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Decision Mining with Time Series Data Based on Automatic Feature Generation

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Advanced Information Systems Engineering (CAiSE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13295))

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Abstract

Decision rules play a crucial role in business process execution. Knowing and understanding decision rules is of utmost importance for business process analysis and optimization. So far, decision discovery has been merely based on data elements that are measured at a single point in time. However, as cases from different application areas show, process behavior and process outcomes might be heavily influenced by additional data such as sensor streams, that consist of time series data. This holds also true for decision rules based on time series data such as ‘if temperature \(>25\) for more than 3 times, discard goods’. Hence, this paper analyzes how time series data can be automatically exploited for decision mining, i.e., for discovering decision rules based on time series data. The paper identifies global features as well as patterns and intervals in time series as relevant for decision mining. In addition to global features, the paper proposes two algorithms for discovering interval-based and pattern-based features. The approach is implemented and evaluated based on an artificial data set as well as on a real-world data set from manufacturing. The results are promising: the approach discovers decision rules with time series features with high accuracy and precision.

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Notes

  1. 1.

    Time series are defined as a sequence of time-stamped, or at least ordered, data with real-valued attribute values. Note that in this paper, we do not assume equidistant observation times.

  2. 2.

    Note, that the ‘Measure Temperature’ task is modelled explicitly here for illustration purposes. However, it could also stem from an external source.

  3. 3.

    https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html.

  4. 4.

    https://tsfresh.readthedocs.io/.

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Acknowledgements

This work has been partially supported and funded by the Austrian Research Promotion Agency (FFG) via the Austrian Competence Center for Digital Production (CDP) under the contract number 881843.

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Authors and Affiliations

  1. Research Group Workflow Systems and Technology, Faculty of Computer Science, University of Vienna, Vienna, Austria

    Beate Scheibel

  2. Chair of Information Systems and Business Process Management, Department of Informatics, Technical University of Munich, Munich, Germany

    Stefanie Rinderle-Ma

Authors
  1. Beate Scheibel
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  2. Stefanie Rinderle-Ma
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Correspondence to Beate Scheibel .

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Editors and Affiliations

  1. Department of Service and Information System Engineering (ESSI), Universitat Politècnica de Catalunya, Barcelona, Spain

    Xavier Franch

  2. Ghent University, Gent, Belgium

    Geert Poels

  3. Ghent University, Gent, Belgium

    Frederik Gailly

  4. KU Leuven, Leuven, Belgium

    Monique Snoeck

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Scheibel, B., Rinderle-Ma, S. (2022). Decision Mining with Time Series Data Based on Automatic Feature Generation. In: Franch, X., Poels, G., Gailly, F., Snoeck, M. (eds) Advanced Information Systems Engineering. CAiSE 2022. Lecture Notes in Computer Science, vol 13295. Springer, Cham. https://doi.org/10.1007/978-3-031-07472-1_1

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  • DOI: https://doi.org/10.1007/978-3-031-07472-1_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07471-4

  • Online ISBN: 978-3-031-07472-1

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