Probabilistic Trace Alignment

TL;DR

This paper introduces a novel probabilistic trace alignment method using stochastic Workflow nets to improve conformance checking by balancing alignment costs and model likelihoods.

Contribution

It presents the first approach to probabilistic trace alignment, integrating stochastic process models into alignment-based conformance checking.

Findings

First probabilistic trace alignment method using stochastic Workflow nets.

Balances alignment costs with model likelihoods for improved diagnostics.

Enhances process diagnostics by incorporating uncertainty in models.

Abstract

Alignments provide sophisticated diagnostics that pinpoint deviations in a trace with respect to a process model and their severity. However, approaches based on trace alignments use crisp process models as reference and recent probabilistic conformance checking approaches check the degree of conformance of an event log with respect to a stochastic process model instead of finding trace alignments. In this paper, for the first time, we provide a conformance checking approach based on trace alignments using stochastic Workflow nets. Conceptually, this requires to handle the two possibly contrasting forces of the cost of the alignment on the one hand and the likelihood of the model trace with respect to which the alignment is computed on the other.