. Part of the book series (LNCS, volume 9253) 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.
So when we looked at the alpha algorithm, we tried to discover places to constrain the behavior, to the behavior that we had seen in the log. Topic tb 5 for macroeconomics. When we were using state-based regions, we first created the transition system.
Discover Petri Net With Ilp In Prom For Machine
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.