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Clustering Raw Sensor Data in Process Logs to Detect Data Streams

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Cooperative Information Systems (CoopIS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14353))

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Abstract

The execution and analysis of processes is strongly influenced by sensor streams, e.g., temperature, that are measured in parallel to the process execution and stored in process event logs. This holds particularly true for application domains such as logistics and manufacturing. However, currently, these sensor streams are collected and stored in an arbitrary and unsystematic way. Hence, this work proposes an approach that prepares sensor streams into individual data streams that can be annotated to process tasks and used for process analysis and prediction.

This work has been partly funded by the Austrian Research Promotion Agency (FFG) via the “Austrian Competence Center for Digital Production” (CDP) under the contract number 881843. This work has been supported by the Pilot Factory Industry 4.0, Seestadtstrasse 27, Vienna, Austria.

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Notes

  1. 1.

    https://gitlab.com/me33551/semi_automatic_context_data_extraction.

  2. 2.

    https://cpee.org/~demo/DaSH/batch14.zip [Online; accessed 15-Jul-2023].

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Authors and Affiliations

  1. Technical University of Munich, TUM School of Computation, Information and Technology, Garching, Germany

    Matthias Ehrendorfer, Juergen Mangler & Stefanie Rinderle-Ma

Authors
  1. Matthias Ehrendorfer
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  2. Juergen Mangler
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  3. Stefanie Rinderle-Ma
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Corresponding author

Correspondence to Matthias Ehrendorfer .

Editor information

Editors and Affiliations

  1. Telecom SudParis, Evry, France

    Mohamed Sellami

  2. Leibniz University Hannover, Hannover, Germany

    Maria-Esther Vidal

  3. Eindhoven University of Technology, Eindhoven, The Netherlands

    Boudewijn van Dongen

  4. Telecom SudParis, Evry, France

    Walid Gaaloul

  5. University of Lorraine, Vandoeuvre-les-Nancy, France

    Hervé Panetto

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Ehrendorfer, M., Mangler, J., Rinderle-Ma, S. (2024). Clustering Raw Sensor Data in Process Logs to Detect Data Streams. In: Sellami, M., Vidal, ME., van Dongen, B., Gaaloul, W., Panetto, H. (eds) Cooperative Information Systems. CoopIS 2023. Lecture Notes in Computer Science, vol 14353. Springer, Cham. https://doi.org/10.1007/978-3-031-46846-9_25

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  • DOI: https://doi.org/10.1007/978-3-031-46846-9_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46845-2

  • Online ISBN: 978-3-031-46846-9

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