Local Concurrency Detection in Business Process Event Logs
| Autor: | Abderrahmane Maaradji, Marlon Dumas, Marcello La Rosa, Abel Armas-Cervantes |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Process modeling
Relation (database) Computer Networks and Communications Computer science Event (computing) Business process Concurrency Process (computing) Process mining 02 engineering and technology computer.software_genre Event structure 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining computer |
| Zdroj: | ACM Transactions on Internet Technology. 19:1-23 |
| ISSN: | 1557-6051 1533-5399 |
| DOI: | 10.1145/3289181 |
| Popis: | Process mining techniques aim at analyzing records generated during the execution of a business process in order to provide insights on the actual performance of the process. Detecting concurrency relations between events is a fundamental primitive underpinning a range of process mining techniques. Existing approaches to this problem identify concurrency relations at the level of event types under a global interpretation. If two event types are declared to be concurrent, every occurrence of one event type is deemed to be concurrent to one occurrence of the other. In practice, this interpretation is too coarse-grained and leads to over-generalization. This article proposes a finer-grained approach, whereby two event types may be deemed to be in a concurrency relation relative to one state of the process, but not relative to other states. In other words, the detected concurrency relation holds locally, relative to a set of states. Experimental results both with artificial and real-life logs show that the proposed local concurrency detection approach improves the accuracy of existing concurrency detection techniques. |
| Databáze: | OpenAIRE |
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