Method and a system for process discovery
First Claim
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1. A computer implemented method comprising:
- extracting process data from a data source;
creating a trace using the extracted process data, wherein the trace comprises a plurality of events;
reorganizing the plurality of events within the trace to create a reorganized plurality of events;
deriving a set comprising events that directly follow one another for a task X and a task Y from the reorganized plurality of events;
deriving a set comprising events that indirectly follow one another for the task X and the task Y from the reorganized plurality of events; and
detecting a process model using the set of events that directly follow one another and the set of events that indirectly follow one another;
wherein detecting the process model comprises detecting a process type based on a probabilistic relationship between the task X and the task Y, the probabilistic relationship determined through statistical analysis of the set of events that directly follow one another and the set of events that indirectly follow one another.
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Abstract
The disclosed embodiments relate to a system and a method for process discovery. Embodiments of the present invention comprise extracting process data from a data source, creating a trace using the extracted process data, wherein the trace comprises a plurality of events, and detecting a process model using the plurality of events.
50 Citations
20 Claims
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1. A computer implemented method comprising:
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extracting process data from a data source; creating a trace using the extracted process data, wherein the trace comprises a plurality of events; reorganizing the plurality of events within the trace to create a reorganized plurality of events; deriving a set comprising events that directly follow one another for a task X and a task Y from the reorganized plurality of events; deriving a set comprising events that indirectly follow one another for the task X and the task Y from the reorganized plurality of events; and detecting a process model using the set of events that directly follow one another and the set of events that indirectly follow one another; wherein detecting the process model comprises detecting a process type based on a probabilistic relationship between the task X and the task Y, the probabilistic relationship determined through statistical analysis of the set of events that directly follow one another and the set of events that indirectly follow one another. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A computer implemented method comprising:
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receiving a transmission comprising process data; creating a trace that comprises a plurality of events using the process data; reorganizing the plurality of events within the trace to create a reorganized plurality of events; deriving a set comprising events that directly follow one another for two tasks X and Y from the reorganized plurality of events; deriving a set comprising events that indirectly follow one another for the two tasks X and Y from the reorganized plurality of events; and detecting a process structure using the set comprising events that directly follow one another and the set comprising events that indirectly follow one another; wherein detecting the process structure comprises detecting a process type based on a probabilistic relationship between the task X and the task Y, the probabilistic relationship determined through statistical analysis of the set of events that directly follow one another and the set of events that indirectly follow one another. - View Dependent Claims (13, 14, 15)
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16. A computer system for discovering process types, the computer system comprising a processor to execute instruction modules, the instruction modules comprising:
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a process data extraction module that extracts data from a log file or database stored in a storage medium; a trace creation module that creates a trace based on the extracted data; a trace reorganization module that reorganizes the trace to produce reorganized trace data; and a model detection module to; derive a set comprising events that directly follow one another for two tasks X and Y; derive a set comprising events that indirectly follow one another for the two tasks X and Y; and detect a process model using the set of events that directly follow one another and the set of events that indirectly follow one another; wherein detecting the process model comprises detecting a process type based on a probabilistic relationship between the task X and the task Y, the probabilistic relationship determined through statistical analysis of the set of events that directly follow one another and the set of events that indirectly follow one another. - View Dependent Claims (17, 18, 19)
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20. A tangible, non-transitory, computer-readable medium that stores instructions that, when executed, effect process discovery, comprising:
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instructions adapted to extract data from a log file or database; instructions adapted to create a trace based on the extracted data; instructions adapted to reorganize the trace to produce reorganized trace data; and instructions adapted to derive a set comprising events that directly follow one another; instructions adapted to derive a set comprising events that indirectly follow one another; instructions adapted to detect a process model using the set of events that directly follow one another and the set of events that indirectly follow one another instructions adapted to detect a process model using the set of events that directly follow one another and the set of events that indirectly follow one another; wherein detecting the process model comprises detecting a process type based on a probabilistic relationship between the task X and the task Y, the probabilistic relationship determined through statistical analysis of the set of events that directly follow one another and the set of events that indirectly follow one another.
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Specification