be represented via a
directed graph, undirected graph, or any combination thereof. Of
course, details relating to determining N-related cardinality are
merely examples, and claimed subject matter is not limited in this
regard.
[0074] Referring now back to FIG. 2, at operation 218, it may be
determined whether a particular type of training, such as a
clustering-based training utilizing a k-means-type process, for
example, may be appropriate or may be useful. As previously
mentioned, training may be implemented so as to characterize or
understand the nature of one or more relationships between sampled
signals, which may facilitate or support more effective or
efficient signal processing, indexing, organization, or the like.
As was also indicated, whether training is appropriate or may be
useful, type of training, training intervals, or other
training-related processes may, for example, be characterized or
specified, in whole or in part, in a suitable configuration or
initialization-type file, such as manifest file 400, just to
illustrate one possible implementation.
[0075] Continuing with example process 200, if training may be
appropriate or otherwise may be useful, at operation 220, a process
may implement, for example, a clustering-based training. In some
instances, a clustering-based training may include, for example,
k-means clustering of STING cells to arrive at spatially,
temporally, or nodally-dominated cluster zones or clusters derived
from a sampled signal stream. For example, in an implementation, a
"k" value of 3 may be used, meaning that a cluster map of three
clusters, such as spatially, temporally, and nodally-oriented
clusters, for example, would be created. Clusters may be created,
for example, by partitioning STING cells represented via
n-dimensional signal vectors, such as vectors discussed above in
connection with FIG. 5, based, at least in part, on one or more
dominant attributes. By applying k-means-type clustering, a sampled
n-dimensional signal vector with one or more dominant attributes
may belong to a respective cluster with a nearest mean.
Relationships between sampled signal vectors may be determined
based, at least in part, on vector proximity, which may be computed
as a function of mutually relative distance between information
points comprising k-means clusters. As described below, distance
may be representative of, for example, one or more spatial,
temporal, nodal, etc. relationships between sampled signals, or any
combination thereof. In some instances, distance between sampled
signals may be representative via a Hamming distance, for example,
as will also be seen. However, these are merely examples of a
particular application of clustering-based training, and claimed
subject matter is not limited in scope in these respects.
[0076] If clustering-based training is omitted, then at operation
222 it may be determined whether correlation-based training may be
appropriate or otherwise may be useful by referencing, for example,
training-related fields in manifest file 400. If it is determined
that correlation-based training may be appropriate or useful,
example process 200 may continue to operation 224. In an
implementation, correlation-based training may employ, for example,
a Pearson's correlation coefficient to determine or learn one or
more relationships between incoming STING cells representative of
one or more sampled signals. For example, how sampled signals may
be related may be evaluated by identifying statistically relevant
trends or overarching similarities in signal trending with respect
to time. Accordingly, an approach may employ, for example, a rate
of change of sampled signal vectors over time or so-called signal
vector derivatives. More specifically, a Pearson coefficient for an
actual or forecasted signal stream may be respectively computed by
utilizing values of corresponding sampled n-dimensional signal
vectors and associated first, second, and third-order derivatives
to evaluate how signals correlate in time. In this context, a
second-order derivative may refer to a derivative of a first-order
derivative, a third-order derivative may refer to a derivative of a
second-order derivative, and so forth. Sample signal values with
higher degrees of correlation may be stored in memory as a trained
signal dataset, for example, and may be subsequently applied in
connection with one or more operations associated with example
process 200.
[0077] If is it determined that correlation-based training may be
less useful, example process 200 may proceed to operation 226 for
determination whether to implement evolutionary training. If yes,
at operation 228, evolutionary training may be performed. For
example, here, a concept of a genetic algorithm or process may be
advantageously leveraged or applied so as to facilitate or support
more effective or efficient characterization of a fitness function
to arrive at a faster, less arbitrary, or otherwise more successful
or useful conversion. In some instances, a fitness function may be
characterized, for example, by one or more proximity-based
parameters, such as a distance between signal vectors
representative of STING cells. As described below, a distance
between STING cells may be determined based, at least in part, on
one or more applicable distance calculators. More specifically,
during initialization, one or more chromosomes and their component
genes, for example, may be identified or generated. A gene may be
represented via one or more signal sample values in a binary,
string, numeric, etc. format and may be associated with a STING
cell's payload. By way of example but not limitation, a chromosome
may comprise an automatic temperature control system realized as a
thermostat having component genes specifying a thermostat's
operating state, temperature, ambient condition, or the like. An
initial set of STING cells (e.g., sampled from a thermostat, etc.)
may be collected as a population and organized in memory. A parent
set may be selected for reproduction in some manner. For example, a
parent set may be selected based, at least in part, on knowledge
assessed or evaluated from one or more training operations, such
as, for example, clustering-based training at operation 220,
correlation-based training at operation 224, or the like. Based, at
least in part, on an application of a proximity-based fitness
function, one or more new or child STING cells may be reproduced in
a manner so as to approach an improved or "ideal state" or reach an
improved or "optimal goal." One or more determinations may be made,
for example, by assessing or evaluating distance between a child
STING cell and an improved or "perfectly fit" or previously trained
(e.g., clustering-based, correlation-based, etc.) STING cell. Of
course, these details are merely examples relating to evolutionary
training, and claimed subject matter is not so limited.
[0078] If evolutionary training is omitted, a process may continue
to operation 230. Here, as a result of one or more training
operations, such as operations 220, 224 or 228 described above, for
example, one or more trained signal datasets may be created. It
should be noted that a trained signal dataset may be stored in any
suitable in-memory appliance, such as, for example, a database,
file memory system, or like information repository. As
schematically illustrated by dashed arrows at 232, a trained signal
dataset may be used or applied in connection with one or more
operations associated with example process 200. For example, in an
implementation, a trained signal dataset may be utilized, in whole
or in part, by an ecosystem configuration manager or like
application programming interface (API) so as to facilitate or
support more effective or efficient processing, indexing,
presentation, visualization, etc., or any combination thereof.
[0079] Although claimed subject matter is not limited in this
respect, a trained signal dataset may be utilized, for example, in
connection with delta processing at operation 246, outgoing signal
processing at operation 248, indexing at operation 242, or possibly
other operations associated with example process 200. For example,
if a trained signal dataset indicates that particular signals
correlate sufficiently well over time, similarly or like trending
incoming signals may be indexed or processed more intelligently.
Namely, if it is observed that two particular signals are trending
in a certain way, for example, then signal sample values for one
signal may be indexed or processed so as to conserve computational
resources, such as memory space, number of processing operations,
or the like, since signal values for a second signal would be
predictable, or otherwise ascertainable (e.g., from training,
etc.). Of course, this is merely one possible example of an
application of a trained signal dataset, and claimed subject matter
in so limited.
[0080] With regard to operation 234, it may be determined whether
one or more intermediate signals are missing or corrupted. Missing
or corrupted signals may, for example, lack an informational
component that may useful in evaluating sampled signals and may
lead or result in less effective or efficient analysis,
performance, processing, presentation, or the like. At operation
236, a Gaussian filter may be applied in some manner to eliminate
or reduce noise, such as, for example, in a manner described above
in connection with operation 210. Example process 200 may proceed
to operation 238, at which point an error approximation operation
may be performed. For example, if one or more space, time, node,
etc. related signal sample values associated with one or more STING
cells of a sampled signal are sing or otherwise corrupted, signal
sample values may be interpolated to arrive at a statistically
acceptable approximated values. In one implementation, a Shepard's
method may be utilized, for example, to approximate missing signal
sample values by applying weighted functions assigned to points in
a scattered set of sampled signal values based, at least in part,
on one or more dominant attributes associated with sampled STING
cells.
[0081] If no, on the other hand, example process 200 may proceed to
operation 240 for a determination regarding indexing for
information structures, such as, for example, sampled STING cells.
Indexing may typically, although not necessarily, be used to
facilitate or support more effective or efficient signal processing
by providing indexing options for faster look-up, access,
performance, retrieval, or the like using appropriate techniques.
If indexing is omitted, a process may by-pass operation 242 and may
continue at operation 244, However, if it is determined that
indexing may be appropriate or useful, at operation 242, STING
cells associated with a sampled signal may be indexed in some
manner based, at least in part, on one or more available indexing
options. By way of example but not limitation, indexing options or
strategies may be specified or characterized in a manifest file,
such as manifest 400, for example, as previously mentioned. It
should be appreciated that any suitable indexing techniques or
processes, such as k-means indexing, spatial query indexing,
tree-based indexing-type process, etc. may be used at operation
242, Various indexing techniques are known and need not be
described here in greater detail. For example, in an
implementation, indexing may be performed using one or more
commercial statistical libraries. Indexed information structures
may be stored in any suitable information repository, such as, for
example, in-memory cache, file cache, or the like. It should also
be noted that in an implementation, operation 242 may be optional,
in which case example process 200 may proceed from operation 240 to
operation 244.
[0082] At operation 244, n-dimensional information structures
associated with a sampled signal may be organized in some manner
into a system of STING cells. In some instances, a system of STING
cells may comprise, for example, a space-time-node engine signal
structure organized linearly in a suitable in-memory array. For
example, n-dimensional STING cells may be organized based, at least
in part, on one or more dominant attributes linearized as a
function of mutually relative distance by performing a memory sweep
with respect to one or more STING clusters having spatially,
temporally, or nodally-dominant information points. Examples of a
memory sweep so as to arrive at a linearized in-memory array will
be described in greater detail below with reference to FIGS.
7-13.
[0083] As referenced at 246, example process 200 may implement a
delta processing operation so as to compress one or more signal
sample values associated with sampled information strictures, such
as STING cells, for example, communicated over any suitable
communication channel or link in connection with example process
200. Delta processing operation 246 may be implemented, in whole or
in part, to reduce consumption of one or more process-related
resources, such as, for example, memory space, transmission
bandwidth, storage size, or the like. It should be appreciated that
even though delta processing operation 246 is illustrated in
connection with a certain processing sequence, such as following
operation 244, for example, delta processing operation 246 may be
implemented in connection with any suitable operation associated
with example process 200. Here, one or more types of compression,
such as lossy compression, lossless compression, or the like may be
employed. Compression techniques may help to eliminate or reduce
statistical or perceptual redundancy, facilitate or support signal
consolidation, etc. and may be based, at least in part, on an
application of approximation thresholds, loss of tolerance ratios,
or the like. By way of example but not limitation, types of delta
compression to be implemented or other compression-related
operations may be characterized or specified, at least in part, in
a manifest file, as was indicated.
[0084] With regard to operation 248, one or more STING cells
associated with a linearized in-memory array may be processed in
some manner so as to help, for example, with presentation of one or
more spatia-temporal-nodal relationships of interest. More
specifically, an outgoing signal processing may comprise, for
example, a process of a relatively rapid pass on linearized memory
locations for spontaneous de-serialization into a suitable output
format. In some instances, an output format may comprise, for
example, a raster or vector output format, which may aid in
visualization, reporting, presentation, as mentioned above.
Accordingly, outgoing signal processing may facilitate or support,
for example, a number of visual options for delivering real-time,
location-intelligent, context-relevant analytics capable of being
intuitively displayed using one or more interfaces associated with
computing platforms or devices, such as GPUs, animation
controllers, dashboards, or the like.
[0085] Referring now to FIGS. 7-13 illustrating an example
implementation of a memory sweep that may be performed, at least in
part, in connection with operation 244 of FIG. 2. To simplify
discussion, features or aspects of FIGS. 7-13 that may correspond
to same or similar features or aspects, for example, or otherwise
may be referenced in a similar fashion, are given the same
reference numbers, where applicable. As previously mentioned, a
memory sweep may facilitate or support effective or efficient
in-memory organization in general, and a space-time-node engine
signal structure in particular, depending, at least in part, on an
implementation. A space-time-node signal structure may, for
example, enhance or improve information analysis, retrieval,
reporting, presentation, or the like, as previously mentioned.
[0086] FIG. 7 is a flow diagram illustrating an implementation of
an example process 700 that may be implemented, in whole or in
part, to facilitate or support one or more operations or techniques
in connection with a memory sweep. It should be noted that
information acquired or produced, such as, for example, input
signals, applications, output signals, operations, results, etc.
associated with example process 700 may be represented by one or
more digital signals. It should also be appreciated that even
though one or more operations are illustrated or described
concurrently or with respect to a certain sequence, other sequences
or concurrent operations may also be employed. In addition,
although the description below references particular aspects or
features illustrated in certain other figures, on more operations
may be performed with other aspects or features.
[0087] Example process 700 may begin at operation 702 with
organizing one or more input STING clusters in an in-memory array.
As described below, one or more input clusters may be organized
based, at least in part, on any suitable indexing strategy,
depending, at least in part, on an implementation. As illustrated,
in some instances. KD-tree-type indexing or R-tree-type indexing
may be employed, though claimed subject matter is not so limited,
of course. Indexing strategy may be characterized or specified, in
whole or in part, in any suitable manifest or INI-type file, such
as, for example, manifest file 400, as referenced at operation 704.
Accordingly, at operation 706, a particular type of an indexing
strategy, such as whether to perform an R-tree-type indexing or
KD-tree-type indexing, for example, may be determined based, at
least in part, on referencing manifest file 400. As previously
mentioned, one or more indexing strategies may be performed using
one or more techniques, such as by accessing, for example, a
particular web-site associated with a suitable commercial
statistical library (e.g.,
http://javadoc.geotools.fr/2.2/org/geotools/index/rtree/RTree.html,
http://www.java2s.com/Open-Source/Java-Document/Database-DBMS/perst/org/g-
arret/perst/impl/KDTree.java.htm, etc.)
[0088] If a determination has been made in favor of a KD-tree-type
indexing, for example, a process may proceed to operation 708 and
further to operation 714 so as to generate a number of STING
clusters based, at least in part, on a KD-tree-type indexing. If a
KD-tree-type indexing approach does not converge, however, example
process 700 may proceed to operation 710, for example, so as to be
terminated in some manner, such as without performing a memory
sweep. On the other hand, having an R-tree type indexing as a
strategy may provide, for example, an advantage in the form of a
stronger mode of convergence, thus, allowing a process to continue
to operation 714 without being terminated.
[0089] Based, at least in part, on an R-tree-type indexing,
KD-tree-type indexing, or any other suitable type of indexing, for
example, at operation 714 one or more STING clusters may be
generated or created, as mentioned above. In certain simulations or
experiments, STING clusters included those illustrated in FIG. 8,
though claimed subject matter is not so limited. Thus, using, at
least in part, an R-tree-type indexing or KD-tree-type indexing,
for example, STING cells associated with sampled signals may be
organized in one or more clusters, such as clusters 802, 804, or
806. As alluded to previously, a STING cluster may comprise, for
example, a number of information points, as indicated generally at
808, or units of spatia-temporal-nodal information sampled with
respect to an information source, By way of example but not
limitation, an information point may be representative of a signal
sample value with respect to temperature, pressure, or the like,
which may be referenced in connection with a certain point in time,
particular location, etc., though claimed subject matter is not so
limited.
[0090] At operation 716, a bounding box for a STING cluster may be
created in some manner. For example, bounding boxes 810, 812, or
814 may be created for clusters 802, 804, and 806, respectively, by
enclosing minimum-perimeter or volume areas around a cluster. As
such, here, for a cluster, a bounding box may comprise, for
example, an area with the smallest perimeter or volume within which
all or most information points may fit or otherwise lie, though
claimed subject matter is not so limited. For example, in some
instances, a bounding box may be created by taking particular
reference lines in a certain order, such as the minimum X, maximum
Y for the upper left corner and the maximum X, minimum Y for the
bottom right corner. Of course, this is merely an example, and
claimed subject matter is not limited.
[0091] Referring back to FIG. 7, at operation 718, a loop for a
cluster may be specified, such that a certain sequence or construct
(e.g., creating a bounding box, memory footprint, etc.) may be
carried out or repeated for any suitable number of clusters. With
regard to operation 720, a memory footprint for a cluster may be
created. For example, n-dimensional signal vectors having
spatially, temporally, or nodally-dominated attributes, as
described above, may be processed or transformed in some manner so
as to arrive at two-dimensional representations of these vectors,
and may be subsequently linearized or stored in one-dimensional
memory as a function of mutually relative distance. A particular
example of creating a memory footprint will be described in greater
detail below with reference to FIG. 9. At operation 722, it may be
determined whether example process 700 has been performed or
completed for a suitable number of STING clusters, such as, for
example, for all or most clusters created or generated at operation
716. Otherwise, then process 700 may return to operation 718 to
repeat one or more functions associated with operations 720 and
722. On the other hand, alternatively, process 700 may terminate a
memory sweep.
[0092] FIG. 9 is a flow diagram illustrating an implementation of
an example process 900 that may include a linearization operation,
which may be performed, at least in part, in connection with one or
more memory sweep operations, such as, for example, operation 720
of FIG. 7. Again, it should be noted that information acquired or
produced, such as, for example, input signals, applications, output
signals, operations, results, etc. associated with example process
900 may be represented by one or more digital signals. It should
also be appreciated that even though one or more operations are
illustrated or described concurrently or with respect to a certain
sequence, other sequences or concurrent operations may also be
employed. In addition, although the description below references
particular aspects or features illustrated in certain other
figures, one or more operations may be performed with other aspects
or features, as was also indicated.
[0093] Continuing with process 900, a cluster of STING cells, such
as cluster 802, 804, or 806 of FIG. 8, for example, indexed in some
manner, such as in the manner described above in connection with
operations 702-718 of FIG. 7, may be used as input signals, as
indicated at operation 902. With regard to operation 904, example
process 900 may reference any suitable manifest file (e.g.,
manifest file 400, etc.) so as to determine one or more dominant
metrics of a sampled signal. In some instances, a dominant metric
may comprise, for example, a default dominant metric that may be
specified by a pointer referenced in a manifest file as
Default_Dominant_Metric_Pointer, though claimed subject matter is
not so limited. For example, a dominant metric may refer to a
dominant attribute associated with a sampled STING cell. As
previously mentioned, a dominant attribute may comprise an
attribute that ray be determined based, at least in part, on an
application of a dominance principle and may influence one or more
characteristics of an associated signal vector. By way of example
but not limitation, a signal sampled with respect to a certain
signal acquisition point, such as a thermostat, for example, may
have a temperature as a dominant metric. As described below, a
dominant metric may be represented via a dominant signal value
vector v.sub.dominant.sub.i, for example, and may influence various
process related strategies, such as indexing, memory sweep,
organization, or the like. If a signal value for a dominant metric
is not declared (e.g., by a vendor, etc.) or otherwise not
specified (e.g., in a manifest file, etc.), for example, a signal
value of 1 may be used. Of course, details relating to a dominant
metric are merely examples, and claimed subject matter is not
limited in this regard.
[0094] At operation 906, a centroid or center of mass for a cluster
of STING cells may be computed in some manner. For example as
illustrated in FIG. 10, a centroid 1000 of a cluster of STING cells
1002 for a bounding box 1004 may be computed by specifying, for
example, one or more dominant metric weighting factors. In some
instances, a dominant metric weighting factor may be characterized,
for example, by a dominant signal value vector
v.sub.dominant.sub.i, just to illustrate one possible
implementation. Thus, consider:
x centroid = i = i n x i v dominant i i = i n v dominant i ( 2 ) y
centroid = i = i n y i v dominant i i = i n v dominant i ( 3 )
##EQU00002##
[0095] As such, n-dimensional signal vectors, such as signal
vectors described above with reference to FIG. 5, for example, may
be advantageously transformed so as to be represented in a
two-dimensional space or matrix by specifying one or more dominant
metric weighting factors that may be based, at least in part, on a
function of mutually relative distance between STING cells
associated with a sampled signal. In some instances, one or more
distance calculators may be utilized to facilitate or support a
representation. Particular examples of one or more spatial,
temporal, nodal, etc. distance calculators will be described in
greater detail below. Accordingly, based, at least in part, on
distance-related calculations, centroid 1000 may comprise, for
example, a two-dimensional center of mass for cluster 1002.
[0096] With regard to operation 908, it may be determined which
STING cell of STING cluster 1002 is the closest to centroid 1000
and which STING cell is the farthest from centroid 1000. As
particularly seen in FIG. 11, such a determination may be made
based, at least in part, on computing the minimum Rmin and the
maximum Rmax distances, as indicated at 1102 and 1104,
respectively. In some instances, a Euclidian-based distance metric
may be utilized, for example, to calculate respective Rein and
Rmax, though claimed subject matter is not so limited. For example,
other distance calculators, such one or more distance calculators
described below, may also be used. At operation 910, an incremental
sweep angle .theta..sub.increment and an incremental radius
R.sub.increment may be computed using, for example, following
respective relations:
.theta. increment = ( sin - 1 ( R min / R_max ) ) / .pi. ( 4 ) R
increment = R max - R min .pi. ( 5 ) ##EQU00003##
where .pi. denotes a mathematical constant of approximately
3.14159.
[0097] At operation 912, computed sweep angle and radius may be
specified as signal inputs so as to produce or generate, for
example, a transitioning curve 1106. Transitioning curve 1106 may
comprise a central point corresponding to centroid 1000, for
example, and which curvature may increase by a value of an angle
increment as curve 1106 unwinds in a spiral-like fashion, as
illustrated. It should be noted that curve 1106 may be specified to
emanate from centroid 1000 in any suitable direction, such as
counter-clockwise or, optionally or alternatively, clockwise, for
example, as referenced by arrows at 1108. For example, a sweep
angle or radius may be incremented in some manner, such as by an
angle counter, for example, utilizing a value computed via
Relations 4 or 5 above. While sweeping and as a curvature of curve
1106 increases, such as due to a specified angle increment, for
example, a leading or endpoint of curve 1106 may encounter a number
of information points representative of STING cells, such as a
point 1110, 1112, and so forth. Accordingly, using curve 1106 in a
spiral-like fashion, information points within a bounding box, such
as bounding box 1104, for example, may be identified or
located.
[0098] Having located an information point, such as point 1112, for
example, process 900 may continue to operation 914 so as to
identify or locate all or some information points within a certain
distance from point 1112. For example, as illustrated in FIG. 12,
to identify information points, a circle 1200 may be generated or
formed around information point 1112 by specifying the distance
Rmin, just to illustrate one possible implementation. All or some
information points inside circle 1200 may be linearized based, at
least in part, on in-circle distance calculations with respect to
these points utilizing, for example, Euclidian distance metrics.
Optionally or alternatively, distance may be calculated by
utilizing applicable distance calculators referenced or
characterized in memory sweep-related fields in a manifest file,
Applicability of a distance calculator may depend, at least in
part, on types of one or more dominant attributes associated with
respective in-circle information points of interest, for example.
One or more distance calculators ill be described in greater detail
below. By way of example but not limitation, linearized in-memory
array may include one referenced by arrow at 1202. Information
points X1-X4 may be representative of signal sample values
linearized based, at least in part, on in-circle distance
calculations, such as, for example, distance calculations utilizing
Euclidian distance metrics for a two-dimensional plane. Points
P1-P3 may represent signal values linearized via an application of
Euclidian-Temporal distance calculations, as another possible
example. It should be appreciated, however, that signal values,
distance calculators, or in-memory array shown are merely examples
to which claimed subject matter is not limited.
[0099] At operation 916, it may be determined if a certain
identified information point representative of a particular STING
cell, such as a STING cell P1, P2, or P3 within circle 1200, for
example, has already been linearized. Encountering a linearized
information point may indicate that one or more memory sweep
parameters, such as an incremental sweep angle or radius, for
example, may be less than adequate or somewhat insufficient, which
may prompt example process 900 for suitable parameter evaluation,
as referenced at operation 918. In certain simulations or
experiments, a computed increment angle or radius were evaluated as
follows: [0100] {is angle>2*PI?} [0101] {is radius>Rmax?},
where PI denotes a mathematical constant .pi. of approximately
3.14159. If yes, at operation 920, a memory sweep may be
terminated. On the other hand, if no, example process 900 may
return to operation 912 to perform suitable parameter adjustments,
such as incrementing a sweep angle, for example. With regard to
operation 922, linearized STING cells may be added to an in-memory
array and may be stored in any suitable manner, such as, for
example, in the manner described above. As illustrated by arrow
924, example process 900 may include repeating one or more
operations, such as operations 912-916, for example, so as to
facilitate or support linearization of all or some of STING cells
associated with a cluster of interest,
[0102] As previously mentioned, one or more distance calculators
may be utilized, in whole or in part, to facilitate or support
transforming n-dimensional signal vectors associated with a sampled
signal into one-dimensional representations including, for example,
a linearization operation discussed above. In some instances,
distance calculators may be realized as a set of APIs that may help
to identify relationships or strengths of relationships between
sampled signals based, at least in part, on proximity between
information points of interest. As described above, a sampled
signal may be represented, for example, via one or more
n-dimensional signal vectors, such that S.sub.i={x.sub.i, y.sub.i,
z.sub.i, t.sub.i, N.sub.i, v1.sub.i, v2.sub.i . . . }. Thus, a
distance between two sampled signals S.sub.1={x.sub.1, y.sub.1,
z.sub.1, t.sub.1, N.sub.1, v1.sub.1, v2.sub.1 . . . } and
S.sub.2={x.sub.2, y.sub.2, z.sub.2, t.sub.2, N.sub.2, v1.sub.2,
v2.sub.2 . . . } may be characterized, for example, as
D.sub.12=distance(S.sub.1,S.sub.2). In one implementation, distance
calculators may be specified or characterized in any suitable
configuration or initialization-type file, such as, for example,
manifest file 400, as was also indicated. For example, distance
calculators may include those listed below, though claimed subject
matter is not so limited.
[0103] SPATIAL2D. Spatial Euclidean distance for a two-dimensional
or {X, Y} plane. Distance D.sub.12 may be calculated using, for
example, the following Relation:
D.sub.12= {square root over
((x.sub.2-x.sub.1).sup.2+(y.sub.2-y.sub.1).sup.2)} (6)
[0104] SPATIAL3D. Spatial Euclidean distance for a
three-dimensional or {X, Y, Z} plane. D.sub.12 may be calculated,
for example, as:
D.sub.12= {square root over
((x.sub.2-x.sub.1).sup.2+(y.sub.2-y.sub.1).sup.2+(z.sub.2-z.sub.1).sup.2)-
} (7)
[0105] SPATIO_TEMPORAL Spatio-Temporal Euclidean distance in a {X,
Y, Z, T} plane or dimension. Distance D.sub.12 may be calculated
as:
D.sub.12= {square root over
(K(t.sub.2-t.sub.1).sup.2+(x.sub.2-x.sub.1).sup.2+(y.sub.2-y.sub.1).sup.2-
+(z.sub.2-z.sub.1).sup.2)} (6)
where K denotes a dimensional factor conversion notated in a unit
that is a {distance measure}/{time measure}. In one implementation,
K=1 may be used, though claimed subject matter is not so
limited.
[0106] SPATIO_NDIMENSIONAL. Euclidean Distance in an N-dimensional
plane or domain. Distance D.sub.12 may be calculated, for example,
using the following relation:
D 12 = i = i n ( p 1 i - p 2 i ) 2 ( 9 ) ##EQU00004##
where p.sub.i is the i.sup.th component or out of n components,
such that, for example, p.sub.11=x.sub.1 and p.sub.12=y.sub.1 and .
. . p.sub.1n=v1.sub.2.
[0107] TEMPORAL. Difference in time-related moments. For a
calculator, distance D.sub.12 may be defined, for example, as:
D.sub.12=t.sub.2-t.sub.1 (10)
[0108] NODAL_DISTANCE_AFFINITY. Distance between nodes based on the
affinity of nodes. For two sampled signals, such as, for example,
signals S.sub.1={x.sub.1, y.sub.1, z.sub.1, t.sub.1, N.sub.1,
v1.sub.1, v2.sub.1 . . . } and S.sub.2={x.sub.2, y.sub.2, z.sub.2,
t.sub.2, N.sub.2, v1.sub.2, v2.sub.2 . . . }, having sample value
vectors v1.sub.i, v2.sub.i representative of pressure, temperature,
etc., distance D.sub.12 may be computed, at least in part, by
utilizing the Hamming distance measure or D.sub.ij, just to
illustrate one possible implementation. Thus, by way of example but
not limitation, the following approach may be used:
[0109] Loop through value vectors. For example, a value vector may
be compared against similar value vector of another signal, such
that v1.sub.1=v1.sub.2.quadrature. v2.sub.1=v2.sub.2?
v2.sub.1=v3.sub.2? v4.sub.1=v4.sub.2? and so forth through, for
example, v7.sub.1=v7.sub.2?, depending, at least in part, on
implementation.
[0110] Create a binary signal value or bitmap register with respect
to value vectors. Thus, consider: [0111] Bitmap.sub.1=Binary
(v1.sub.1) Binary (v2.sub.1) Binary (v3.sub.1) . . . (Binary
(v7.sub.1) For purposes of explanation, a word like "hello" may be
represented via a binary string
0110100001100101011011000110110001101111.
[0112] Compute the Hamming distance, or example, as:
D.sub.if=HammingDistance(Bitmap.sub.i,Bitmap.sub.j)
Hamming distance may be utilized, for example, to identify related
nodes (e.g., democratic leadership, etc.) that are close to
relatively stronger nodes (e.g., the President--the Vice President,
etc.) in a sociogram or graph-type relationship (e.g., the
Democratic Party, etc.). Hamming distance computations are known
and need not be described here in greater detail.
[0113] NODAL_DISTANCE_DEGREE_OF_SEPARATION. Distance between nodes
is based, at least in part, on degree of separation within a graph.
A degree of separation distance D.sub.12 may refer to a distance
between two nodes in a graph calculated using a smallest number of
links connecting nodes. An example Nodal Distance Degree of
Separation calculator may be illustrated with reference to FIG. 13.
As previously mentioned, nodes in a graph, such as a graph 1300,
may be representative of sampled signals having signal values
descriptive, for example, of canonical relationships within a
graph. For graph 1300, two nodes, such as nodes 4 and 8, for
example, may be represented via signal vectors as S.sub.4={x.sub.4,
y.sub.4, z.sub.4, t.sub.4, N.sub.4, v1.sub.4, v2.sub.4 . . . } and
S.sub.8={x.sub.8, y.sub.8, z.sub.8, t.sub.8, N.sub.8, v1.sub.8,
v2.sub.8 . . . }, respectively. For example, node 4 may be
traversed to node 8 by following links to nodes 3, 7, 6, and 2,
respectively. However, node 4 may also be traversed to node 8 by
following one link or edge. Accordingly, a distance calculator may
follow or navigate the smallest number of links to arrive from node
4 to node 8, which is 1. As such, the degree of separation between
signals S.sub.4 and S.sub.8 is 1 or [d.sub.4,8=1].
[0114] Further, a mean of all distances for graph 1300 may be
computed. For example, for a node, compute the minimum degree of
separation with respect to other nodes. By way of example but not
limitation, for Node or Signal 1, the minimum degree of separation
with respect to other nodes (e.g., signals) in graph 1300 may
include those illustrated in Table 3 below, though claimed subject
matter is not so limited.
TABLE-US-00003 TABLE 3 Examples of minimum degrees of separation
for Node or Signal 1 Signal 1 -> Signal 2 1 Signal 1 ->
Signal 3 1 Signal 1 -> Signal 4 1 Signal 1 -> Signal 5 2
Signal 1 -> Signal 6 2 Signal 1 -> Signal 7 2 Signal 1 ->
Signal 8 1
[0115] Accordingly, here, a mean of distances for Node or Signal 1
may be computed as {1+1+1+1+2+2+2}=10/7=1.43.
[0116] By way of example but not limitation, for a canonical
relationship of graph 1300, an approach for determining the minimum
degree of separation with respect to other nodes (e.g., signals) in
may include one illustrated in Table 3 below, though claimed
subject matter is not so limited. Thus, consider
TABLE-US-00004 TABLE 4 Example of determining the minimum degrees
of separation for a graph. Signal 1 -> Signal 2 1 Signal 1 ->
Signal 3 1 Signal 1 -> Signal 4 1 Signal 1 -> Signal 5 2
Signal 1 -> Signal 6 2 Signal 1 -> Signal 7 2 Signal 1 ->
Signal 8 1 Signal 2 -> Signal 1 <double counted> so
eliminate from calculation Signal 2 -> Signal 3 2 Signal 2 ->
Signal 4 2 . . . . . . Signal 8 -> Signal 6 1 Signal 8 ->
Signal 7 2
[0117] Likewise, a mean of all distances (e.g., for all nodes) for
graph 1300 may be computed in a similar fashion.
[0118] Accordingly, a distance D.sub.12 between two signals or
nodes of interest (e.g., information points, etc.) may be
calculated as a degree of separation between signals divided by a
mean of all distances for a particular graph associated with these
signals, or:
D.sub.12=[degree of separation between signals]/[mean of all
distances] (11)
[0119] FIG. 14 is a schematic representation of an implementation
of an example space-time-node engine signal structure 1400. As
seen, space-time-node engine signal structure 1400 may comprise,
for example, a number of STING cells that may be converted in some
manner so as to comply with a particular signal configuration. For
example, in some instances, a number of STING cells that may be
converted so as to be organized in a one-dimensional or linearized
in-memory array 1402. As also illustrated, STING cells may be
organized such that a number of certain STING cells, such as
temporally-dominant STING cells 1404, for example, may cluster
together so as to be adjacent or in sufficiently close proximity in
memory. As previously mentioned, one or more dominant attributes of
a STING cell, such as temporal attributes 1408, for example, may be
identified or determined based, at least in part, on an application
of a dominance principle in a suitable manner, such as in a manner
discussed above. As previously mentioned, in-memory arrangement of
STING cells may be characterized based, at least in part, on
mutually relevant distance between corresponding information points
computed via one or more distance calculators. Optionally or
alternatively, STING cells may be organized in memory based, at
least in part, on Hamming distance, as was also indicated. By
having STING cells organized or arranged as a function of mutually
relative distance, for example, space-time-node engine signal
structure 1400 may implement faster information accessing or
retrieving. This may help to anticipate preferences including, for
example, user preferences with respect to n-dimensional content
(e.g., queries, etc.) or information that may be associated with a
variety of sources or communicated in a variety of formats, thus,
facilitating or supporting more effective or efficient formation
processing, analysis, reporting, presentation, etc., or any
combination thereof.
[0120] FIG. 16 is a schematic diagram illustrating an
implementation of an example computing environment 1600 that may
include one or more devices or platforms capable of partially or
substantially implementing one or more processes or operations in
connection with a space-time-node engine signal structure, such as,
for example, space-time-node signal structure 1400 of FIG. 14,
Computing environment system 1600 may include, for example, a first
device 1602 and a second device 1604, which may be operatively
coupled together via a network 1606, though claimed subject matter
is not so limited. For example, first device 1602 and a second
device 1604 may be operatively coupled together via a communication
link 1608, which may or may not be associated with network 1606.
Optionally or alternatively, first device 1602 and second device
1604 may comprise or be a part of a certain computing platform,
such as a platform associated, for example, with a space-time-node
engine. In an implementation, first device 1602 and second device
1604 may be representative of any electronic device, appliance,
machine, or the like that may have capability to exchange signal
information, such as multi-source information, for example, over
network 1606, communication link 1608, or the like. Network 1606
may represent one or more communication links, processes, or
resources having capability to facilitate or support exchange or
communication of signal information between first device 1602,
second device 1604, or the like,
[0121] In an implementation, first device 1602 or second device
1604 may be capable of facilitating or supporting one or more
processes or operations associated with computing environment 1600,
such as, for example, process 200 of FIG. 2, process 700 of FIG. 7,
process 900 of FIG. 9, or the like. As previously mentioned, first
device 1602 may comprise, for example, a sensor associated with a
signal acquisition point, just to illustrate one possible
implementation. Second device 1604 may comprise, for example, at
least one processor and memory that may be configurable to exchange
data or information over any suitable communications network. For
example, second device 1604 may include one or more computing
devices or platforms capable of communicating with, for example, a
laptop computer, a desktop computer, a tablet PC, a cellular
telephone, an access point, a transceiver chip, an e-book reader, a
workstation, a server device, a data storage unit, a file system, a
sensor, or the like. In certain implementations, first device 1604
or second device 1604 may take the form of one or more integrated
circuits, circuit boards, or the like that may be operatively
enabled for use in another device.
[0122] It should be appreciated that all or part of various
components shown in connection with computing environment 1600, or
the processes or operations as described herein, may be implemented
using or otherwise include hardware, firmware, or any combination
thereof along with software. It should also be noted that computing
environment 1600 may include more, fewer, or different components
from those that are illustrated. Although not shown, optionally or
alternatively, there may be additional devices operatively coupled
to first device 1602, second device 1604, etc. to facilitate or
otherwise support one or more processes or operations associated
with computing environment 1600. Thus, unless stated otherwise, to
simplify discussion, various functionalities, elements, components,
etc. as described below with reference to second device 1604 may
also be applicable to other devices, such as first device 1602, for
example, or devices not shown so as to facilitate or support one or
more processes associated with example computing environment
1600.
[0123] As illustrated, second device 1604 may include at least one
processing unit 1610, memory 1612, communication interface 1614,
and one or more other components, indicated generally at 1616, for
example, as to facilitate or support one or more processes or
operations in connection with a space-time-node engine signal
structure. Processing unit 1610 may be implemented in hardware or a
combination of hardware and software. Processing unit 1610 may be
representative of one or more circuits configurable to perform at
least a portion of information computing techniques or processes.
By way of example but not limitation, processing unit 1610 may
include one or more processors, controllers, microprocessors,
microcontrollers, application specific integrated circuits, digital
signal processors, programmable logic devices, field programmable
gate arrays, etc., or any combination thereof.
[0124] Memory 1612 may store, comprise, or otherwise provide access
to computer-readable instructions, such as a program, an
application, etc. or portions) thereof, including, for example,
initialization or configuration files, information structures,
processor-executable instructions or code, or the like that may be
accessible or executable by processing unit 1610. Execution of such
instructions by processing unit 1610 may transform second device
1604 into a special purpose computing device, apparatus, platform,
etc., or some combination thereof. Memory 1612 may represent any
information or signal storage medium or mechanism. For example,
memory 1612 may include a primary memory 1618 and a secondary
memory 1620. Primary memory 1618 may include, for example, a random
access memory, read only memory, or the like and may comprise
information with respect to one or more training signal datasets,
cardinality maps, indexing options, manifest classes or properties,
linearized values, STING cells or clusters, various thresholds
(e.g., sweep angle, radius, etc.) dominant attributes, or other
suitable or desires information to facilitate or support one or
more processes or operations in connection with a space-time-node
engine signal structure. While illustrated in this example as being
separate from processing unit 1610, it should be appreciated that
all or part of memory 1612 may be provided within or otherwise
co-located/coupled with processing unit 1610.
[0125] Secondary memory 1620 may include, for example, the same or
similar type of memory as primary memory. In certain
implementations, secondary memory 1620 may comprise, for example,
one or more information storage devices or systems, such as, for
example, a disk drive, an optical disc drive, a tape drive, a solid
state memory drive, or the like. Secondary memory 1620 may be
operatively receptive of, or otherwise enabled to be coupled to, a
computer-readable medium 1622. Computer-readable medium 1622 may
include, for example, any medium capable of storing or providing
access to information, code, or instructions (e.g., an article of
manufacture, etc.) for second device 1604 or any other device
associated with computing environment 1600. It should be understood
that a storage medium may typically, although not necessarily, be
non-transitory or may comprise a non-transitory device. In this
context, a non-transitory storage medium may include, for example,
a device that is physical or tangible, meaning that the device has
a concrete physical form, although the device may change state. For
example, one or more electrical binary digital signals
representative of information, in whole or in part, in the form of
zeros may change a state to represent information, in whole or in
part, as binary digital electrical signals in the form of ones, to
illustrate one possible implementation. As such, "non-transitory"
may refer, for example, to any medium or device remaining tangible
despite this change in state.
[0126] Computer-readable medium 1622 may be accessed by processing
unit 1610, for example. As such, in certain example
implementations, the methods or apparatuses may take the form, in
whole or in part, of a computer-readable medium that may include
computer-implementable instructions stored thereon, which, if
executed by at least one processing unit or other like circuitry,
may enable processing unit 1610 or the other like circuitry to
perform all or portions of a memory sweep operation, or any
operation or process to facilitate or otherwise support a
space-time-node engine structure. In certain example
implementations, processing unit 1610 may be capable of performing
or supporting other functions associated with computing environment
1600, such as signal acquisition, training, presentation,
communication, routing, or the like.
[0127] Communication interface 1614 may allow for communication
with one or more devices or systems associated with computing
environment 1600 over one or more wired or wireless communication
links. In certain implementations, communication interface may
comprise, for example, a function or class-type driver or adapter
(e.g., a STING adapter, etc.) that may provide for or otherwise
support communicative coupling between different levels or layers
of an architecture or a part of an architecture associated with
computing environment 1600, as previously mentioned. Although not
shown, second device 1604 may include a power source to provide
power to some or all of the components or circuitry. A power source
may be a portable power source, such as a battery, for example, or
may comprise a fixed or stationary power source, such as an outlet
(e.g. in a building, electric charging station, car, etc.). It
should be appreciated that a power source may be integrated into
(e.g., built-in, etc.) or otherwise supported by (e.g.,
stand-alone, etc.) second device 1604. A power source may also be a
transportable power source, such as a solar panel,
carbon-fuel-based generator, or the like. Also, components or
circuitry of second device 1604 may include an analog-to-digital
converter (ADC) for digitizing output signals, for example,
[0128] Second device 1604 may also include one or more buses or
connections 1624 (e.g., connectors, lines, conductors, optic
fibers, etc.) to operatively couple various circuits or components
together including, for example, one or more other components 1616.
As also seen, second device may comprise, for example, an
input/output device 1626. Input/output device 1626 may represent
one or more devices or features that may be able to accept or
otherwise input human or machine instructions, or one or more
devices or features that may be able to deliver or otherwise output
human or machine instructions. By way of example but not
limitation, input/output device may include, for example, a user
interface, such as display, touch screen, keypad, buttons, knobs,
microphone, speaker, trackball, data port, or the like. Other
components 1616, if present, may comprise one or more other device,
features, functionalities, or the like capable of facilitating or
supporting one or more operations or processes implemented by
second device 1604, such as operations in connection with a
space-time-node engine signal structure, for example.
[0129] According to an implementation, one or more portions of a
device associated with computing environment 1600, such as first
device 1602, second device 1604, or the like, for example, may
store one or more binary digital electronic signals representative
of information expressed as a particular state of a device. To
illustrate, an electrical binary digital signal representative of
information may be "stored" in a portion of memory 1612 of second
device 1604 by affecting or changing a state of particular memory
locations, for example, to represent information as binary digital
electronic signals in the form of ones or zeros. As such, in a
particular implementation of a device, such a change of state of a
portion of a memory within a device, such a state of particular
memory locations, for example, to store a binary digital electronic
signal representative of information constitutes a transformation
of a physical thing, such as memory 1612, for example, to a
different state or thing.
[0130] Thus, as illustrated in various example implementations or
techniques presented herein, in accordance with certain aspects, a
method may be provided for use as part of a special purpose
computing device or other like machine that accesses digital
signals from memory or processes digital signals to establish
transformed digital signals which may be stored in memory as part
of one or information files or a database specifying or otherwise
associated with an index, in-memory or otherwise.
[0131] Some portions of the detailed description herein are
presented in terms of algorithms or symbolic representations of
operations on binary digital signals stored within a memory of a
specific apparatus or special purpose computing device or platform.
In the context of this particular specification, the term specific
apparatus or the like includes a general purpose computer once it
is programmed to perform particular functions pursuant to
instructions from program software, Algorithmic descriptions or
symbolic representations are examples of techniques used by those
of ordinary skill in the signal processing or related arts to
convey the substance of their work to others skilled in the art. An
algorithm is here, and generally, is considered to be a
self-consistent sequence of operations or similar signal processing
leading to a desired result. In this context, operations or
processing involve physical manipulation of physical quantities.
Typically, although not necessarily, such quantities may take the
form of electrical or magnetic signals capable of being stored,
transferred, combined, compared or otherwise manipulated. It has
proven convenient at times, principally for reasons of common
usage, to refer to such signals as bits, data, values, elements,
symbols, characters, terms, numbers, numerals or the like. It
should be understood, however, that all of these or similar terms
are to be associated with appropriate physical quantities and are
merely convenient labels.
[0132] Unless specifically stated otherwise, as apparent from the
discussion herein, it is appreciated that throughout this
specification discussions utilizing terms such as "processing,"
"computing," "calculating," "determining" or the like refer to
actions or processes of a specific apparatus, such as a special
purpose computer or a similar special purpose electronic computing
device. In the context of this specification, therefore, a special
purpose computer or a similar special purpose electronic computing
device is capable of manipulating or transforming signals,
typically represented as physical electronic or magnetic quantities
within memories, registers, or other information storage devices,
transmission devices, or display devices of the special purpose
computer or similar special purpose electronic computing
device.
[0133] Terms, "and" and "or" as used herein, may include a variety
of meanings that also is expected to depend at least in part upon
the context in which such terms are used. Typically, "or" if used
to associate a list, such as A, B or C, is intended to mean A, B,
and C, here used in the inclusive sense, as well as A, B or C, here
used in the exclusive sense. In addition, the term "one or more" as
used herein may be used to describe any feature, structure, or
characteristic in the singular or may be used to describe some
combination of features, structures or characteristics. Though, it
should be noted that this is merely an illustrative example and
claimed subject matter is not limited to this example.
[0134] While certain example techniques have been described or
shown herein using various methods or systems, it should be
understood by those skilled in the art that various other
modifications may be made, or equivalents may be substituted,
without departing from claimed subject matter. Additionally, many
modifications may be made to adapt a particular situation to the
teachings of claimed subject matter without departing from the
central concept(s) described herein. Therefore, it is intended that
claimed subject matter not be limited to particular examples
disclosed, but that claimed subject matter may also include all
implementations falling within the scope of the appended claims, or
equivalents thereof.
* * * * *
References