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Incremental Process Discovery using Petri Net Synthesis

Badouel E., Schlachter U.

Fundamenta Informaticae

2017

Vol. 154, nr 1/4

1--13

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.

Synthesis of Live and Bounded Persistent Systems

Best E., Devillers R.

Fundamenta Informaticae

2015

Vol. 140, nr 1

39--59

This paper presents a dedicated Petri net synthesis algorithm for the case that a transition system is finite, live, and persistent. In particular, the paper delineates exactly when and how a structurally persistent net may be constructed, by crystallising, out of a general region-theoretic approach, a minimised set of simplified systems of linear inequalities. This extends previous results where reversibility, instead of liveness, played an important role.

Light Region-based Techniques for Process Discovery

Solé M., Carmona J.

Fundamenta Informaticae

2011

Vol. 113, nr 3/4

343-376

A central problem in the area of Process Mining is to obtain a formal model that represents selected behavior of a system. The theory of regions has been applied to address this problem, enabling the derivation of a Petri net whose language includes a set of traces. However, when dealing with real-life systems, the available tool support for performing such a task is unsatisfactory, due to the complex algorithms that are required. In this paper, the theory of regions is revisited to devise a novel technique that explores the space of regions by combining the elements of a region basis. Due to its light space requirements, the approach can represent an important step for bridging the gap between the theory of regions and its industrial application. Experimental results show that there is improvement in orders of magnitude in comparison with state-of-the-art tools for the same task.