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Petri Net Classes for Collaboration Mining: Assessment and Design Guidelines

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Process Mining Workshops (ICPM 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 503))

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Abstract

Collaboration mining develops discovery, conformance checking, and enhancement techniques for collaboration processes. The collaboration process model is key to represent the discovery result. As for process mining in general, Petri Net classes are candidates for collaboration process models due to their analytical power. However, a standard model class to represent collaboration processes is lacking due to the heterogeneity of collaboration and, thus, of collaboration mining techniques. Collaboration heterogeneity requires to cover, for example, intra-organizational collaborations as well as choreographies that span a process across multiple organizations. A standard collaboration model class would advance collaboration mining by focusing discovery through a common target model, supporting comparison, and enabling flexible mining pipelines. To find a standard model class, we aim at capturing collaboration heterogeneity in a meta model, assess Petri net classes as candidates for collaboration process models through the meta model, and derive design guidelines for the collaboration discovery.

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Notes

  1. 1.

    Many alternative modelling languages can be transformed into Petri nets [40].

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Acknowledgements

This work has been partly funded by the Austrian Research Promotion Agency (FFG) via the “Austrian Competence Center for Digital Production” (CDP) under the contract number 854187.

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  1. TUM School of Computation, Information and Technology, Technical University of Munich, Garching, Germany

    Janik-Vasily Benzin & Stefanie Rinderle-Ma

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  1. Janik-Vasily Benzin
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Correspondence to Janik-Vasily Benzin .

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  1. KU Leuven, Leuven, Belgium

    Johannes De Smedt

  2. University of Haifa, Haifa, Israel

    Pnina Soffer

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Benzin, JV., Rinderle-Ma, S. (2024). Petri Net Classes for Collaboration Mining: Assessment and Design Guidelines. In: De Smedt, J., Soffer, P. (eds) Process Mining Workshops. ICPM 2023. Lecture Notes in Business Information Processing, vol 503. Springer, Cham. https://doi.org/10.1007/978-3-031-56107-8_34

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