Strategies to automatically derive a process model from a configurable process model based on event data

M. Arriagada-Benítez (Corresponding author), M. Sepúlveda, J. Munoz-Gama, J.C.A.M. Buijs

    Research output: Contribution to journalArticleAcademicpeer-review

    16 Citations (Scopus)
    66 Downloads (Pure)

    Abstract

    Configurable process models are frequently used to represent business workflows and other discrete event systems among different branches of large organizations: they unify commonalities shared by all branches and describe their differences, at the same time. The configuration of such models is usually done manually, which is challenging. On the one hand, when the number of configurable nodes in the configurable process model grows, the size of the search space increases exponentially. On the other hand, the person performing the configuration may lack the holistic perspective to make the right choice for all configurable nodes at the same time, since choices influence each other. Nowadays, information systems that support the execution of business processes create event data reflecting how processes are performed. In this article, we propose three strategies (based on exhaustive search, genetic algorithms and a greedy heuristic) that use event data to automatically derive a process model from a configurable process model that better represents the characteristics of the process in a specific branch. These strategies have been implemented in our proposed framework and tested in both business-like event logs as recorded in a higher educational enterprise resource planning system and a real case scenario involving a set of Dutch municipalities.

    Original languageEnglish
    Article number1023
    Number of pages28
    JournalApplied Sciences
    Volume7
    Issue number10
    DOIs
    Publication statusPublished - 4 Oct 2017

    Keywords

    • Business processes
    • Business workflows
    • Configurable process models
    • Configurable process trees
    • Discrete event systems
    • Event logs
    • Process mining

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