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Discovering Process-Based Drivers for Case-Level Outcome Explanation

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Process Mining Workshops (ICPM 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 503))

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Abstract

Process mining has shown great impact in improving business Key Performance Indicators (KPIs), which are typically measured as aggregations over case-level outcomes. A commonly encountered key question in achieving such impact is understanding the underlying reasons for why a certain outcome appears in some cases (e.g., why certain cases take long to finish). We use the term drivers to refer to explanations for case-level outcomes. We hypothesize that how process is run, in other words, process traces, directly influences case-level outcomes, and hence KPIs. In this paper, we propose a new method to automatically and efficiently discover process-based drivers that are effective, significant and interpretable. We formally define the problem of driver discovery as a constrained optimization problem. Given that the problem is NP-hard, we develop efficient greedy algorithms to solve the problem. We evaluate our method on real-world datasets to demonstrate the effectiveness and efficiency of our approach.

H. Zhang—Equal contribution, work done in Celonis.

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Authors and Affiliations

  1. Georgia Tech, Atlanta, USA

    Peng Li & Hantian Zhang

  2. Celonis, New York, USA

    Xu Chu, Alexander Seeliger & Cong Yu

Authors
  1. Peng Li
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  2. Hantian Zhang
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  3. Xu Chu
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  4. Alexander Seeliger
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  5. Cong Yu
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Corresponding author

Correspondence to Xu Chu .

Editor information

Editors and Affiliations

  1. KU Leuven, Leuven, Belgium

    Johannes De Smedt

  2. University of Haifa, Haifa, Israel

    Pnina Soffer

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Li, P., Zhang, H., Chu, X., Seeliger, A., Yu, C. (2024). Discovering Process-Based Drivers for Case-Level Outcome Explanation. In: De Smedt, J., Soffer, P. (eds) Process Mining Workshops. ICPM 2023. Lecture Notes in Business Information Processing, vol 503. Springer, Cham. https://doi.org/10.1007/978-3-031-56107-8_13

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  • DOI: https://doi.org/10.1007/978-3-031-56107-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56106-1

  • Online ISBN: 978-3-031-56107-8

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