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Data Object Cardinalities in Flexible Business Processes

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Business Process Management Workshops (BPM 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 397))

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Abstract

Business process models are an important tool for business process management, used to specify, enact, and analyze organizations’ processes. Traditional process modeling languages are particularly well suited for highly structured, predictable processes but lead to complex models when faced with flexible data-centric processes such as knowledge-intensive ones. Different approaches address this gap. However, while data is central in many modeling languages for knowledge-intensive processes, the impact of a data model (containing classes, associations, and cardinalities) on the process behavior has not been investigated holistically. We extend the fragment-based Case Management (fCM) approach with data cardinalities and discuss the impact of cardinalities on process execution and analysis, as well as flexibility.

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Notes

  1. 1.

    Assuming that run-time adaptability is not required.

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Authors and Affiliations

  1. Hasso Plattner Institute, University of Potsdam, 14482, Potsdam, Germany

    Stephan Haarmann & Mathias Weske

Authors
  1. Stephan Haarmann
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  2. Mathias Weske
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Corresponding author

Correspondence to Stephan Haarmann .

Editor information

Editors and Affiliations

  1. Universidad de Sevilla, Seville, Spain

    Adela Del Río Ortega

  2. Kühne Logistics University, Hamburg, Germany

    Henrik Leopold

  3. Universidade do Estado do Rio de Janeiro – UERJ, Rio de Janeiro, Brazil

    Flávia Maria Santoro

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Haarmann, S., Weske, M. (2020). Data Object Cardinalities in Flexible Business Processes. In: Del Río Ortega, A., Leopold, H., Santoro, F.M. (eds) Business Process Management Workshops. BPM 2020. Lecture Notes in Business Information Processing, vol 397. Springer, Cham. https://doi.org/10.1007/978-3-030-66498-5_28

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  • DOI: https://doi.org/10.1007/978-3-030-66498-5_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66497-8

  • Online ISBN: 978-3-030-66498-5

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