Stochastic Alignments: Matching an Observed Trace to Stochastic Process Models

2025-07-08

AI Summary

Existing alignment-based conformance checking methods prioritize model paths with minimal edit distance to observed traces, neglecting their probabilistic plausibility—leading to low-probability, high-bias explanations. This paper proposes a path-likelihood-oriented alignment optimization framework that jointly minimizes edit distance and maximizes path probability within a stochastic process model, thereby identifying high-likelihood, low-deviation alignments. The method integrates stochastic process modeling, probability-aware heuristic search, and efficient optimization techniques. An open-source implementation demonstrates substantial improvements in alignment reasonableness and diagnostic utility across multiple business process scenarios. By unifying statistical rigor with interpretability, the approach establishes a novel paradigm for process deviation analysis.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Heuristic Search
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Matching observed traces to likely stochastic model paths
• Optimizing low edit distance and high path likelihood
• Improving alignment-based conformance checking techniques

Innovation

• Heuristic-guided path-finding algorithm for trace alignment
• Optimization problem formulation for likely model paths
• Open-source implementation demonstrating diagnostic feasibility