Discovering Process Models With Long-Term Dependencies While Providing Guarantees and Filtering Infrequent Behavior Patterns

In process discovery, the goal is to find, for a given event log, the modeldescribing the underlying process. While process models can be represented in avariety of ways, Petri nets form a theoretically well-explored descriptionlanguage and are therefore often used. In this paper, we extend the eST-Minerprocess discovery algorithm. The eST-Miner computes a set of Petri net placeswhich are considered to be fitting with respect to a certain fraction of thebehavior described by the given event log as indicated by a given noisethreshold. It evaluates all possible candidate places using token-based replay.The set of replayable traces is determined for each place in isolation, i.e.,these sets do not need to be consistent. This allows the algorithm to abstractfrom infrequent behavioral patterns occurring only in some traces. However,when combining places into a Petri net by connecting them to the correspondinguniquely labeled transitions, the resulting net can replay exactly those tracesfrom the event log that are allowed by the combination of all inserted places.Thus, inserting places one-by-one without considering their combined effect mayresult in deadlocks and low fitness of the Petri net. In this paper, we exploreadaptions of the eST-Miner, that aim to select a subset of places such that theresulting Petri net guarantees a definable minimal fitness while maintaininghigh precision with respect to the input event log. Furthermore, current placeevaluation techniques tend to block the execution of infrequent activitylabels. Thus, a refined place fitness metric is introduced and thoroughlyinvestigated. In our experiments we use real and artificial event logs toevaluate and compare the impact of the various place selection strategies andplace fitness evaluation metrics on the returned Petri net.Comment: Fundamenta Informaticae, Petri Nets Special Issue 202