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

  • Martin AtzmuellerEmail author
  • Stefan Bloemheuvel
  • Benjamin Kloepper
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11828)

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.

Notes

Acknowledgements

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

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Martin Atzmueller
    • 1
    • 2
    Email author
  • Stefan Bloemheuvel
    • 1
    • 2
  • Benjamin Kloepper
    • 3
  1. 1.Department of Cognitive Science and Artificial IntelligenceTilburg UniversityTilburgThe Netherlands
  2. 2.Jheronimus Academy of Data Science (JADS)’s-HertogenboschThe Netherlands
  3. 3.Corporate Research CenterABB AGLadenburgGermany

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