Publikation
Data Petri Nets Meet Probabilistic Programming
Martin Kuhn; Joscha Grüger; Christoph Matheja; Andrey Rivkin
In: Andrea Marrella; Manuel Resinas; Mieke Jans; Michael Rosemann (Hrsg.). Proceedings of the 22nd International Conference on Business Process Management. Business Process Management (BPM-2024), September 1-6, Krakow, Poland, Pages 21-38, LNCS, Vol. 14940, ISBN 978-3-031-70396-6, Springer, Switzerland, 9/2024.
Zusammenfassung
Probabilistic programming (PP) is a programming paradigm that allows for writing statistical models like ordinary programs, performing simulations by running those programs, and analyzing and refining their statistical behavior using powerful inference engines. This paper takes a step towards leveraging PP for reasoning about data-aware processes. To this end, we present a systematic translation of Data Petri Nets (DPNs) into a model written in a PP language whose features are supported by most PP systems. We show that our translation is sound and provides statistical guarantees for simulating DPNs. Furthermore, we discuss how PP can be used for process mining tasks and report on a prototype implementation of our translation and discuss further analysis scenarios that could be easily approached based on the proposed translation and available PP tools.