Stochastic Alignments: Matching an Observed Trace to Stochastic Process Models

TL;DR

This paper introduces a heuristic algorithm for aligning observed event traces with stochastic process models, improving conformance checking by considering likelihoods rather than just minimal deviations, thus aiding process analysis.

Contribution

It presents a novel optimization approach and heuristic algorithm for matching traces to stochastic models, addressing limitations of traditional alignment techniques.

Findings

The approach effectively identifies likely model paths with low edit distance.

Open-source implementation demonstrates practical feasibility.

Provides new diagnostic insights for process analysts.

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.