CN112069228B - Event sequence-oriented causal visualization method and device - Google Patents
Event sequence-oriented causal visualization method and device Download PDFInfo
- Publication number
- CN112069228B CN112069228B CN202010829972.7A CN202010829972A CN112069228B CN 112069228 B CN112069228 B CN 112069228B CN 202010829972 A CN202010829972 A CN 202010829972A CN 112069228 B CN112069228 B CN 112069228B
- Authority
- CN
- China
- Prior art keywords
- event
- causal
- sequence
- events
- visualization
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000001364 causal effect Effects 0.000 title claims abstract description 201
- 238000007794 visualization technique Methods 0.000 title claims abstract description 19
- 238000012800 visualization Methods 0.000 claims abstract description 69
- 238000011161 development Methods 0.000 claims abstract description 10
- 238000005065 mining Methods 0.000 claims abstract description 5
- 238000005516 engineering process Methods 0.000 claims description 36
- 238000004458 analytical method Methods 0.000 claims description 9
- 230000000694 effects Effects 0.000 claims description 7
- 238000001914 filtration Methods 0.000 claims description 7
- 238000009412 basement excavation Methods 0.000 claims description 6
- 239000007787 solid Substances 0.000 claims description 4
- 230000000007 visual effect Effects 0.000 claims description 4
- 230000001747 exhibiting effect Effects 0.000 claims description 2
- 238000000034 method Methods 0.000 description 16
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2474—Sequence data queries, e.g. querying versioned data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/248—Presentation of query results
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Probability & Statistics with Applications (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Fuzzy Systems (AREA)
- User Interface Of Digital Computer (AREA)
Abstract
The invention discloses a causal visualization method and a causal visualization device for an event sequence, wherein the causal visualization method and the causal visualization device comprise the following steps: inputting a causal relation and an event sequence conforming to the causal relation, and generating a layout about event nodes by using the existing causal network visualization; counting event information according to an event sequence, and displaying the event information through icon visualization, wherein the generated event icons are placed on corresponding event layouts in a causal network visualization, and the event information comprises event categories, event occurrence time distribution and event occurrence frequency; digging out frequently-occurring event occurrence modes in the event sequence by using a frequent pattern mining algorithm, and visualizing the event occurrence modes obtained by digging in a time line mode; for each event occurrence mode, generating a causal event stream according to event causal relations in a causal network, and displaying the development sequence of the events in time and the causal relations among the events; a visualization of all event sequences is generated in the form of a timeline.
Description
Technical Field
The present invention relates to the field of computer visualization, and in particular, to a causal visualization method and apparatus for an event sequence.
Background
Causal analysis of event sequence data can characterize relationships between events, and can play an important role in various areas, such as marketing behavior analysis, electronic medical records and healthcare analysis, error log analysis, and so forth. Control experiments are a common method of deriving the cause of an event, but due to the high cost of experimental setup, control experiments are not applicable and applicable in many cases. Based on this, expert scientists have invented a series of causal detection algorithms to automatically infer causal relationships contained therein from observed data. Visualization has also been applied to causal analysis due to the validity of the analysis. Researchers have invented a collection of visualizations suitable for revealing causal networks. However, these visualizations are only applicable to causal networks that present static tabular data, and do not present causal networks in event sequences with time information. On the other hand, existing event visualization methods typically use a time-axis based visual representation to visualize a sequence of events. However, displaying causal information of events to explain the occurrence of the events while preserving the chronological order of the events in the visualization has not yet been solved. Neither the current event visualization nor the causal visualization involves visualization of the causal relationship of the event, and a specific theory and visualization method is lacking.
Disclosure of Invention
The embodiment of the invention aims to provide a causal visualization method and a causal visualization device for an event sequence, so as to solve the problem that the occurrence of the event is not solved yet by displaying causal relation information of the event while maintaining the time sequence of the event in the visualization.
In order to achieve the above purpose, the technical scheme adopted by the embodiment of the invention is as follows:
in a first aspect, a causal visualization method for a sequence of events, comprising:
inputting a causal relation and an event sequence conforming to the causal relation, and generating a layout about event nodes by using the existing causal network visualization;
counting event information according to an event sequence, and displaying the event information through icon visualization, wherein the generated event icons are placed on corresponding event layouts in a causal network visualization, and the event information comprises event categories, event occurrence time distribution and event occurrence frequency;
digging out frequently-occurring event occurrence modes in the event sequence by using a frequent pattern mining algorithm, and visualizing the event occurrence modes obtained by digging in a time line mode;
for each event occurrence mode, generating a causal event stream according to event causal relations in a causal network, and displaying the development sequence of the events in time and the causal relations among the events;
a visualization of all event sequences is generated in the form of a timeline.
In a second aspect, an embodiment of the present invention further provides an event sequence-oriented cause and effect visualization device, including:
the input visualization module is used for inputting causal relations and event sequences conforming to the causal relations, and generating layout related to event nodes by using the existing causal network visualization;
the causal graph module is used for counting event information according to the event sequence and displaying the event information through icon visualization, and the generated event icons are placed on corresponding event layouts in causal network visualization, wherein the event information comprises event categories, event occurrence time distribution and event occurrence frequency;
the sequence mode module is used for excavating event occurrence modes frequently appearing in the event sequence by using a frequent mode excavation algorithm, and visualizing the event occurrence modes obtained by excavation in a time line mode;
the causal flow module is used for generating a causal event flow according to the causal relation of the events in the causal network for each event occurrence mode and displaying the development sequence of the events in time and the causal relation among the events;
and the sequence detail module is used for generating visualizations of all event sequences in a time line mode.
In a third aspect, an embodiment of the present invention further provides an event sequence-oriented causal visualization method, applied to causal visualization of a motion event sequence of a table tennis ball, including:
inputting a causal relation between table tennis technologies and a table tennis technology sequence conforming to the causal relation, and generating a layout related to the causal relation of the table tennis technologies by using the existing causal network visualization;
counting event information according to a table tennis technical sequence, displaying the event information through icon visualization, using the color of the icon to represent event types, using the radian of the outer ring to represent frequency, using a pie chart in the icon to represent time distribution, and placing the generated event icons on corresponding event layouts in causal network visualization, wherein the event information comprises the types of technologies, the time distribution used by the technologies and the frequency used by the technologies;
digging out frequently appearing technology use modes in a table tennis technology sequence by using a frequent mode digging algorithm, and visualizing the obtained table tennis technology use modes in a time line mode;
for each table tennis technology use mode, generating a causal event stream according to event causal relation in a causal network, and displaying the development sequence of the table tennis technology in time and the causal relation among the technologies;
a visualization of all table tennis technical sequences is generated in the form of a timeline.
According to the technical scheme, the method has the beneficial effects that the method uses the layout method based on force guidance to realize the common display of the event time information and the cause and effect information, the method avoids overlapping and crossing on the layout, the cause and effect relationship and the time sequence are clearly identified, and the readability is ensured.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
FIG. 1 is a block flow diagram of a causal visualization method for event sequences according to an embodiment of the present invention;
FIG. 2 is an overall view of a causal visualization of a sequence of events in an embodiment of the present invention;
FIG. 3 is a visualization of causal event flow in an embodiment of the present invention;
FIG. 4 is a causal structure extracted in an embodiment of the invention;
FIG. 5 is a block diagram of a causal visualization device for event sequences according to an embodiment of the present invention.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Example 1:
FIG. 1 is a flow chart of a causal visualization method for event sequence provided in an embodiment of the present invention; the causal visualization method for the event sequence provided by the embodiment comprises the following steps:
step S101, inputting a causal relation and an event sequence conforming to the causal relation, and generating a layout about event nodes by using the existing causal network visualization;
specifically, each node in the N causal networks G represents an event, and the different causal networks G share a set of events. Each causal network G contains a plurality of event sequences S.
Step S102, event information is counted according to an event sequence, the event information is displayed through icon visualization, and the generated event icons are placed on corresponding event layouts in causal network visualization, wherein the event information comprises event categories, event occurrence time distribution and event occurrence frequency;
the event information is counted according to the event sequence, and the method specifically comprises the following steps:
category of statistical event: for event data with metadata, classifying the events according to the existing categories; for data without metadata, each event becomes a type separately; using the color of the icon to represent event category;
counting the frequency of events: counting occurrence frequencies of events in all event sequences, wherein the occurrence frequencies are represented by the radian of an outer circular ring in the icon, and the larger the radian is, the higher the occurrence frequency of the events is;
statistics of time distribution of events: dividing each event sequence equally according to the time sequence of 4, counting the occurrence frequency of the event on each part, and forming time analysis; non-statistics of sequence lengths exceeding 4; the time distribution is represented by a pie chart within the icon, the pie chart representing the frequency of occurrence of events for the 1-4 parts in a clockwise order, the higher the frequency, the larger the corresponding block size in the pie chart.
Step S103, excavating event occurrence modes frequently appearing in the event sequence by using a frequent mode excavation algorithm, and visualizing the excavated event occurrence modes in a time line mode;
and mining an event sub-sequence with the occurrence frequency of more than 50% as a frequent event mode.
Step S104, for each event occurrence mode, generating a causal event stream according to the causal relationship of the events in the causal network, and displaying the time development sequence of the events and the causal relationship between the events, specifically comprising the following sub-steps:
step S1041, counting the causal relationship involved in the event sequence: filtering irrelevant causal relations in a causal network according to events in the event sequence and generating a causal sub-network corresponding to the event sequence; an irrelevant definition is that the path (path) where the causal relationship is located does not have any event in the directed edge pointing event sequence;
step S1042, obtaining a causal structure: combining the causal relationship obtained by filtering to obtain causal structures, namely a Chain structure, a Fork structure and a V structure;
step S1043, obtaining a topological order of the events in the event sequence: obtaining topological ordering of events in the event sequence, and optionally, ordering of event rows serving as father nodes before events serving as child nodes in a causal network;
step S1044, visualizing the layout: generating a horizontal axis of the event from left to right according to the occurrence sequence of the event, and generating a vertical axis of the event from top to bottom according to the topological order of the event, wherein the positions on the coordinate axes are marked with corresponding event names; generating a dot representing the event, and referring to the positions of the events on the coordinate axes of the ordinate and the abscissa; connecting event dots from left to right; for events with father nodes in the causal sub-network, placing a solid rectangle beside the dots, which is called an event rectangle, wherein the length of the rectangle represents the number of the father nodes of the event; connecting event dots and event rectangles in a stream form, and displaying causal relations in a causal sub-network; the position of the event rectangle on the transverse axis is adjusted in a force guiding mode according to the causal structure, so that the readability of the causal structure is ensured; in this visualization, the time sequence of event occurrence can be seen from left to right, and the causal relationship of event occurrence can be seen from top to bottom.
Step S105, generating visualizations of all event sequences in the form of a timeline.
Specifically, each event is represented by the color of a dot, and the dot is arranged on the horizontal axis to display the occurrence sequence of the event in the sequence.
Example 2:
the invention is based on a causal visualization method facing event sequences, which is applied to analyzing the motion event sequences of table tennis and comprises the following steps:
step one: inputting a causal relation between table tennis technologies and a table tennis technology sequence conforming to the causal relation; a layout of causal relationships with respect to table tennis technology is generated using existing causal network visualizations.
Step two: counting event information according to the table tennis technical sequence, wherein the event information comprises technical categories, technical use time distribution and technical use frequency, and information of the three aspects is visually displayed through icons; using the color of the icon to represent event category, the radian of the outer ring to represent frequency, and using the pie chart in the icon to represent time distribution; the generated event icons are placed on the corresponding event layouts in the causal network visualization; the resulting causal network visualization is shown in fig. 2 (a).
Step three: digging out frequently occurring technology use modes in a table tennis technology sequence by using an existing frequent mode digging algorithm; the table tennis technology usage patterns obtained by excavation are visualized in the form of a time line.
Step four: for each table tennis technology usage pattern, a causal event stream is generated according to event causal relationships in the causal network, as shown in fig. 2 (B), showing the development sequence of the table tennis technology in time and the causal relationships between technologies.
This step is the core of the present invention and is divided into the following sub-steps.
1) Counting causal relationship related in table tennis technical sequence
Filtering irrelevant causal relations in a causal network according to the technology in the table tennis technical sequence and generating a causal sub-network corresponding to the table tennis technical sequence; the irrelevant definition is that the path where the causality is located does not have any technology in the edge-pointing table tennis technical sequence;
2) Acquisition cause and effect structure
Combining the filtered causal relationships to obtain causal structures, as shown in fig. 4, namely a Chain structure (representing Chain causal relationships), a Fork structure (representing causal relationships of common ancestors), and a V structure (representing causal relationships of common decisions);
3) Acquiring topological ordering of events in a sequence of events
The sequence of events in the topological ordering meets the requirement that events serving as father nodes in the causal network are arranged before events serving as child nodes; the order meeting the standard is multiple, and any order meeting the standard is selected in the step;
4) Visual layout
As shown in fig. 3 (a), the horizontal axis is generated from left to right according to the occurrence order of the table tennis technique, and the vertical axis is generated from top to bottom according to the causal topological ordering of the table tennis technique, as shown in fig. 3 (C). The positions on the coordinate axes are marked with corresponding technical names; as shown in fig. 3 (B, F), generating event dots representing the technique, and referring to the positions of the technique on the coordinate axes on the ordinate and the abscissa; connecting event dots from left to right; for the table tennis technique in which a parent node exists in the causal child network, as shown in fig. 3 (E), a solid rectangle, called an event rectangle, is placed beside the dots, and the length of the rectangle represents the number of the parent nodes of the table tennis technique; as shown in fig. 3 (D), the event dots and event rectangles are connected in a stream, exhibiting causal relationships in a causal sub-network; as shown in fig. 4, the position of the event rectangle on the horizontal axis is adjusted in a force-directed manner according to the causal structure in 2), ensuring the readability of the causal structure; in the visualization, the time sequence of the table tennis technique can be seen from left to right, and the causal relationship used by the table tennis technique can be seen from top to bottom;
step five: a visualization of all table tennis technical sequences is generated in the form of a timeline, as shown in fig. 1 (C).
Example 3:
referring to fig. 5, an embodiment of the present invention further provides an event sequence-oriented cause and effect visualization device, which can execute the event sequence-oriented cause and effect visualization method provided by any of the embodiments of the present invention, and has a function module and beneficial effects corresponding to the execution of the method. As shown in fig. 5, includes:
an input visualization module 901, configured to input a causal relationship and a causal event sequence, and generate a layout for event nodes using an existing causal network visualization;
the causal graph module 902 is configured to count event information according to an event sequence, and display the event information through icon visualization, where the generated event icon is placed on a corresponding event layout in the causal network visualization, and the event information includes a category of an event, a time distribution of occurrence of the event, and a frequency of occurrence of the event;
a sequence pattern module 903, configured to mine an event occurrence pattern frequently occurring in the event sequence using a frequent pattern mining algorithm, and visualize the mined event occurrence pattern in a timeline form;
a causal event stream module 904, configured to generate, for each event occurrence pattern, a causal event stream according to the causal relationships of the events in the causal network, and show the time development sequence of the events and the causal relationships between the events;
a sequence details module 905 for generating a visualization of all event sequences in the form of a timeline.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiment of the apparatus is merely exemplary, and for example, the division of the units may be a logic function division, and there may be another division manner when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (7)
1. A causal visualization method for a sequence of events, comprising:
inputting a causal relation and an event sequence conforming to the causal relation, and generating a layout about event nodes by using the existing causal network visualization;
counting event information according to an event sequence, and displaying the event information through icon visualization, wherein the generated event icons are placed on corresponding event layouts in a causal network visualization, and the event information comprises event categories, event occurrence time distribution and event occurrence frequency;
digging out frequently-occurring event occurrence modes in the event sequence by using a frequent pattern mining algorithm, and visualizing the event occurrence modes obtained by digging in a time line mode;
for each event occurrence mode, generating a causal event stream according to event causal relations in a causal network, and displaying the development sequence of the events in time and the causal relations among the events;
a visualization of all event sequences is generated in the form of a timeline.
2. The event sequence oriented causal visualization method of claim 1, wherein the event information is counted according to the event sequence, and specifically comprises:
category of statistical event: for event data with metadata, classifying the events according to the existing categories; for data without metadata, each event becomes a type separately; using the color of the icon to represent event category;
counting the frequency of events: counting occurrence frequencies of events in all event sequences, wherein the occurrence frequencies are represented by the radian of an outer circular ring in the icon, and the larger the radian is, the higher the occurrence frequency of the events is;
statistics of time distribution of events: dividing each event sequence equally according to the time sequence of 4, counting the occurrence frequency of the event on each part, and forming time analysis; non-statistics of sequence lengths exceeding 4; the time distribution is represented by a pie chart within the icon, the pie chart representing the frequency of occurrence of events for the 1-4 parts in a clockwise order, the higher the frequency, the larger the corresponding block size in the pie chart.
3. The event sequence oriented causal visualization method of claim 1, wherein for each event occurrence pattern, a causal event stream is generated according to event causal relationships in a causal network, showing a sequence of events developed in time and causal relationships between events, and specifically comprising:
counting causal relationships involved in a sequence of events: filtering irrelevant causal relations in a causal network according to events in the event sequence and generating a causal sub-network corresponding to the event sequence; an irrelevant definition is that the path (path) where the causal relationship is located does not have any event in the directed edge pointing event sequence;
acquiring a causal structure: combining the causal relationship obtained by filtering to obtain causal structures, namely a Chain structure, a Fork structure and a V structure;
obtaining topological ordering of events in an event sequence: obtaining topological ordering of events in the event sequence, and optionally, ordering of event rows serving as father nodes before events serving as child nodes in a causal network;
visual layout: generating a horizontal axis of the event from left to right according to the occurrence sequence of the event, and generating a vertical axis of the event from top to bottom according to the topological order of the event, wherein the positions on the coordinate axes are marked with corresponding event names; generating a dot representing the event, and referring to the positions of the events on the coordinate axes of the ordinate and the abscissa; connecting event dots from left to right; for events with father nodes in the causal sub-network, placing a solid rectangle beside the dots, which is called an event rectangle, wherein the length of the rectangle represents the number of the father nodes of the event; connecting event dots and event rectangles in a stream form, and displaying causal relations in a causal sub-network; the position of the event rectangle on the transverse axis is adjusted in a force guiding mode according to the causal structure, so that the readability of the causal structure is ensured; in this visualization, the time sequence of event occurrence can be seen from left to right, and the causal relationship of event occurrence can be seen from top to bottom.
4. A causal visualization device for a sequence of events, comprising:
the input visualization module is used for inputting causal relations and event sequences conforming to the causal relations, and generating layout related to event nodes by using the existing causal network visualization;
the causal graph module is used for counting event information according to the event sequence and displaying the event information through icon visualization, and the generated event icons are placed on corresponding event layouts in causal network visualization, wherein the event information comprises event categories, event occurrence time distribution and event occurrence frequency;
the sequence mode module is used for excavating event occurrence modes frequently appearing in the event sequence by using a frequent mode excavation algorithm, and visualizing the event occurrence modes obtained by excavation in a time line mode;
the causal flow module is used for generating a causal event flow according to the causal relation of the events in the causal network for each event occurrence mode and displaying the development sequence of the events in time and the causal relation among the events;
and the sequence detail module is used for generating visualizations of all event sequences in a time line mode.
5. The event sequence oriented cause and effect visualization device of claim 4, wherein the event information is counted according to the event sequence, and specifically comprises:
category of statistical event: for event data with metadata, classifying the events according to the existing categories; for data without metadata, each event becomes a type separately; using the color of the icon to represent event category;
counting the frequency of events: counting occurrence frequencies of events in all event sequences, wherein the occurrence frequencies are represented by the radian of an outer circular ring in the icon, and the larger the radian is, the higher the occurrence frequency of the events is;
statistics of time distribution of events: dividing each event sequence equally according to the time sequence of 4, counting the occurrence frequency of the event on each part, and forming time analysis; non-statistics of sequence lengths exceeding 4; the time distribution is represented by a pie chart within the icon, the pie chart representing the frequency of occurrence of events for the 1-4 parts in a clockwise order, the higher the frequency, the larger the corresponding block size in the pie chart.
6. The event sequence oriented causal visualization apparatus of claim 4, wherein for each event occurrence pattern, a causal event stream is generated based on event causal relationships in a causal network, exhibiting a sequence of events developed over time and causal relationships between events, comprising:
counting causal relationships involved in a sequence of events: filtering irrelevant causal relations in a causal network according to events in the event sequence and generating a causal sub-network corresponding to the event sequence; an irrelevant definition is that the path (path) where the causal relationship is located does not have any event in the directed edge pointing event sequence;
acquiring a causal structure: combining the causal relationship obtained by filtering to obtain causal structures, namely a Chain structure, a Fork structure and a V structure;
obtaining topological ordering of events in an event sequence: obtaining topological ordering of events in the event sequence, and optionally, ordering of event rows serving as father nodes before events serving as child nodes in a causal network;
visual layout: generating a horizontal axis of the event from left to right according to the occurrence sequence of the event, and generating a vertical axis of the event from top to bottom according to the topological order of the event, wherein the positions on the coordinate axes are marked with corresponding event names; generating a dot representing the event, and referring to the positions of the events on the coordinate axes of the ordinate and the abscissa; connecting event dots from left to right; for events with father nodes in the causal sub-network, placing a solid rectangle beside the dots, which is called an event rectangle, wherein the length of the rectangle represents the number of the father nodes of the event; connecting event dots and event rectangles in a stream form, and displaying causal relations in a causal sub-network; the position of the event rectangle on the transverse axis is adjusted in a force guiding mode according to the causal structure, so that the readability of the causal structure is ensured; in this visualization, the time sequence of event occurrence can be seen from left to right, and the causal relationship of event occurrence can be seen from top to bottom.
7. The causal visualization method for the event sequence is applied to causal visualization of the motion event sequence of the table tennis and is characterized by comprising the following steps of:
inputting a causal relation between table tennis technologies and a table tennis technology sequence conforming to the causal relation, and generating a layout related to the causal relation of the table tennis technologies by using the existing causal network visualization;
counting event information according to a table tennis technical sequence, displaying the event information through icon visualization, using the color of the icon to represent event types, using the radian of the outer ring to represent frequency, using a pie chart in the icon to represent time distribution, and placing the generated event icons on corresponding event layouts in causal network visualization, wherein the event information comprises the types of technologies, the time distribution used by the technologies and the frequency used by the technologies;
digging out frequently appearing technology use modes in a table tennis technology sequence by using a frequent mode digging algorithm, and visualizing the obtained table tennis technology use modes in a time line mode;
for each table tennis technology use mode, generating a causal event stream according to event causal relation in a causal network, and displaying the development sequence of the table tennis technology in time and the causal relation among the technologies;
a visualization of all table tennis technical sequences is generated in the form of a timeline.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010829972.7A CN112069228B (en) | 2020-08-18 | 2020-08-18 | Event sequence-oriented causal visualization method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010829972.7A CN112069228B (en) | 2020-08-18 | 2020-08-18 | Event sequence-oriented causal visualization method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112069228A CN112069228A (en) | 2020-12-11 |
| CN112069228B true CN112069228B (en) | 2023-12-19 |
Family
ID=73661966
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010829972.7A Active CN112069228B (en) | 2020-08-18 | 2020-08-18 | Event sequence-oriented causal visualization method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112069228B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106203631A (en) * | 2016-07-05 | 2016-12-07 | 中国科学院计算技术研究所 | The parallel Frequent Episodes Mining of description type various dimensions sequence of events and system |
| CN110233849A (en) * | 2019-06-20 | 2019-09-13 | 电子科技大学 | The method and system of network safety situation analysis |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180107529A1 (en) * | 2016-10-13 | 2018-04-19 | Nec Laboratories America, Inc. | Structural event detection from log messages |
| US10977154B2 (en) * | 2018-08-03 | 2021-04-13 | Dynatrace Llc | Method and system for automatic real-time causality analysis of end user impacting system anomalies using causality rules and topological understanding of the system to effectively filter relevant monitoring data |
-
2020
- 2020-08-18 CN CN202010829972.7A patent/CN112069228B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106203631A (en) * | 2016-07-05 | 2016-12-07 | 中国科学院计算技术研究所 | The parallel Frequent Episodes Mining of description type various dimensions sequence of events and system |
| CN110233849A (en) * | 2019-06-20 | 2019-09-13 | 电子科技大学 | The method and system of network safety situation analysis |
Non-Patent Citations (2)
| Title |
|---|
| 基于时态逻辑的多时间序列挖掘模型;陈卓;杨炳儒;周法国;李琳娜;;辽宁工程技术大学学报(自然科学版)(04);全文 * |
| 基于条件概率的临床诊疗事件打包算法研究;黄浩未;金涛;王建民;;计算机集成制造系统(05);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112069228A (en) | 2020-12-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Trier | Research note—towards dynamic visualization for understanding evolution of digital communication networks | |
| Valdivia et al. | Analyzing dynamic hypergraphs with parallel aggregated ordered hypergraph visualization | |
| US10331742B2 (en) | Thresholds for key performance indicators derived from machine data | |
| Smith et al. | Analyzing (social media) networks with NodeXL | |
| US9984148B2 (en) | Visualization of graphical representation of log files | |
| US20110148880A1 (en) | Smart Impact Views | |
| Kintzel et al. | Monitoring large ip spaces with clockview | |
| US8856766B2 (en) | Generating layouts for graphs of data flow applications | |
| Borland et al. | Selection bias tracking and detailed subset comparison for high-dimensional data | |
| CN112069228B (en) | Event sequence-oriented causal visualization method and device | |
| Grabowicz et al. | Fast filtering and animation of large dynamic networks | |
| US20090132958A1 (en) | Distinct Groupings of Related Objects for Display in a User Interface | |
| Etoty et al. | A survey of visualization tools assessed for anomaly-based intrusion detection analysis | |
| US20210405827A1 (en) | Display control device, display control method, and display control program | |
| Vanamala et al. | Interactive Visualization Dashboard for Common Attack Pattern Enumeration Classification | |
| Diehl et al. | Multivariate networks in software engineering | |
| US11614850B2 (en) | System and method for analysis and visualization of incident data | |
| US9405651B1 (en) | Apparatus and method for processing log file data | |
| CN115696337A (en) | Mobile terminal safety monitoring analysis method and device | |
| KR20140059054A (en) | Method and apparatus for visualizing social network elements | |
| Kulahcioglu et al. | LogVis: Graph-Assisted Visual Analysis of Event Logs from Industrial Equipment. | |
| Panagiotidis et al. | Graph exploration by multiple linked metric views | |
| EP2953016B1 (en) | System and method for interactively visualizing rules and exceptions | |
| Roberts et al. | RiverState: A Visual Metaphor Representing Millions of Time-Oriented State Transitions. | |
| US20230230300A1 (en) | Display control device, display control method and display control program |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |