Incremental Process Discovery using Petri Net Synthesis

Eric Badouel 1 Uli Schlachter 2
1 SUMO - SUpervision of large MOdular and distributed systems
Inria Rennes – Bretagne Atlantique , IRISA_D4 - LANGAGE ET GÉNIE LOGICIEL
Abstract : Process discovery aims at constructing a model from a set of observations given by execution traces (a log). Petri nets are a preferred target model in that they produce a compact description of the system by exhibiting its concurrency. This article presents a process discovery algorithm using Petri net synthesis, based on the notion of region introduced by A. Ehrenfeucht and G. Rozenberg and using techniques from linear algebra. The algorithm proceeds in three successive phases which make it possible to find a compromise between the ability to infer behaviours of the system from the set of observations while ensuring a parsimonious model, in terms of fitness, precision and simplicity. All used algorithms are incremental which means that one can modify the produced model when new observations are reported without reconstructing the model from scratch.
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Submitted on : Monday, October 2, 2017 - 2:54:54 PM
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Eric Badouel, Uli Schlachter. Incremental Process Discovery using Petri Net Synthesis. Fundamenta Informaticae, Polskie Towarzystwo Matematyczne, 2017, 154 (1-4), pp.1-13. ⟨10.3233/FI-2017-1548⟩. ⟨hal-01599760⟩

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