Abstract
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.
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van Zelst, S.J., van Dongen, B.F., van der Aalst, W.M.P. (2015). Avoiding Over-Fitting in ILP-Based Process Discovery. In: Motahari-Nezhad, H., Recker, J., Weidlich, M. (eds) Business Process Management. BPM 2016. Lecture Notes in Computer Science(), vol 9253. Springer, Cham. https://doi.org/10.1007/978-3-319-23063-4_10
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DOI: https://doi.org/10.1007/978-3-319-23063-4_10
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