Enjoy the Silence: Analysis of Stochastic Petri Nets with Silent Transitions
This work addresses a specific bottleneck in process mining for business and work processes, enabling quantitative analysis of stochastic behaviors with internal steps, though it appears incremental as it builds on generalized stochastic Petri nets.
The paper tackles the challenge of analyzing stochastic Petri nets with silent transitions and activity labels in a finite-trace semantics, which existing methods cannot handle directly. It introduces labelled stochastic processes (LSPs) and shows that key analysis tasks can be solved analytically by combining automata-based techniques with absorbing Markov chains, demonstrating practical feasibility through a proof-of-concept implementation.
Capturing stochastic behaviors in business and work processes is essential to quantitatively understand how nondeterminism is resolved when taking decisions within the process. This is of special interest in process mining, where event data tracking the actual execution of the process are related to process models, and can then provide insights on frequencies and probabilities. Variants of stochastic Petri nets provide a natural formal basis for this. However, when capturing processes, such nets need to be labelled with (possibly duplicated) activities, and equipped with silent transitions that model internal, non-logged steps related to the orchestration of the process. At the same time, they have to be analyzed in a finite-trace semantics, matching the fact that each process execution consists of finitely many steps. These two aspects impede the direct application of existing techniques for stochastic Petri nets, calling for a novel characterization that incorporates labels and silent transitions in a finite-trace semantics. In this article, we provide such a characterization starting from generalized stochastic Petri nets and obtaining the framework of labelled stochastic processes (LSPs). On top of this framework, we introduce different key analysis tasks on the traces of LSPs and their probabilities. We show that all such analysis tasks can be solved analytically, in particular reducing them to a single method that combines automata-based techniques to single out the behaviors of interest within a LSP, with techniques based on absorbing Markov chains to reason on their probabilities. Finally, we demonstrate the significance of how our approach in the context of stochastic conformance checking, illustrating practical feasibility through a proof-of-concept implementation and its application to different datasets.