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ProcessExplorer: Intelligent Process Mining Guidance

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Business Process Management (BPM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11675))

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Abstract

Large amount of data is collected in event logs from information systems, reflecting the actual execution of business processes. Due to the highly competitive pressure in the market, organizations are particularly interested in optimizing their processes. Process mining enables the extraction of valuable knowledge from event logs, such as deviations, bottlenecks, and anomalies. Due to the increase of process complexity in flexible environments, visual exploration is increasingly becoming more challenging. In this paper, we propose ProcessExplorer, an interactive process mining approach to enable fast data analysis and exploration. ProcessExplorer takes an event log as input to automatically suggest subsets of similar process behavior, evaluate each subset, generate interesting insights, and suggest the subsets with the most interesting characteristics. We implemented our approach into an interactive visual exploration system, which we use as part of a user study conducted to evaluate our approach. Our results show that ProcessExplorer can be successfully applied to analyze and explore real-life data sets efficiently.

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Notes

  1. 1.

    van Dongen, B.F., Dataset BPI Challenge 2019. 4TU.Centre for Research Data.

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Acknowledgements

This work is funded by the German Federal Ministry of Education and Research (BMBF) Software Campus project “AI-PM” [01IS17050] and the research project “KI.RPA” [01IS18022D].

Author information

Authors and Affiliations

  1. Telecooperation Lab, Technische Universität Darmstadt, Darmstadt, Germany

    Alexander Seeliger, Alejandro Sánchez Guinea, Timo Nolle & Max Mühlhäuser

Authors
  1. Alexander Seeliger
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  2. Alejandro Sánchez Guinea
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  3. Timo Nolle
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  4. Max Mühlhäuser
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Corresponding author

Correspondence to Alexander Seeliger .

Editor information

Editors and Affiliations

  1. University of Copenhagen, Copenhagen, Denmark

    Thomas Hildebrandt

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Boudewijn F. van Dongen

  3. University of Bayreuth, Bayreuth, Germany

    Maximilian Röglinger

  4. Vienna University of Economics and Business, Vienna, Austria

    Jan Mendling

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Seeliger, A., Sánchez Guinea, A., Nolle, T., Mühlhäuser, M. (2019). ProcessExplorer: Intelligent Process Mining Guidance. In: Hildebrandt, T., van Dongen, B., Röglinger, M., Mendling, J. (eds) Business Process Management. BPM 2019. Lecture Notes in Computer Science(), vol 11675. Springer, Cham. https://doi.org/10.1007/978-3-030-26619-6_15

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  • DOI: https://doi.org/10.1007/978-3-030-26619-6_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26618-9

  • Online ISBN: 978-3-030-26619-6

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