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PEM4PPM: A Cognitive Perspective on the Process of Process Mining

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Business Process Management (BPM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14159))

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Abstract

During the last decades, process mining (PM) has matured and rapidly increased in its adoption. Making sense of data is a main part of the work of PM analysts, which involves cognitive processes. Recent work has leveraged behavioral data to explain these processes. Still, the process of process mining (PPM) is yet to be well understood and a theoretical foundation for explaining how these processes unfold is missing. This paper attempts to fill this gap by understanding how PPM data can be analyzed in a theory-guided manner and what insights can be gained from this analysis. To investigate these aspects, we analyzed verbal data and interaction traces obtained from analysis sessions with 29 participants performing a PM task. The analysis was based on the Predictive Processing (PP) theory and the derived Prediction Error Minimization (PEM) process, anchored in cognitive science. The results include (1) a theoretical adaptation of the PEM theory to the PPM context, (2) four strategies utilized by PM analysts, identified, and validated based on the adapted theory, and (3) an understanding of the differences in performance between analysts using different strategies and independence of the expertise level and the strategy choice.

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Notes

  1. 1.

    Zoom https://zoom.us/

  2. 2.

    The full study design, including participants’ recruitment procedure, was approved by the Institutional Ethics Committee (Approval no. 238/21).

  3. 3.

    Fluxicon Disco https://fluxicon.com/disco/

  4. 4.

    Vocalmatic https://vocalmatic.com/

  5. 5.

    MAXQDA https://www.maxqda.com/

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Acknowledgment

Acknowledgment. This work was partially funded by the Israel Science Foundation under grant agreement 2005/21 and the Swiss National Science Foundation (SNSF) under Grant No.: 200021 197032.

Author information

Authors and Affiliations

  1. University of Haifa, City Campus, Hanamal St. 65, Haifa, Israel

    Elizaveta Sorokina, Pnina Soffer & Irit Hadar

  2. Technion - Israel Institute of Technology, Technion City, 3200003, Haifa, Israel

    Uri Leron

  3. Institute of Computer Science, University of St, Gallen, Switzerland

    Francesca Zerbato & Barbara Weber

Authors
  1. Elizaveta Sorokina
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  2. Pnina Soffer
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  3. Irit Hadar
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  4. Uri Leron
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  5. Francesca Zerbato
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  6. Barbara Weber
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Corresponding author

Correspondence to Elizaveta Sorokina .

Editor information

Editors and Affiliations

  1. University of Trento, Trento, Italy

    Chiara Di Francescomarino

  2. Technical University of Denmark, Kgs. Lyngby, Denmark

    Andrea Burattin

  3. TU Dortmund University, Dortmund, Germany

    Christian Janiesch

  4. The University of Queensland, Brisbane, QLD, Australia

    Shazia Sadiq

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Sorokina, E., Soffer, P., Hadar, I., Leron, U., Zerbato, F., Weber, B. (2023). PEM4PPM: A Cognitive Perspective on the Process of Process Mining. In: Di Francescomarino, C., Burattin, A., Janiesch, C., Sadiq, S. (eds) Business Process Management. BPM 2023. Lecture Notes in Computer Science, vol 14159. Springer, Cham. https://doi.org/10.1007/978-3-031-41620-0_27

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  • DOI: https://doi.org/10.1007/978-3-031-41620-0_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41619-4

  • Online ISBN: 978-3-031-41620-0

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