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Mining Attributed Interaction Networks on Industrial Event Logs

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Intelligent Data Engineering and Automated Learning – IDEAL 2018 (IDEAL 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11315))

Abstract

In future Industry 4.0 manufacturing systems reconfigurability and flexible material flows are key mechanisms. However, such dynamics require advanced methods for the reconstruction, interpretation and understanding of the general material flows and structure of the production system. This paper proposes a network-based computational sensemaking approach on attributed network structures modeling the interactions in the event log. We apply descriptive community mining methods for detecting patterns on the structure of the production system. The proposed approach is evaluated using two real-world datasets.

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Acknowledgements

This work has been partially funded by the German Research Foundation (DFG) project “MODUS” (under grant AT 88/4-1) and by the EU ECSEL project Productive 4.0.

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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

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

    Benjamin Kloepper

Authors
  1. Martin Atzmueller
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  2. Benjamin Kloepper
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Corresponding author

Correspondence to Martin Atzmueller .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Rm 209, Building B, Autonomous University of Madrid, Madrid, Spain

    David Camacho

  3. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

  4. University of Seville, Seville, Spain

    Antonio J. Tallón-Ballesteros

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Atzmueller, M., Kloepper, B. (2018). Mining Attributed Interaction Networks on Industrial Event Logs. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11315. Springer, Cham. https://doi.org/10.1007/978-3-030-03496-2_11

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  • DOI: https://doi.org/10.1007/978-3-030-03496-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03495-5

  • Online ISBN: 978-3-030-03496-2

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