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International Conference on Business Process Management

BPM 2016: Business Process Management pp 350–364Cite as

Goal-Aligned Categorization of Instance Variants in Knowledge-Intensive Processes

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9253))

Abstract

Discovering and reasoning about deviations of business process executions (from intended designs) enables organizations to continuously evaluate their execution/performance relative to their strategic goals. We leverage the observation that a deviating process instance can be viewed as a valid variant of the intended process design provided it achieves the same goals as the intended process design. However, organizations often find it difficult to categorize and classify process execution deviations in a goal-based fashion (necessary to decide if a deviation represents a valid variant). Given that industry-scale knowledge-intensive processes typically manifest a large number of variants, this can pose a problem. In this paper, we propose an approach to help decide whether process instances in execution logs are valid variants using the goal-based notion of validity described above. Our proposed approach also enables analysis of the impact of contextual factors in the execution of specific goal-aligned process variants. We demonstrate our approach with an Eclipse-based plugin and evaluate it using an industry-scale setting in IT Incident Management with a process log of 25000 events.

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Author information

Authors and Affiliations

  1. IBM Research India, Bangalore, India

    Karthikeyan Ponnalagu

  2. Cognizant Technology Solutions, Bangalore, India

    Nanjangud C. Narendra

  3. University of Wollongong, Wollongong, Australia

    Karthikeyan Ponnalagu, Aditya Ghose & Hoa Khanh Dam

Authors
  1. Karthikeyan Ponnalagu
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  2. Aditya Ghose
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  3. Nanjangud C. Narendra
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  4. Hoa Khanh Dam
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Corresponding author

Correspondence to Karthikeyan Ponnalagu .

Editor information

Editors and Affiliations

  1. IBM Almaden Research Center, San Jose, California, USA

    Hamid Reza Motahari-Nezhad

  2. Queensland University, Brisbane, Queensland, Australia

    Jan Recker

  3. Humboldt-Universität zu Berlin, Berlin, Germany

    Matthias Weidlich

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Ponnalagu, K., Ghose, A., Narendra, N.C., Dam, H.K. (2015). Goal-Aligned Categorization of Instance Variants in Knowledge-Intensive Processes. In: Motahari-Nezhad, H., Recker, J., Weidlich, M. (eds) Business Process Management. BPM 2016. Lecture Notes in Computer Science(), vol 9253. Springer, Cham. https://doi.org/10.1007/978-3-319-23063-4_24

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  • DOI: https://doi.org/10.1007/978-3-319-23063-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23062-7

  • Online ISBN: 978-3-319-23063-4

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