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Towards Automated Support for Case Management Processes with Declarative Configurable Specifications

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Business Process Management Workshops (BPM 2012)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 132))

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Abstract

Until recently, efficiency gained through process automation and control was the main preoccupation of BPM practitioners. As a result, the majority of mainstream process modeling standards today is characterized by the imperative modeling style. This style encourages a modeler to commit to a well-determined process execution scenario already at the early design stages. For case management processes, however, a strict commitment to a predefined control flow is considered by organizations as a serious handicap. This is the main reason why case management as well as other knowledge-intensive processes in the organizations mostly remain “pen and paper”. In this article we demonstrate how configurable data objects and context-based configuration rules can be integrated into a process model in order to improve the process post-design adaptability and to pave the road for case management automated support. These concepts are defined as a part of DeCo (the Declarative Configurable process specification language). DeCo is a declarative modeling approach that is currently under development. We illustrate our results on the example.

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Authors and Affiliations

  1. Centre de Recherches en Informatique, University Paris 1 Pantheon-Sorbonne, 90, rue Tolbiac, 75013, Paris, France

    Irina Rychkova

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  1. Irina Rychkova
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Editors and Affiliations

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

    Marcello La Rosa

  2. University of Haifa, Haifa, Israel

    Pnina Soffer

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Rychkova, I. (2013). Towards Automated Support for Case Management Processes with Declarative Configurable Specifications. In: La Rosa, M., Soffer, P. (eds) Business Process Management Workshops. BPM 2012. Lecture Notes in Business Information Processing, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36285-9_9

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  • DOI: https://doi.org/10.1007/978-3-642-36285-9_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36284-2

  • Online ISBN: 978-3-642-36285-9

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