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A Novel Approach to Discover Switch Behaviours in Process Mining

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Process Mining Workshops (ICPM 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 406))

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Abstract

Process mining is a relatively new subject which builds a bridge between process modelling and data mining. An exclusive choice in a process model usually splits the process into different branches. However, in some processes, it is possible to switch from one branch to another. The inductive miner guarantees to return sound process models, but fails to return a precise model when there are switch behaviours between different exclusive choice branches due to the limitation of process trees. In this paper, we present a novel extension to the process tree model to support switch behaviours between different branches of the exclusive choice operator and propose a novel extension to the inductive miner to discover sound process models with switch behaviours. The proposed discovery technique utilizes the theory of a previous study to detect possible switch behaviours. We apply both artificial and publicly-available datasets to evaluate our approach. Our results show that our approach can improve the precision of discovered models by 36% while maintaining high fitness values compared to the original inductive miner.

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Authors and Affiliations

  1. School of Computer Science, The University of Sydney, 2006, Sydney, NSW, Australia

    Yang Lu, Qifan Chen & Simon Poon

Authors
  1. Yang Lu
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  2. Qifan Chen
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  3. Simon Poon
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Corresponding author

Correspondence to Yang Lu .

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Editors and Affiliations

  1. Queensland University of Technology, Brisbane, QLD, Australia

    Sander Leemans

  2. Kühne Logistics University, Hamburg, Germany

    Henrik Leopold

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Lu, Y., Chen, Q., Poon, S. (2021). A Novel Approach to Discover Switch Behaviours in Process Mining. In: Leemans, S., Leopold, H. (eds) Process Mining Workshops. ICPM 2020. Lecture Notes in Business Information Processing, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-72693-5_5

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  • DOI: https://doi.org/10.1007/978-3-030-72693-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72692-8

  • Online ISBN: 978-3-030-72693-5

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