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International Conference on Enterprise Design, Operations, and Computing

EDOC 2022: Enterprise Design, Operations, and Computing pp 128–144Cite as

Enhanced Transformation of BPMN Models with Cancellation Features

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13585))

Abstract

Canceling ongoing process instances is a natural phenomenon in practice. As such, modeling cancellation behavior is supported in the Business Process Model and Notation (BPMN) via exception events. Event-data-driven analysis techniques using such process models, e.g., conformance checking, require converting the BPMN model into a formal process modeling representation, i.e., Petri nets. However, the existing transformation of BPMN models with exception events renders a classical Petri net, with various additional modeling constructs to mimic the exception behavior. Using such a model in a subsequent analysis renders an infeasible computational complexity. Hence, this paper presents a novel conversion of BPMN models with exception events into reset nets, significantly reducing the number of required invisible transitions in the corresponding transformation. Our results show that the enhanced conversion reduces the computational effort of using the converted models for conformance checking.

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Notes

  1. 1.

    We omit OR gateways, yet, the framework is easily extended with OR-support.

  2. 2.

    \(\mathcal {P}(X)\) denotes the power set of set X.

  3. 3.

    All models (i.e., both designed and obtained by means of transformation), event data generated and computational results, are available via https://drive.google.com/drive/folders/10Q11FfRu_Lf9kA1moR2gikQc9HAwnsNv?usp=sharing. The code used in the experiments is available via https://github.com/require-gio/pm4py-resetnet.

  4. 4.

    As there is no executable implementation available of the Dijkman transformation, we re-implemented the approach.

  5. 5.

    Note that the \(A^*\) variant for reset/inhibitor nets has been implemented in python, extending the pm4py framework [5].

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Authors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Giorgi Lomidze, Daniel Schuster, Chiao-Yun Li & Sebastiaan J. van Zelst

  2. Fraunhofer Institute for Applied Information Technology, Sankt Augustin, Germany

    Daniel Schuster, Chiao-Yun Li & Sebastiaan J. van Zelst

Authors
  1. Giorgi Lomidze
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  2. Daniel Schuster
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  3. Chiao-Yun Li
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  4. Sebastiaan J. van Zelst
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Corresponding author

Correspondence to Sebastiaan J. van Zelst .

Editor information

Editors and Affiliations

  1. Universidade Federal do Espírito Santo, Vitória, Espírito Santo, Brazil

    João Paulo A. Almeida

  2. University of Groningen, Groningen, The Netherlands

    Dimka Karastoyanova

  3. University of Twente, Enschede, The Netherlands

    Giancarlo Guizzardi

  4. Free University of Bozen-Bolzano, Bolzano, Italy

    Marco Montali

  5. Free University of Bozen-Bolzano, Bolzano, Italy

    Fabrizio Maria Maggi

  6. University of Twente, Enschede, The Netherlands

    Claudenir M. Fonseca

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Lomidze, G., Schuster, D., Li, CY., van Zelst, S.J. (2022). Enhanced Transformation of BPMN Models with Cancellation Features. In: Almeida, J.P.A., Karastoyanova, D., Guizzardi, G., Montali, M., Maggi, F.M., Fonseca, C.M. (eds) Enterprise Design, Operations, and Computing. EDOC 2022. Lecture Notes in Computer Science, vol 13585. Springer, Cham. https://doi.org/10.1007/978-3-031-17604-3_8

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  • DOI: https://doi.org/10.1007/978-3-031-17604-3_8

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  • Online ISBN: 978-3-031-17604-3

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