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PRISM – A Predictive Risk Monitoring Approach for Business Processes

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Business Process Management (BPM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9850))

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Abstract

Nowadays, organizations face severe operational risks when executing their business processes. Some reasons are the ever more complex and dynamic business environment as well as the organic nature of business processes. Taking a risk perspective on the business process management (BPM) lifecycle has thus been recognized as an essential research stream. Despite profound knowledge on risk-aware BPM with a focus on process design, existing approaches for real-time risk monitoring treat instances as isolated when detecting risks. They do not propagate risk information to other instances in order to support early risk detection. To address this gap, we propose an approach for predictive risk monitoring (PRISM). This approach automatically propagates risk information, which has been detected via risk sensors, across similar running instances of the same process in real-time. We demonstrate PRISM’s capability of predictive risk monitoring by applying it in the context of a real-world scenario.

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Notes

  1. 1.

    http://www.camunda.com/. The authors are happy to provide the source code upon request.

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Acknowledgements

This research is partially funded by the ARC Discovery Project DP150103356 and was partially carried out in the context of the Project Group Business and Information Systems Engineering of the Fraunhofer Institute for Applied Information Technology FIT.

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Authors and Affiliations

  1. Queensland University of Technology, Brisbane, Australia

    Raffaele Conforti

  2. FIM Research Center, University of Augsburg, Augsburg, Germany

    Sven Fink & Jonas Manderscheid

  3. FIM Research Center, University of Bayreuth, Bayreuth, Germany

    Maximilian Röglinger

Authors
  1. Raffaele Conforti
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  2. Sven Fink
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  3. Jonas Manderscheid
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  4. Maximilian Röglinger
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Corresponding author

Correspondence to Jonas Manderscheid .

Editor information

Editors and Affiliations

  1. Queensland University of Technology, Brisbane, Queensland, Australia

    Marcello La Rosa

  2. DFKI, Universität des Saarlandes DFKI, Saarbrücken, Saarland, Germany

    Peter Loos

  3. Universidad Politècnica de Valencia , Valencia, Spain

    Oscar Pastor

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Conforti, R., Fink, S., Manderscheid, J., Röglinger, M. (2016). PRISM – A Predictive Risk Monitoring Approach for Business Processes. In: La Rosa, M., Loos, P., Pastor, O. (eds) Business Process Management. BPM 2016. Lecture Notes in Computer Science(), vol 9850. Springer, Cham. https://doi.org/10.1007/978-3-319-45348-4_22

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  • DOI: https://doi.org/10.1007/978-3-319-45348-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45347-7

  • Online ISBN: 978-3-319-45348-4

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