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From Text to Performance Measurement: Automatically Computing Process Performance Using Textual Descriptions and Event Logs

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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

Process performance measurement assesses how well a process is running, covering various dimensions such as time, cost, and quality. This task involves the definition of measurable Process Performance Indicators (PPIs), which in many cases are calculated based on data recorded in an event log. An inhibitor of effective performance analysis is that establishing PPI definitions measurable from event logs is highly complex, because it requires process analytical expertise, as well as in-depth knowledge about the structure and contents of the available event data. Given that managers typically do not have such knowledge, this means that those stakeholders that are generally most interested in measuring process performance cannot do so in a convenient manner. Recognizing this, we bridge this gap by proposing an approach for the measurement of process performance based on textual descriptions and event logs, which combines state-of-the-art natural language processing techniques with matching strategies that are tailored to the task at hand. Evaluation experiments using textual descriptions provided by both industry and academic users demonstrate the accuracy of our approach.

This work has been partially supported by projects PID2021-126227NB-C21/ AEI/10.13039/501100011033/ FEDER, UE; TED2021-131023B-C22/ AEI/10.13039/501100011033/ Unión Europea NextGenerationEU/PRTR, and US-1381595 (Junta de Andalucía/FEDER, UE).

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Notes

  1. 1.

    \((>, 0)\) is used to count the cases for which this activity happens at least once.

  2. 2.

    Tag O indicates that a chunk does not belong to any entity from the tag set.

  3. 3.

    https://rodrigopivi.github.io/Chatito/.

  4. 4.

    We use the en_core_web_lg model provided by spacy (https://spacy.io/).

  5. 5.

    We again use the Spacy library for this.

  6. 6.

    More information, our prototype, and links to the materials can be found at https://github.com/isa-group/ppinat.

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Acknowledgments

We thank Maria Isabel Ramos and Javier Vilariño for their support in the implementation.

Author information

Authors and Affiliations

  1. SCORE Lab, I3US, Universidad de Sevilla, Seville, Spain

    Manuel Resinas & Adela del-Río-Ortega

  2. University of Mannheim, Mannheim, Germany

    Han van der Aa

Authors
  1. Manuel Resinas
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  2. Adela del-Río-Ortega
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  3. Han van der Aa
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Corresponding author

Correspondence to Manuel Resinas .

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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Resinas, M., del-Río-Ortega, A., van der Aa, H. (2023). From Text to Performance Measurement: Automatically Computing Process Performance Using Textual Descriptions and Event Logs. 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_16

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  • DOI: https://doi.org/10.1007/978-3-031-41620-0_16

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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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