Event abstraction for process mining using supervised learning techniques
| Autor: | Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.M.P., Bi, Y., Kapoor, S., Bhatia, R. |
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| Přispěvatelé: | Process Science |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Process modeling
business.industry Event (computing) Computer science Feature vector Supervised learning Process mining 02 engineering and technology Machine learning computer.software_genre Conformance checking Business process discovery Event abstraction 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Abstraction (linguistics) Probabilistic graphical models |
| Zdroj: | Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 ISBN: 9783319569932 Proceedings of the SAI Intelligent Systems Conference (IntelliSys 2016), 21-22 September 2016, London, United Kingdom, 251-269 STARTPAGE=251;ENDPAGE=269;TITLE=Proceedings of the SAI Intelligent Systems Conference (IntelliSys 2016), 21-22 September 2016, London, United Kingdom |
| Popis: | Process mining techniques focus on extracting insight in processes from event logs. In many cases, events recorded in the event log are too fine-grained, causing process discovery algorithms to discover incomprehensible process models or process models that are not representative of the event log. We show that when process discovery algorithms are only able to discover an unrepresentative process model from a low-level event log, structure in the process can in some cases still be discovered by first abstracting the event log to a higher level of granularity. This gives rise to the challenge to bridge the gap between an original low-level event log and a desired high-level perspective on this log, such that a more structured or more comprehensible process model can be discovered. We show that supervised learning can be leveraged for the event abstraction task when annotations with high-level interpretations of the low-level events are available for a subset of the sequences (i.e., traces). We present a method to generate feature vector representations of events based on XES extensions, and describe an approach to abstract events in an event log with Condition Random Fields using these event features. Furthermore, we propose a sequence-focused metric to evaluate supervised event abstraction results that fits closely to the tasks of process discovery and conformance checking. We conclude this paper by demonstrating the usefulness of supervised event abstraction for obtaining more structured and/or more comprehensible process models using both real life event data and synthetic event data. |
| Databáze: | OpenAIRE |
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