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Applying Case Management Principles to Support Analytics Process Management

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Business Process Management Workshops (BPM 2016)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 256))

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Abstract

Analytics Process Management (APM) is an emerging branch of Business Process Management that is focused on supporting Business Analysts and others as they apply analytics approaches, algorithms, and outputs in order to discover and/or repeatedly produce business-relevant insights and apply them into on-going business operations. While APM is now occurring in many businesses, it is typically managed in ad hoc ways using a variety of different tools and practices. This paper proposes to use principles from Case Management (or equivalently, Business Artifacts) to provide a foundational structure for APM. In particular, six key classes of Case Types are identified, that can model the vast majority of activities and data being manipulated in APM contexts. These Case Types can simplify support for managing provenance, auditability, repeatability, and explanation of analytics results. The paper also identifies two key adaptations of the classical Case Management paradigm that are needed to support APM. The paper validates the proposed Case Types and adaptations by examining two recent systems built at IBM Research that support Business Analysists in the use of analytics tools.

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Authors and Affiliations

  1. IBM T.J. Watson Research Center, Yorktwon Heights, NY, USA

    Fenno F. Heath III, Richard Hull & Daniel Oppenheim

Authors
  1. Fenno F. Heath III
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  2. Richard Hull
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  3. Daniel Oppenheim
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Corresponding author

Correspondence to Richard Hull .

Editor information

Editors and Affiliations

  1. Institute of Databases and Information Systems, University of Ulm, Ulm, Germany

    Manfred Reichert

  2. Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Hajo A. Reijers

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Heath III, F.F., Hull, R., Oppenheim, D. (2016). Applying Case Management Principles to Support Analytics Process Management. In: Reichert, M., Reijers, H. (eds) Business Process Management Workshops. BPM 2016. Lecture Notes in Business Information Processing, vol 256. Springer, Cham. https://doi.org/10.1007/978-3-319-42887-1_31

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  • DOI: https://doi.org/10.1007/978-3-319-42887-1_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42886-4

  • Online ISBN: 978-3-319-42887-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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