Data Petri Nets meet Probabilistic Programming (Extended version)
This work addresses the integration of probabilistic programming with process modeling for researchers in process mining and AI, though it is incremental as it builds on existing paradigms.
The paper tackles the problem of reasoning about data-aware processes by translating Data Petri Nets into a probabilistic programming language, showing that the translation is sound and provides statistical guarantees for simulation.
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. We also discuss further analysis scenarios that could be easily approached based on the proposed translation and available PP tools.