U.S. patent application number 13/755059 was filed with the patent office on 2014-02-13 for method and system for visualizing information extracted from big data.
This patent application is currently assigned to XURMO TECHNOLOGIES PVT. LTD.. The applicant listed for this patent is XURMO TECHNOLOGIES PVT. LTD.. Invention is credited to SRIDHAR GOPALAKRISHNAN, SUJATHA RAVIPRASAD UPADHYAYA.
Application Number | 20140046892 13/755059 |
Document ID | / |
Family ID | 50066829 |
Filed Date | 2014-02-13 |
United States Patent
Application |
20140046892 |
Kind Code |
A1 |
GOPALAKRISHNAN; SRIDHAR ; et
al. |
February 13, 2014 |
METHOD AND SYSTEM FOR VISUALIZING INFORMATION EXTRACTED FROM BIG
DATA
Abstract
The various embodiments herein describe a method for providing
information visualization comprising identifying a plurality of
events from a big data, calculating a temporal distance between at
least two events, calculating a semantic distance between the at
least two events, storing the calculated semantic distance and the
temporal distance with respect to the at least two events in a data
structure, providing the semantic distance and the temporal
distances calculated between the at least two events on a visual
representation, identifying one or more relevant events with
respect to the target event based on the semantic distance and the
temporal distance through visualization, selecting a plurality of
events that influence the target event and examining a pattern of
the plurality of events to realize possible collinearity of the
events to further reduce the influencing events in order to
facilitate feature selection through visualization.
Inventors: |
GOPALAKRISHNAN; SRIDHAR;
(BANGALORE, IN) ; UPADHYAYA; SUJATHA RAVIPRASAD;
(BANGALORE, IN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
XURMO TECHNOLOGIES PVT. LTD. |
Bangalore |
|
IN |
|
|
Assignee: |
XURMO TECHNOLOGIES PVT.
LTD.
BANGALORE
IN
|
Family ID: |
50066829 |
Appl. No.: |
13/755059 |
Filed: |
January 31, 2013 |
Current U.S.
Class: |
706/46 |
Current CPC
Class: |
G06F 16/288 20190101;
G06F 40/40 20200101; G06F 16/2465 20190101; G06F 16/367 20190101;
G06N 20/00 20190101; G06N 5/02 20130101; G06F 40/295 20200101; G06F
16/35 20190101 |
Class at
Publication: |
706/46 |
International
Class: |
G06N 5/02 20060101
G06N005/02 |
Foreign Application Data
Date |
Code |
Application Number |
Aug 10, 2012 |
IN |
3286/CHE/2012 |
Claims
1. A method for providing information visualization comprises:
identifying a plurality of events from a big data; calculating a
temporal distance between at least two events; calculating a
semantic distance between the at least two events; storing the
calculated semantic distance and the temporal distance with respect
to the at least two events in a data structure; providing the
semantic distance and the temporal distances calculated between the
at least two events on a visual representation; identifying one or
more relevant events with respect to the target event based on the
semantic distance and the temporal distance through visualization;
selecting a plurality of events that influence the target event;
and examining a pattern of the plurality of events to realize a
collinearity of the events to limit the influencing events
considered for feature analysis.
2. The method of claim 1, wherein realizing the collinearity of the
events comprises identifying at least two events sharing an exact
relationship and reducing the number of variables to facilitate
effective feature selection.
3. The method of claim 1, further comprises: selecting a new target
event; and re-visualizing the influence of the selected features on
the new target event.
4. The method of claim 1, wherein an event is defined as a
relationship which occurred at an instant of time.
5. The method of claim 1, wherein the feature selection is defined
as selecting a plurality of variables having an influence on the
occurrence of the event.
6. The method of claim 1, wherein calculating the temporal distance
between at least two events comprises computing temporal
correlation between the at least two events; wherein the temporal
correlation is a measure of correlation of the events across
time.
7. The method of claim 1, wherein the temporal distance is
calculated on the basis of time series data obtained from one of a
structured data, an unstructured data or a combination of
structured data and unstructured data.
8. The method of claim 1, wherein calculating the semantic distance
between the two events comprises at least one of: measuring a
contextual distance between one or more words if the plurality of
events are described by the words; measuring a contextual
similarity or a semantic similarity as provided by a domain model
and a language model; and measuring the contextual or the semantic
similarity based on analyzing the relationships and the
entities.
9. The method of claim 1, wherein the semantic distance is
calculated on the basis of structured data and an unstructured data
or a combination of structured data and unstructured data.
10. The method of claim 1, further comprises storing the semantic
distance and the temporal distances between the at least two events
in a data structure capable of storing both the semantic distances
and the temporal distances separately.
11. The method of claim 1, wherein a value corresponding to the
semantic distance and the temporal distance is in a preset
numerical range or correspond to a discrete values range, with an
event being closest to itself both temporally and semantically.
12. The method of claim 1, wherein the representation of the
visualization comprises: events represented by a predefined shape;
negatively co-related events differentiated by the shape; different
events represented by different colors; different event types
represented by different colors; highly influential events rendered
around the target event; and less influential events rendered away
from the targeted event as they approach the origin.
13. The method of claim 1, wherein further comprises: setting
limits for semantic distances and the temporal distances for the
plurality of events influencing the target event; and selecting the
events falling in a defined limit as the highly influential
events.
14. A system for providing information visualization comprises: an
event extractor to extract a plurality of events from a big data; a
semantic distance estimator to calculate a semantic distance
between at least two events; a temporal distance estimator to
calculate a temporal distance between the at least two events; a
data structure to store to store the calculated semantic distance
and the temporal distance with respect to the at least two events;
an user interface provided on a user device to: display one or more
relevant events with respect the target event based on the semantic
distance and the temporal distance; and provide an interactive
input to the data structure to select selecting a plurality of
events that influence the target event; and a display unit for
visualizing the influence of the selected features on the target
event.
15. The system of claim 14, wherein the temporal distance estimator
calculates the temporal distance between at least two events by
computing a temporal correlation between the at least two events
across time.
16. The system of claim 14, wherein the semantic distance estimator
calculates the semantic distance between the two events by at least
one of: measuring a contextual distance between one or more words
if the plurality of events are described by the words; measring a
contextual similarity or a semantic similarity as provided by a
domain model and a language model; and measuring the contextual
similarity or the semantic similarity based on analyzing the
relationships and the entities.
17. The system of claim 14, wherein the semantic distances and the
temporal distances are stored in a data structure which allows
separate storage and easy retrieval of each of the semantic
distance and the temporal distance with respect to an event-target
event pair.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application claims priority of Indian
provisional application serial number 3286/CHE/2012 filed on Aug.
10, 2012, and that application is incorporated in its entirety at
least by reference.
BACKGROUND
[0002] 1. Technical Field
[0003] The embodiments herein generally relate to data mining and
particularly relates to a method for extracting and processing
events from a large collection of data. The embodiments herein more
particularly relates to a method and system for visualizing
information by realizing the influence of one or more events on a
target event.
[0004] 2. Description of the Related Art
[0005] An entity is an unit of data which has an independent
self-explanatory meaning, and is also referred as an object that
makes an independent sense. A relationship is a property which
describes an association between two or more entities. The
relationship between two or more entities helps in understanding
the characteristics and behavior of the entities. An event is a
relationship that occurs between a entity and a time entity,
simply, it is a relationship with respect to time. The big data is
a large collection of data that comes from structured, unstructured
and semi-structured data sources. In an analytics context, the
entities, relationships and events manifest as variables or
features.
[0006] In big data analytics, the influence of other variables or
features on a given variable is often studied to make a prediction
of the value or state of the variable. This is a typical feature
selection problem that is magnified in the context of big data
analytics because of the large number of features/variables
available. The existing technology discusses various feature
selection techniques specific to different context. Feature
selection is quite a complex process and therefore a large number
of efforts have gone into addressing specific problems related to
feature selection in different domains.
[0007] The current feature selection procedures are mostly based on
machine learning/statistics/data mining techniques. All these
efforts require a good understanding of machine learning,
statistics techniques and also of the problem domain. However, the
existing techniques do not provide a simple, generic strategy that
can be applied to all contexts.
[0008] In big data analytics, the problem of predicting the
occurrence of events often comes across. The occurrence of a given
event is greatly influenced by the occurrences of many other events
that happen simultaneously. However, not all of the events bear
equal influence on the target event. To predict the occurrence or
the state of a target event, it is important to identify the events
that bear high influence on the target event. This reduces the
problem to that of feature selection or dimension reduction.
Feature selection typically requires utilization of domain
knowledge combined with knowledge of statistics, data mining and
machine learning.
[0009] Hence, there is a need for a method and system for
visualizing the influence of one or more events on a target event
interactively. Also, there is a need for a method and system for
performing feature selection without the need for deep
understanding of statistics, machine learning or the problem
domain. Further, there is a need for a method and system for
providing effective visualization of the feature selection.
Moreover, there is a need for a method and system for enabling
feature selection from various context perspectives.
[0010] The abovementioned shortcomings, disadvantages and problems
are addressed herein and which will be understood by reading and
studying the following specification.
SUMMARY
[0011] The primary object of the embodiments herein is to provide a
method and system for visualizing the influence of one or more
events on a target event interactively.
[0012] Also, there is a need for a method and system for providing
a simple and intuitive method of feature selection which does not
require an understanding of statistics, machine learning or the
problem domain.
[0013] Another object of the embodiments herein is to provide a
method and system which enables effective feature selection to
identify the relevant events that influence the target event.
[0014] Another object of the embodiments herein is to provide a
method and system for visualizing information which employs an
information dimension for representing information based on
semantic and temporal relatedness.
[0015] Another object of the embodiments herein is to provide a
method and system for computing semantic distance and temporal
distance between one or more influencing events and a target
event.
[0016] Another object of the embodiments herein is to provide a
method and system for representing information on semantic vs.
temporal distance axes.
[0017] These and other objects and advantages of the present
embodiments will become readily apparent from the following
detailed description taken in conjunction with the accompanying
drawings.
[0018] The various embodiment herein describe a method for
providing information visualization comprising, identifying a
plurality of events from a big data, calculating a temporal
distance between at least two events, calculating a semantic
distance between the at least two events, storing the calculated
semantic distance and the temporal distance with respect to the at
least two events in a data structure, providing the semantic
distance and the temporal distances calculated between the at least
two events on a visual representation, identifying one or more
relevant events with respect to the target event based on the
semantic distance and the temporal distance through visualization,
selecting a plurality of events that influence the target event and
examining a pattern of the plurality of events to realize possible
collinearity of the events to limit the influencing events
considered for feature analysis.
[0019] According to an embodiment herein, realizing the
collinearity of the events comprises identifying at least two
events sharing an exact relationship and reducing the number of
variables to ensure effective feature selection.
[0020] According to an embodiment herein, an event is defined as a
relationship which occurred at an instant of time.
[0021] According to an embodiment herein, the method for providing
information visualization further comprises selecting a new target
event and re-visualizing the influence of the selected features on
the new target event.
[0022] According to an embodiment herein, the feature selection is
defined as selecting a plurality of variables having an influence
on the occurrence of the event.
[0023] According to an embodiment herein, calculating the temporal
distance between at least two events comprise computing temporal
correlation between the at least two events. The temporal
correlation is measure of correlation of the events across
time.
[0024] According to an embodiment herein, the temporal distance is
calculated on the basis of time series data obtained from big
data.
[0025] According to an embodiment herein, calculating the semantic
distance between the two events comprises at least one of measuring
a contextual distance between one or more words if the plurality of
events are described by the words, measuring a contextual
similarity or a semantic similarity as provided by a domain model
and a language model and measuring the contextual or the semantic
similarity based on analyzing the relationships and the
entities.
[0026] According to an embodiment herein, the semantic distance is
calculated using structured data and an unstructured data or a
combination of structured data and unstructured data.
[0027] According to an embodiment herein, the method for providing
information visualization further comprises storing the semantic
distance and the temporal distances between the at least two events
in a data structure capable of storing both the semantic distances
and the temporal distances separately.
[0028] According to an embodiment herein, a value corresponding to
the semantic distance and the temporal distance is in a preset
numerical range or correspond to a discrete values range, with an
event being closest to itself both temporally and semantically.
[0029] According to an embodiment herein, the representation of the
visualization comprises events represented by a predefined shape,
negatively co-related events differentiated by the shape, different
events or event types represented by different colors, highly
influential events arranged around the target event and less
influential events arranged away from the targeted event as they
approach the origin.
[0030] According to an embodiment herein, the method for providing
information visualization further comprises setting limits for
semantic distances and the temporal distances for the plurality of
events influencing the target event and selecting the events
falling in the defined limit as the highly influential events.
[0031] The various embodiments herein describe a system for
providing information visualization comprising an event extractor
to extract a plurality of events from a big data, a semantic
distance estimator to calculate a temporal distance between at
least two events. The system further comprises a temporal distance
estimator to calculate a temporal distance between the at least two
events, a data structure to store the calculated temporal distance
and the semantic distance with respect to the a pair of events, an
user interface provided on a user device to display one or more
relevant events with respect to the target event based on the
semantic distance and the temporal distance and provide an
interactive input to the data structure to select a plurality of
events that influence the target event, and display unit for
visualizing the influence of the selected features on the target
event.
[0032] According to an embodiment herein, the temporal distance
estimator calculates the temporal distance between a pair of events
by computing a temporal correlation between the a pair of events
across time.
[0033] According to an embodiment herein, the semantic distance
estimator calculates the semantic distance between the two events
by at least one of measuring a contextual distance between one or
more words if the plurality of events are described by the words,
measuring a contextual similarity or a semantic similarity as
provided by a domain model and a language model and measuring the
contextual similarity or the semantic similarity based on analyzing
the relationships and the entities.
[0034] According to an embodiment herein, the semantic distances
and the temporal distances are stored in a data structure which
allows separate storage and easy retrieval of each of the semantic
distance and the temporal distance with respect to an event
pair.
[0035] These and other aspects of the embodiments herein will be
better appreciated and understood when considered in conjunction
with the following description and the accompanying drawings. It
should be understood, however, that the following descriptions,
while indicating preferred embodiments and numerous specific
details thereof are given by way of illustration and not of
limitation. Many changes and modifications may be made within the
scope of the embodiments herein without departing from the spirit
thereof, and the embodiments herein include all such
modifications.
BRIEF DESCRIPTION OF THE DRAWINGS
[0036] The other objects, features and advantages will occur to
those skilled in the art from the following description of the
preferred embodiment and the accompanying drawings in which:
[0037] FIG. 1 is a block diagram illustrating a process for
visualizing the influence of one or more events on a target event,
according to an embodiment of the present disclosure.
[0038] FIG. 2 is a block diagram illustrating a process for
computing temporal distance between events, according to an
embodiment of the present disclosure.
[0039] FIG. 3 is a block diagram illustrating a process for
computing semantic distance events, according to an embodiment of
the present disclosure.
[0040] FIG. 4 is a flow chart illustrating a method for visualizing
the effect of events on a target event, according to an embodiment
of the present disclosure.
[0041] FIG. 5 is a graph showing an example illustration of the
interactive visualization of events, according to an embodiment of
the present disclosure.
[0042] Although the specific features of the present embodiments
are shown in some drawings and not in others. This is done for
convenience only as each feature may be combined with any or all of
the other features in accordance with the present embodiments.
DETAILED DESCRIPTION OF THE DRAWINGS
[0043] In the following detailed description, a reference is made
to the accompanying drawings that form a part hereof, and in which
the specific embodiments that may be practiced is shown by way of
illustration. These embodiments are described in sufficient detail
to enable those skilled in the art to practice the embodiments and
it is to be understood that the logical, mechanical and other
changes may be made without departing from the scope of the
embodiments. The following detailed description is therefore not to
be taken in a limiting sense.
[0044] The various embodiment herein describe a method for
providing information visualization comprising, identifying a
plurality of events from a big data, calculating a temporal
distance between at least two events, calculating a semantic
distance between the at least two events, storing the calculated
semantic distance and the temporal distance with respect to the at
least two events in a data structure, providing the semantic
distance and the temporal distances calculated with respect to a
target event on a visual representation, identifying one or more
relevant events with respect to a target event based on the
semantic distance and the temporal distance through visualization,
selecting a plurality of events that influence the target event,
and examining a pattern of the plurality of events to realize
possible collinearity among events to reduce number of influencing
events to assist feature selection. Here the collinearity of the
plurality of events is suggested by approximately same semantic and
temporal distances on the visualization scheme. The collinear
events as defined herein are likely to have the same semantic and
temporal distance from the target event and hence appear very close
to each other or one on top of the other in visualization.
[0045] The method for providing information visualization further
comprises selecting a new target event and re-visualizing the
influence of the plurality of events on the new target event. Here
an event is defined as a relationship which occurred at an instant
of time and feature selection is defined as selecting a plurality
of variables having an influence on the occurrence of the
event.
[0046] The method of calculating the temporal distance between at
least two events comprise computing temporal correlation between
the at least two events. The temporal correlation is measure of
correlation of the events across time. The temporal distance is
calculated on the basis of time series data obtained from one of a
structured data, an unstructured data or a combination of
structured data and unstructured data.
[0047] The method of calculating the semantic distance between the
two events comprises at least one of measuring a contextual
distance between one or more words if the plurality of events are
described by the words, measuring a contextual similarity or a
semantic similarity as provided by a domain model and a language
model and measuring the contextual or the semantic similarity based
on analyzing the relationships and the entities. The semantic
distance is calculated on the basis of structured data and an
unstructured data or a combination of structured data and
unstructured data.
[0048] The method for providing information visualization further
comprises storing the semantic distance and the temporal distances
between the at least two events in a data structure capable of
storing both the semantic distances and the temporal distances
separately.
[0049] The value corresponding to the semantic distance and the
temporal distance is in a preset numerical range or correspond to a
discrete values range, with an event being closest to itself both
temporally and semantically.
[0050] The representation of the visualization comprises events
represented by a predefined shape, negatively co-related events
differentiated by the shape, different events represented by
different colors, different type of events represented by different
colors, highly influential events arranged around the target event,
and less influential events arranged away from the targeted event
as they approach the origin.
[0051] The method for providing information visualization further
comprises setting limits for semantic distances and the temporal
distances for the plurality of events influencing the target event
and selecting the events falling in the pre-set limit as the highly
influential events.
[0052] The various embodiments herein describe a system for
providing information visualization comprising an event extractor
to extract a plurality of events from a big data, a semantic
distance estimator to calculate a temporal distance between at
least two events. At least one event among the events is a target
event. The system further comprises a temporal distance estimator
to calculate a temporal distance between the at least two events, a
data structure to store the calculated semantic distance and the
semantic distance with respect to the at least two events, an user
interface provided on a user device to display one or more relevant
events with respect to the target event based on the semantic
distance and the temporal distance and provide an interactive input
to the data structure to select a plurality of events that
influence the target event, and display unit for visualizing the
influence of the selected features on the target event.
[0053] The temporal distance estimator calculates the temporal
distance between at least two events by computing a temporal
correlation between the at least two events across time.
[0054] The semantic distance estimator calculates the semantic
distance between the two events by at least one of: measuring a
contextual distance between one or more words if the plurality of
events are described by the words, measuring a contextual
similarity or a semantic similarity as provided by a domain model
and a language model and measuring the contextual similarity or the
semantic similarity based on analyzing the relationships and the
entities.
[0055] The semantic distances and the temporal distances are stored
in a data structure which allows separate storage and easy
retrieval of each of the semantic distance and the temporal
distance with respect to an event-target event pair.
[0056] FIG. 1 is a block diagram illustrating a process for
visualizing the influence of one or more events on a target event,
according to an embodiment of the present disclosure. The big data
101 represents a big data from which one or more events are
selected for analysis. The big data 101 comprises plurality of
events and variables that influence an event. The big data 101 also
comprises one or more events specifically termed as target events
on which influence is to be visualized. The event extractor 102
processes the big data 101 and extracts one or more events which
are specific for analysis. The extracted event is processed by a
semantic distance estimator 103 and a temporal distance estimator
104. The semantic distance estimator 103 calculates a semantic
distance between the target event and one or more other events.
Similarly, the temporal distance estimator 104 calculates a
temporal distance between the target event and at least one other
event. After computing both the semantic distance and the temporal
distance, the computed values are stored in a data structure 105.
The computed value of semantic distance and the temporal distance
with respect to any given pair of event is stored in the data
structure 105. The data structure 105 allows separate storage and
easy retrieval, of each of the semantic distance and the temporal
distance with respect to an event-target pair. A user device
communicates with the data structure 105 and retrieves the computed
value for display. The user device includes a user interface 106 by
which a user is able to query an interactive input to the data
structure 105 for selecting a plurality of events that influence a
target event according to their preferences. The user interface 106
is in conjunction with a display/visualization unit 107 for
displaying one or more relevant events with respect to the target
event based on the semantic distance and the temporal distance. The
display unit 107 enables the user to visualize the influence of the
selected feature on the target event.
[0057] FIG. 2 is a block diagram illustrating a process for
computing temporal distance between events, according to an
embodiment of the present disclosure. The temporal distance also
referred as temporal relatedness is measured in terms of temporal
correlation between two events, where one event is a target event.
The temporal correlation is a measure of correlation of events
across time. The temporal correlation assigns a value to the
co-occurrence of events over a time lag. The influence of an event
on a target event changes over a period of time in a specific
manner. The influence of other events on a particular event is
measured in terms of temporal correlation. The data collected over
a period of time reflects in temporal relatedness. Therefore, a
event related time series data 201, extracted from the big data
(combination of structured, unstructured and semi structured data)
101 is used to compute temporal correlations. The event related
time series data 201 is taken as input in a temporal distance
estimator. The temporal distance estimator performs the computation
of temporal correlation 202 based on the event related time series
data 201. If an event always co-occurred with the target event at
all instances of time and the magnitude of both the target event
and the other event remain proportional, then the temporal
correlation of two events is said to be high. The temporal
correlation can be either positive of negative, where the positive
correlation indicates a high probability of two events co-occurring
and the negative correlation indicates a high chance of two events
not co-occurring. The computed temporal distances between any two
events are then stored in the data structure 105. The user queries
the data structure 105 for visualizing the influence of the one or
more events on the target event.
[0058] FIG. 3 is a block diagram illustrating a process for
computing semantic distance events, according to an embodiment of
the present disclosure. The semantic distance also referred as
semantic relatedness defines a contextual/semantic relation between
any two events. For computation of semantic distance, information
is extracted from big data (the combination of structured,
semi-structured and unstructured data) 101, the events 301, one or
more domain models 302 and language models 303. The method of
computation adopted by semantic distance estimator depends on
nature of data available. At first, event data is first received as
input which is extracted from big data 101. The computation of
contextual or semantic similarity 304 is performed by semantic
distance estimator based on the type of event data 301 received.
For instance, when the event data is described by words, the
semantic distance is computed by measuring the contextual or
semantic distance between the words. The semantic distance
estimator also measures the contextual similarity on the basis of
data provided by ontologies, domain models 302 and language
repositories 303, on the basis of language models 304 or upon
analyzing relationships and entities. The computed semantic
distance is then stored in data structure 105 for displaying to a
user.
[0059] FIG. 4 is a flow chart illustrating a method for visualizing
the effect of events on a target event, according to an embodiment
of the present disclosure. The method comprises identifying a
plurality of events from the big data (401). The events are
extracted from the big data by an event extractor. The semantic
distance and the temporal distance are the calculated between every
pair of events (402). The semantic distance estimator calculates
the semantic distance between every pair of events. Similarly, the
temporal distance estimator calculates a temporal distance between
every pair of events. The computed semantic distance and temporal
distance values are then stored in the data structure. The
datastructure is then queried as per the requirement by a user for
visualizing the influence of the events on the target event (403).
A display unit is then adopted for visually representing the
calculated temporal and semantic distances between the at least two
events. The visual representation comprises a information
coordinate axes in which influencing events are represented by
different shapes, colors and sizes, and target event is placed at
the coordinate (404). Based on the semantic distance and the
temporal distance, the events which are relevant to the target
event are automatically identified (405). The most influencing
events are near to the target event. The user then selects the
plurality of events that influence the target event through
visualization (406). Further, the user is also allowed to change
the target event and visualize results particular to the new target
event interactively (407). Further the steps 404 to 406 are
repeated.
[0060] FIG. 5 is a graph showing an example illustration of the
interactive visualization of events, according to an embodiment of
the present disclosure. The visualization of events that influence
the target event is represented on an information dimension. The
information dimension comprises a new co-ordinate system consisting
of semantic and temporal distance axes starting from origin (0, 0).
A rectangular region 501 with the two axes as a side is the region
where the events are visually represented. The rectangular boxes
inside the rectangular region of different patterns represent
different events or different type of events. The one or more
events are represented by small rectangles (or any other shape).
The negatively co-related events are differentiated by the shape.
The different events or different types of events can also be
represented by different colors and shape. The different types of
shapes comprise but not limited to circular, square, rectangular
etc. The different shapes indicate different influences (positive
and negative). The rectangular box at the coordinate axes (1, 1) is
the target event 502. The events closer to the target event 502
have more influence on the target event 502. The highly influential
events are spread around the target event 502. While the FIG. 5 is
a simple two dimensional representation with (0, 0) as the origin
and (1, 1) as the extreme co-ordinate, the embodiments herein
provide multiple different representations each for a different
purpose. The influence of events that are spread away from the
target event 502 goes on decreasing as they approach the origin.
The user can set the limits of semantic and temporal distances, and
the events that fall, within the rectangle defined by these limits
indicate the most influential events. The user is also allowed to
change the target event 502 and the graphical representation is
re-casted with no effort as the data structure holds all relevant
information required to re-cast.
[0061] According to an embodiment herein, the data structure
storing the semantic distance and the temporal distance with
respect to an event-target event pair is in a matrix form, in which
the lower and upper triangles hold semantic and the temporal
distances respectively. The values corresponding to the semantic
and temporal distances in the matrix form vary from zero to one,
where one being the closest representing the semantic/temporal
distance of an event to itself.
[0062] The embodiments herein provide a simpler, intuitive,
interactive and easy to deploy visualization process. Also, the
interactive visualization uses the semantic and temporal distances
to identify the most relevant variables based on the guiding
principles of temporal and semantic distances. This interactive
visualization procedure works on most simple, heuristic and
intuitive principles making the understanding of results easy. The
results are displayed with a few clicks, without requiring user
input and visualization of influences on an event is made more
interactive and intuitive. While the computations of semantic and
temporal distances are complex, the complexities are hidden from
the user. The embodiments of the present disclosure provide immense
benefit in Retail, Healthcare Education, Governance, etc.
[0063] The foregoing description of the specific embodiments will
so fully reveal the general nature of the embodiments herein that
others can, by applying current knowledge, readily modify and/or
adapt for various applications such specific embodiments without
departing from the generic concept, and, therefore, such
adaptations and modifications should and are intended to be
comprehended within the meaning and range of equivalents of the
disclosed embodiments. It is to be understood that the phraseology
or terminology employed herein is for the purpose of description
and not of limitation. Therefore, while the embodiments herein have
been described in terms of preferred embodiments, those skilled in
the art will recognize that the embodiments herein can be practiced
with modification.
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