Finding Complex Process-Structures by Exploiting the Token-Game

  • Lisa Luise MannelEmail author
  • Wil M. P. van der Aalst
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11522)


In process discovery, the goal is to find, for a given event log, the model describing the underlying process. While process models can be represented in a variety of ways, in this paper we focus on the representation by Petri nets. Using an approach inspired by language-based regions, we start with a Petri net without any places, and then insert the maximal set of places considered fitting with respect to the behavior described by the log. Traversing and evaluating the whole set of all possible places is not feasible since their number is exponential in the number of activities. Therefore, we propose a strategy to drastically prune this search space to a small number of candidates, while still ensuring that all fitting places are found. This allows us to derive complex model structures that other discovery algorithms fail to discover. In contrast to traditional region-based approaches this new technique can handle infrequent behavior and therefore also noisy real-life event data. The drastic decrease of computation time achieved by our pruning strategy, as well as our noise handling capability, is demonstrated and evaluated by performing various experiments.


Process discovery Petri nets Language-based regions 



We thank the Alexander von Humboldt (AvH) Stiftung for supporting our research. We thank Alessandro Berti for his support with implementing the eST-Miner, and Sebastiaan J. van Zelst for reviewing this paper.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Lisa Luise Mannel
    • 1
    Email author
  • Wil M. P. van der Aalst
    • 1
  1. 1.Process and Data Science (PADS)RWTH Aachen UniversityAachenGermany

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