Publikation
Probabilistic Programming for Trace Generation (and Beyond)
Martin Kuhn; Joscha Grüger; Christoph Matheja; Andrey Rivkin
In: Joint Proceedings of the Workshops at the 46th International Conference on Application and Theory of Petri Nets and Concurrency: Petri Nets and Software Engineering (PNSE'25), Algorithms & Theories for the Analysis of Event Data (ATAED’25), and. Algorithms & Theories for the Analysis of Event Data (ATAED-2025), located at Petri Nets 2025, June 24, Paris, France, Pages 160-165, Vol. 3998, CEUR-WS.org, 6/2025.
Zusammenfassung
Probabilistic programming is a programming paradigm that enables the creation of probabilistic models, their
simulation through execution, and their analysis through various inference engines. Recently, it has been applied
in a framework for simulating Data Petri Nets (DPNs) – a class of Petri nets widely used in the business process
community for modeling and reasoning about data-aware processes. In this paper, we summarize recent advances
in this area and outline potential directions for future research. We hope this will highlight the potential of
the synergy between probabilistic programming and process science, and inspire further exploration at the
intersection of these fields.