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
|Title of host publication||Business Process Management |
|Subtitle of host publication||13th International Conference, BPM 2015, Innsbruck, Austria, August 31 - September 3, 2015, Proceedings|
|Editors||H.R. Motahari-Nezhad, J. Recker, M. Weidlich|
|Place of Publication||Dordrecht|
|Publication status||Published - 2015|
|Name||Lecture Notes in Computer Science|