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Data-Driven Process Performance Measurement and Prediction: A Process-Tree-Based Approach

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Intelligent Information Systems (CAiSE 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 424))

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Abstract

To achieve operational excellence, a clear understanding of the core processes of a company is vital. Process mining enables companies to achieve this by distilling historical process knowledge based on recorded historical event data. Few techniques focus on the prediction of process performance after process redesign. This paper proposes a foundational framework for a data-driven business process redesign approach, allowing the user to investigate the impact of changes in the process, w.r.t. the overall process performance. The framework supports the prediction of future performance based on anticipated activity-level performance changes and control-flow changes. We have applied our approach to several real event logs, confirming our approach’s applicability.

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Authors and Affiliations

  1. Fraunhofer Institute for Applied Information Technology (FIT), Sankt Augustin, Germany

    Sebastiaan J. van Zelst & Wil M. P. van der Aalst

  2. RWTH Aachen University, Aachen, Germany

    Sebastiaan J. van Zelst, Luis F. R. Santos & Wil M. P. van der Aalst

Authors
  1. Sebastiaan J. van Zelst
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  2. Luis F. R. Santos
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  3. Wil M. P. van der Aalst
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Corresponding author

Correspondence to Sebastiaan J. van Zelst .

Editor information

Editors and Affiliations

  1. University of Paris 1 Panthéon-Sorbonne, Paris, France

    Selmin Nurcan

  2. Swinburne University of Technology, Melbourne, VIC, Australia

    Axel Korthaus

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van Zelst, S.J., Santos, L.F.R., van der Aalst, W.M.P. (2021). Data-Driven Process Performance Measurement and Prediction: A Process-Tree-Based Approach. In: Nurcan, S., Korthaus, A. (eds) Intelligent Information Systems. CAiSE 2021. Lecture Notes in Business Information Processing, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-030-79108-7_9

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  • DOI: https://doi.org/10.1007/978-3-030-79108-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-79107-0

  • Online ISBN: 978-3-030-79108-7

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