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Text-Aware Predictive Process Monitoring of Knowledge-Intensive Processes: Does Control Flow Matter?

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Business Process Management Workshops (BPM 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 492))

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Abstract

Predictive process monitoring (PPM) enables organizations to predict the behavior of ongoing processes, e.g., the lead time. This is of great interest for knowledge-intensive processes (KIPs), which often cover long time spans. With such insights, resource allocation or customer relationship management could be improved. While already many PPM methods exist, they have not yet been applied to KIPs. Thus, we extend PPM research by using machine learning and natural language processing (NLP) to develop and evaluate a novel text-aware PPM approach tailored towards monitoring KIPs. By developing suitable features and considering various time intervals, our approach encodes and aggregates the event log. Using two real-world event logs, we assess our methodology. We demonstrate that the MAE improves as compared to state-of-the-art PPM methods. It shows that the control flow perspective of KIPs should primarily be neglected, while considering more structured features and unstructured textual information is essential.

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Notes

  1. 1.

    https://github.com/olivermueller/bpmi40_ppm.

  2. 2.

    https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html.

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Acknowledgements

This research and development project is funded by the Ministry of Economic Affairs, Innovation, Digitalization, and Energy of the State of North Rhine-Westphalia (MWIDE) as part of the Leading-Edge Cluster, Intelligente Technische Systeme OstWestfalenLippe (it’s OWL) and supervised by the project administration in Jülich (PtJ). The responsibility for the content of this publication lies with the authors.

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Authors and Affiliations

  1. Faculty of Business Administration and Economics, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany

    Katharina Brennig, Kay Benkert, Bernd Löhr & Oliver Müller

Authors
  1. Katharina Brennig
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  2. Kay Benkert
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  3. Bernd Löhr
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  4. Oliver Müller
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Correspondence to Katharina Brennig .

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Editors and Affiliations

  1. KU Leuven, Leuven, Belgium

    Jochen De Weerdt

  2. TUM School of Computation, Information and Technology, Heilbronn, Germany

    Luise Pufahl

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Brennig, K., Benkert, K., Löhr, B., Müller, O. (2024). Text-Aware Predictive Process Monitoring of Knowledge-Intensive Processes: Does Control Flow Matter?. In: De Weerdt, J., Pufahl, L. (eds) Business Process Management Workshops. BPM 2023. Lecture Notes in Business Information Processing, vol 492. Springer, Cham. https://doi.org/10.1007/978-3-031-50974-2_33

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  • DOI: https://doi.org/10.1007/978-3-031-50974-2_33

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