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Towards Simulation- and Mining-Based Translation of Process Models

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Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 272))

Abstract

Process modeling is usually done using imperative modeling languages like BPMN or EPCs. In order to cope with the complexity of human-centric and flexible business processes several declarative process modeling languages (DPMLs) have been developed during the last years. DPMLs allow for the specification of constraints that restrict execution flows. They differ widely in terms of their level of expressiveness and tool support. Furthermore, research has shown that the understandability of declarative process models is rather low. Since there are applications for both classes of process modeling languages, there arises a need for an automatic translation of process models from one language into another. Our approach is based upon well-established methodologies in process management for process model simulation and process mining without requiring the specification of model transformation rules. In this paper, we present the technique in principle and evaluate it by transforming process models between two exemplary process modeling languages.

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Notes

  1. 1.

    The BPMN 2.0 standard is available at http://www.omg.org/spec/BPMN/2.0/.

  2. 2.

    Declare does not distinguish between different transaction types.

  3. 3.

    KPI = Key Performance Indicator (used in performance measurement to rate success regarding a particular ambition).

  4. 4.

    Log available at: http://www.win.tue.nl/bpi/doku.php?id=2014:challenge.

  5. 5.

    ICT = Information and Communication Technology.

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Correspondence to Lars Ackermann .

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Ackermann, L., Schönig, S., Jablonski, S. (2016). Towards Simulation- and Mining-Based Translation of Process Models. In: Pergl, R., Molhanec, M., Babkin, E., Fosso Wamba, S. (eds) Enterprise and Organizational Modeling and Simulation. EOMAS 2016. Lecture Notes in Business Information Processing, vol 272. Springer, Cham. https://doi.org/10.1007/978-3-319-49454-8_1

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  • DOI: https://doi.org/10.1007/978-3-319-49454-8_1

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