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A Framework for Human-Centered Exploration of Complex Event Log Graphs

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Discovery Science (DS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11828))

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Abstract

Graphs can conveniently model complex multi-relational characteristics. For making sense of such data, effective interpretable methods for their exploration are crucial, in order to provide insights that cover the relevant analytical questions and are understandable to humans. This paper presents a framework for human-centered exploration of attributed graphs on complex, i.e., large and heterogeneous event logs. The proposed approach is based on specific graph modeling, graph summarization and local pattern mining methods. We demonstrate promising results in the context of a real-world industrial dataset.

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Acknowledgements

This work has been partially supported by Interreg NWE, project Di-Plast - Digital Circular Economy for the Plastics Industry.

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Authors and Affiliations

  1. Department of Cognitive Science and Artificial Intelligence, Tilburg University, Warandelaan 2, 5037 AB, Tilburg, The Netherlands

    Martin Atzmueller & Stefan Bloemheuvel

  2. Jheronimus Academy of Data Science (JADS), Sint Janssingel 92, 5211 DA, ’s-Hertogenbosch, The Netherlands

    Martin Atzmueller & Stefan Bloemheuvel

  3. Corporate Research Center, ABB AG, Wallstadter Str. 59, 68526, Ladenburg, Germany

    Benjamin Kloepper

Authors
  1. Martin Atzmueller
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  2. Stefan Bloemheuvel
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  3. Benjamin Kloepper
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Correspondence to Martin Atzmueller .

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Editors and Affiliations

  1. Jožef Stefan Institute, Ljubljana, Slovenia

    Petra Kralj Novak

  2. Rudjer Bošković Institute, Zagreb, Croatia

    Tomislav Šmuc

  3. Jožef Stefan Institute, Ljubljana, Slovenia

    Sašo Džeroski

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Atzmueller, M., Bloemheuvel, S., Kloepper, B. (2019). A Framework for Human-Centered Exploration of Complex Event Log Graphs. In: Kralj Novak, P., Šmuc, T., Džeroski, S. (eds) Discovery Science. DS 2019. Lecture Notes in Computer Science(), vol 11828. Springer, Cham. https://doi.org/10.1007/978-3-030-33778-0_26

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  • DOI: https://doi.org/10.1007/978-3-030-33778-0_26

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  • Online ISBN: 978-3-030-33778-0

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