LogPPL: A Tool for Probabilistic Process Mining

This paper introduces LogPPL, a novel tool designed to bridge the gap between Data Petri Nets (DPNs) and probabilistic programming, enabling the generation of event logs with statistical guarantees via probabilistic program executions. LogPPL implements the transformation of DPNs into probabilistic programs written in the WebPPL language, allowing to harness the power of simulation and inference engines supplied for the WebPPL environment. Our tool simplifies the configuration of the DPN simulation setup and allows for exporting both event logs in XES format as well as WebPPL files. LogPPL capabilities are demonstrated through various scenarios, showcasing its potential to enhance process mining tasks by offering rigorous statistical modeling and advanced simulation features. The tool’s design, features, and performance are evaluated, highlighting its utility in both academic and industrial settings.