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Artifact-Driven Process Monitoring at Scale

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Service-Oriented Computing (ICSOC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14420))

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Abstract

Artifact-driven process monitoring is an effective technique to autonomously monitor business processes. Instead of requiring human operators to notify when an activity is executed, artifact-driven process monitoring infers this information from the conditions of physical or virtual objects taking part in a process. However, SMARTifact, the existing monitoring platform implementing this technique, has been designed to run entirely on edge devices, each of which can monitor only one execution of the process. Thus, monitoring multiple executions at the same time, or reducing the computational requirements of edge devices is not possible. In this paper, we introduce a new artifact-driven monitoring platform that overcomes these limitations and makes artifact-driven monitoring fully scalable.

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Notes

  1. 1.

    Source code available at https://github.com/eGSM-platform.

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Author information

Authors and Affiliations

  1. Technical University of Denmark, Kgs. Lyngby, Denmark

    Giovanni Meroni & Szabolcs Garda

Authors
  1. Giovanni Meroni
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  2. Szabolcs Garda
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Corresponding author

Correspondence to Giovanni Meroni .

Editor information

Editors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Flavia Monti

  2. Technical University of Munich, Garching, Germany

    Stefanie Rinderle-Ma

  3. University of Seville, Seville, Spain

    Antonio Ruiz Cortés

  4. Sun Yat-sen University, Guangzhou, China

    Zibin Zheng

  5. Sapienza University of Rome, Rome, Italy

    Massimo Mecella

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Meroni, G., Garda, S. (2023). Artifact-Driven Process Monitoring at Scale. In: Monti, F., Rinderle-Ma, S., Ruiz Cortés, A., Zheng, Z., Mecella, M. (eds) Service-Oriented Computing. ICSOC 2023. Lecture Notes in Computer Science, vol 14420. Springer, Cham. https://doi.org/10.1007/978-3-031-48424-7_1

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  • DOI: https://doi.org/10.1007/978-3-031-48424-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48423-0

  • Online ISBN: 978-3-031-48424-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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