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Unsupervised Event Abstraction in a Process Mining Context: A Benchmark Study

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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

Due to the rise of IoT, event data becomes increasingly fine-grained. Faced with such data, process discovery often produces incomprehensible spaghetti-models expressed at a granularity level that doesn’t match the mental model of a business user. One approach is to use event abstraction patterns to transform the event log towards a more coarse-grained level and to discover process models from this transformed log. Recent literature has produced various (partial) implementations of this approach, but insights how these techniques compare against each other is still limited.

This paper focuses on the use of Local Process Models and Combination based Behavioural Pattern Mining to discover event abstraction patterns in combination with the approach of Mannhardt et al. [15] to transform the event log. Experiments are conducted to gain insights into the performance of these techniques. Results are very limited with a general decrease in fitness and precision and only a minimal improvement of complexity. Results also show that the combination of the process discovery algorithm and the event abstraction pattern miner matters. In particular, the combination of Local Process Models with Split Miner seems to improve precision.

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Notes

  1. 1.

    This is done via the R package understandBPMN [13].

  2. 2.

    The event logs were extracted from the 4TU Centre for Research Data in May 2020.

  3. 3.

    Done via the convert BPMN diagram to Petri Net (Control Flow) plug-in in ProM.

  4. 4.

    https://github.com/gregvanhoudt/UnsupervisedEventAbstraction.

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Authors and Affiliations

  1. Research Group Business Informatics, UHasselt - Hasselt University, Hasselt, Belgium

    Greg Van Houdt, Benoît Depaire & Niels Martin

  2. Research Foundation Flanders (FWO), Brussels, Belgium

    Niels Martin

  3. Data Analytics Laboratory, Vrije Universiteit Brussel, Brussels, Belgium

    Niels Martin

Authors
  1. Greg Van Houdt
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  2. Benoît Depaire
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  3. Niels Martin
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Corresponding author

Correspondence to Greg Van Houdt .

Editor information

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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Van Houdt, G., Depaire, B., Martin, N. (2021). Unsupervised Event Abstraction in a Process Mining Context: A Benchmark Study. 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_7

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  • DOI: https://doi.org/10.1007/978-3-030-72693-5_7

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