Stochastic Alignments: Matching an Observed Trace to Stochastic Process Models

• URL: http://arxiv.org/abs/2507.06472v1
• Date: Wed, 09 Jul 2025 01:20:53 GMT
• Title: Stochastic Alignments: Matching an Observed Trace to Stochastic Process Models
• Authors: Tian Li, Artem Polyvyanyy, Sander J. J. Leemans,
• Abstract summary: We study the problem of matching an observed trace to a process model by identifying a likely model path with a low edit distance to the trace.<n>Our open-source implementation demonstrates the feasibility of the approach and shows that it can provide new, useful diagnostic insights for analysts.
• Score: 6.757160484361399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Process mining leverages event data extracted from IT systems to generate insights into the business processes of organizations. Such insights benefit from explicitly considering the frequency of behavior in business processes, which is captured by stochastic process models. Given an observed trace and a stochastic process model, conventional alignment-based conformance checking techniques face a fundamental limitation: They prioritize matching the trace to a model path with minimal deviations, which may, however, lead to selecting an unlikely path. In this paper, we study the problem of matching an observed trace to a stochastic process model by identifying a likely model path with a low edit distance to the trace. We phrase this as an optimization problem and develop a heuristic-guided path-finding algorithm to solve it. Our open-source implementation demonstrates the feasibility of the approach and shows that it can provide new, useful diagnostic insights for analysts.

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