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International Conference on Process Mining

ICPM 2020: Process Mining Workshops pp 69–81Cite as

Process Model Discovery from Sensor Event Data

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 406))

Abstract

Virtually all techniques, developed in the area of process mining, assume the input event data to be discrete, and, at a relatively high level (i.e., close to the business-level). However, in many cases, the event data generated during the execution of a process is at a much lower level of abstraction, e.g., sensor data. Hence, in this paper, we present a novel technique that allows us to translate sensor data into higher-level, discrete event data, thus enabling existing process mining techniques to work on data tracked at a sensory level. Our technique discretises the observed sensor data into activities by applying unsupervised learning in the form of clustering. Furthermore, we refine the observed sequences by deducing imperative sub-models for the observed discretised data, i.e., allowing us to identify concurrency and interleaving within the data. We evaluated the approach by comparing the obtained model quality for several clustering techniques on a publicly available data-set in a smart home scenario. Our results show that applying our framework combined with a clustering technique yields results on data that otherwise would not be suitable for process discovery.

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Notes

  1. 1.

    https://github.com/d-o-m-i-n-i-k/Process-Model-Discovery-public.

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Author information

Authors and Affiliations

  1. Group Process Analytics, Kiel University, Kiel, Germany

    Dominik Janssen & Agnes Koschmider

  2. Department of Technology Management, SINTEF Digital, Oslo, Norway

    Felix Mannhardt

  3. Department of Computer Science, NTNU, Trondheim, Norway

    Felix Mannhardt

  4. Fraunhofer Institute for Applied Information Technology, Fraunhofer Gesellschaft, Sankt Augustin, Germany

    Sebastiaan J. van Zelst

  5. Chair of Process and Data Science, RWTH Aachen University, Aachen, Germany

    Sebastiaan J. van Zelst

Authors
  1. Dominik Janssen
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  2. Felix Mannhardt
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  3. Agnes Koschmider
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  4. Sebastiaan J. van Zelst
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Corresponding author

Correspondence to Dominik Janssen .

Editor information

Editors and Affiliations

  1. Queensland University of Technology, Brisbane, QLD, Australia

    Sander Leemans

  2. Kühne Logistics University, Hamburg, Germany

    Henrik Leopold

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Janssen, D., Mannhardt, F., Koschmider, A., van Zelst, S.J. (2021). Process Model Discovery from Sensor Event Data. In: Leemans, S., Leopold, H. (eds) Process Mining Workshops. ICPM 2020. Lecture Notes in Business Information Processing, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-72693-5_6

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  • DOI: https://doi.org/10.1007/978-3-030-72693-5_6

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