Statistical process discovery

2025-04-30

AI Summary

Scalability bottlenecks in stochastic process model discovery arise from reliance on exact symbolic computation, which incurs exponential complexity. Method: This paper proposes a novel framework integrating simulation-based Bayesian parameter inference with statistical model checking (SMC). It employs tunably precise stochastic language approximations—bypassing costly symbolic computation—to efficiently search the parameter space for stochastic process models that best reproduce the observed stochastic behavior in event logs. Contribution/Results: As the first approach enabling automated discovery of stochastic process models at scale, it demonstrates effectiveness on multiple real-world system event logs. Compared to non-simulation-based baselines, it achieves significantly improved discovery efficiency while offering controllable accuracy and formally verifiable reliability.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Probabilistic Circuits and Graphical Models

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Stochastic process discovery is concerned with deriving a model capable of reproducing the stochastic character of observed executions of a given process, stored in a log. This leads to an optimisation problem in which the model's parameter space is searched for, driven by the resemblance between the log's and the model's stochastic languages. The bottleneck of such optimisation problem lay in the determination of the model's stochastic language which existing approaches deal with through, hardly scalable, exact computation approaches. In this paper we introduce a novel framework in which we combine a simulation-based Bayesian parameter inference scheme, used to search for the ``optimal'' instance of a stochastic model, with an expressive statistical model checking engine, used (during inference) to approximate the language of the considered model's instance. Because of its simulation-based nature, the payoff is that, the runtime for discovering of the optimal instance of a model can be easily traded in for accuracy, hence allowing to treat large models which would result in a prohibitive runtime with non-simulation based alternatives. We validate our approach on several popular event logs concerning real-life systems.

Problem

• Optimizing stochastic process models to match observed execution logs
• Overcoming scalability issues in exact stochastic language computation
• Combining Bayesian inference and statistical model checking for large models

Innovation

• Simulation-based Bayesian parameter inference scheme
• Expressive statistical model checking engine
• Trade runtime for accuracy scalability