SYSTEM AND METHOD FOR VISUAL ANALYSIS OF EVENT SEQUENCES

1 . A computer-implemented method for a visual analysis of event sequences, the method comprising:

receiving, from one or more data sources, event-related data for a set of event sequences;

selecting one or more event grouping criteria from a list of event grouping criteria, wherein the list of event grouping criteria comprises a criterion of grouping by an event location, a criterion of grouping by an event entity, and a criterion of grouping by an event time;

grouping, into one or more groups, event sequences within the set of event sequences based on the one or more event grouping criteria;

calculating sequence metrics for each group of the one or more groups of the event sequences within the set of event sequences; and

displaying, on a user interface, the sequence metrics for the set of event sequences.

2 . The computer-implemented method of claim 1 , further comprising:

receiving, via the user interface, a filter selection for the set of event sequences;

generating a new representative sample subset of the set of event sequences based on the filter selection;

calculating new sequence metrics for the new representative sample subset of the set of event sequences; and

displaying, on the user interface, the new sequence metrics for the set of event sequences.

3 . The computer-implemented method of claim 2 , further comprising:

generating a visual representation of each group of the one or more groups of the event sequences within the set of event sequences;

displaying, on the user interface, the visual representation of each group of event sequences within the set of event sequences;

generating a new visual representation of the new representative sample subset of the set of event sequences; and

displaying, on the user interface, the new visual representation of the new representative sample subset of the set of event sequences.

4 . The computer-implemented method of claim 1 , further comprising:

receiving, via the user interface, a selection to modify a visual representation of the set of event sequences;

generating a new visual representation of the set of event sequences based on the selection; and

displaying, on the user interface, the new visual representation of the set of event sequences.

5 . The computer-implemented method of claim 1 , wherein grouping the event sequences further comprises:

determining a probability of events occurring in a same event sequence; and

responsive to determining that the probability exceeds a threshold value, grouping the events in the same event sequence.

6 . The computer-implemented method of claim 1 , wherein the sequence metrics comprise a duration of each event sequence, a number of events in each event sequence, and a time gap between consecutive event sequences.

7 . The computer-implemented method of claim 1 , wherein grouping the event sequences further comprises:

utilizing a machine learning model to analyze the event-related data for the grouping.

8 . A non-transitory computer-readable medium storing instructions, which when executed by a processor, cause the processor to perform operations comprising:

receiving, from one or more data sources, event-related data for a set of event sequences;

selecting one or more event grouping criteria from a list of event grouping criteria, wherein the list of event grouping criteria comprises a criterion of grouping by an event location, a criterion of grouping by an event entity, and a criterion of grouping by an event time;

grouping, into one or more groups, event sequences within the set of event sequences based on the one or more event grouping criteria;

calculating sequence metrics for each group of the one or more groups of the event sequences within the set of event sequences; and

displaying, on a user interface, the sequence metrics for the set of event sequences.

9 . The non-transitory computer-readable medium of claim 8 , the operations further comprising:

receiving, via the user interface, a filter selection for the set of event sequences;

generating a new representative sample subset of the set of event sequences based on the filter selection;

calculating new sequence metrics for the new representative sample subset of the set of event sequences; and

displaying, on the user interface, the new sequence metrics for the set of event sequences.

10 . The non-transitory computer-readable medium of claim 9 , the operations further comprising:

generating a visual representation of each group of the one or more groups of the event sequences within the set of event sequences;

displaying, on the user interface, the visual representation of each group of event sequences within the set of event sequences;

generating a new visual representation of the new representative sample subset of the set of event sequences; and

displaying, on the user interface, the new visual representation of the new representative sample subset of the set of event sequences.

11 . The non-transitory computer-readable medium of claim 8 , the operations further comprising:

receiving, via the user interface, a selection to modify a visual representation of the set of event sequences;

generating a new visual representation of the set of event sequences based on the selection; and

displaying, on the user interface, the new visual representation of the set of event sequences.

12 . The non-transitory computer-readable medium of claim 8 , wherein grouping the event sequences further comprises:

determining a probability of events occurring in a same event sequence; and

responsive to determining that the probability exceeds a threshold value, grouping the events in the same event sequence.

13 . The non-transitory computer-readable medium of claim 8 , wherein the sequence metrics comprise a duration of each event sequence, a number of events in each event sequence, and a time gap between consecutive event sequences.

14 . The non-transitory computer-readable medium of claim 8 , wherein grouping the event sequences further comprises:

utilizing a machine learning model to analyze the event-related data for the grouping.

15 . A system for a visual analysis of event sequences, the system comprising:

a memory; and

a processor, coupled to the memory, to perform operations comprising:

receiving, from one or more data sources, event-related data for a set of event sequences;

selecting one or more event grouping criteria from a list of event grouping criteria, wherein the list of event grouping criteria comprises a criterion of grouping by an event location, a criterion of grouping by an event entity, and a criterion of grouping by an event time;

grouping, into one or more groups, event sequences within the set of event sequences based on the one or more event grouping criteria;

calculating sequence metrics for each group of the one or more groups of the event sequences within the set of event sequences; and

displaying, on a user interface, the sequence metrics for the set of event sequences.

16 . The system of claim 15 , the operations further comprising:

receiving, via the user interface, a filter selection for the set of event sequences;

generating a new representative sample subset of the set of event sequences based on the filter selection;

calculating new sequence metrics for the new representative sample subset of the set of event sequences; and

displaying, on the user interface, the new sequence metrics for the set of event sequences.

17 . The system of claim 16 , the operations further comprising:

generating a visual representation of each group of the one or more groups of the event sequences within the set of event sequences;

displaying, on the user interface, the visual representation of each group of event sequences within the set of event sequences;

generating a new visual representation of the new representative sample subset of the set of event sequences; and

displaying, on the user interface, the new visual representation of the new representative sample subset of the set of event sequences.

18 . The system of claim 15 , the operations further comprising:

receiving, via the user interface, a selection to modify a visual representation of the set of event sequences;

generating a new visual representation of the set of event sequences based on the selection; and

displaying, on the user interface, the new visual representation of the set of event sequences.

19 . The system of claim 15 , wherein grouping the event sequences further comprises:

determining a probability of events occurring in a same event sequence; and

responsive to determining that the probability exceeds a threshold value, grouping the events in the same event sequence.

20 . The system of claim 15 , wherein the sequence metrics comprise a duration of each event sequence, a number of events in each event sequence, and a time gap between consecutive event sequences.