Avoiding Over-Fitting in ILP-Based Process Discovery
| Autor: | Zelst, van, S.J., Dongen, van, B.F., van der Aalst, W.M.P., Motahari-Nezhad, H.R., Recker, J., Weidlich, M. |
|---|---|
| Přispěvatelé: | Process Science |
| Rok vydání: | 2015 |
| Předmět: | |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319230627 BPM Business Process Management: 13th International Conference, BPM 2015, Innsbruck, Austria, August 31-September 3, 2015, Proceedings, 163-171 STARTPAGE=163;ENDPAGE=171;TITLE=Business Process Management |
| DOI: | 10.1007/978-3-319-23063-4_10 |
| Popis: | The aim of process discovery is to discover a process model based on business process execution data, recorded in an event log. One of several existing process discovery techniques is the ILP-based process discovery algorithm. The algorithm is able to unravel complex process structures and provides formal guarantees w.r.t. the model discovered, e.g., the algorithm guarantees that a discovered model describes all behavior present in the event log. Unfortunately the algorithm is unable to cope with exceptional behavior present in event logs. As a result, the application of ILP-based process discovery techniques in everyday process discovery practice is limited. This paper addresses this problem by proposing a filtering technique tailored towards ILP-based process discovery. The technique helps to produce process models that are less over-fitting w.r.t. the event log, more understandable, and more adequate in capturing the dominant behavior present in the event log. The technique is implemented in the ProM framework. Keywords: Process mining Process discovery Integer linear programming Filtering |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|