FoldA: Computing Partial-Order Alignments Using Directed Net Unfoldings

TL;DR

FoldA introduces a novel method for computing partial-order alignments in process mining by utilizing directed Petri net unfoldings, effectively capturing concurrency and reducing state space explosion compared to traditional approaches.

Contribution

The paper presents FoldA, a new technique that computes partial-order alignments using directed Petri net unfoldings, addressing concurrency and state explosion issues in process conformance checking.

Findings

Reduces queued states compared to traditional methods

Provides a more accurate representation of concurrent behavior

Achieves better alignment quality at the cost of increased computation time

Abstract

Conformance checking is a fundamental task of process mining, which quantifies the extent to which the observed process executions match a normative process model. The state-of-the-art approaches compute alignments by exploring the state space formed by the synchronous product of the process model and the trace. This often leads to state space explosion, particularly when the model exhibits a high degree of choice and concurrency. Moreover, as alignments inherently impose a sequential structure, they fail to fully represent the concurrent behavior present in many real-world processes. To address these limitations, this paper proposes a new technique for computing partial-order alignments {on the fly using directed Petri net unfoldings, named FoldA. We evaluate our technique on 485 synthetic model-log pairs and compare it against Astar- and Dijkstra-alignments on 13 real-life model-log pairs and 6 benchmark pairs. The results show that our unfolding alignment, although it requires more computation time, generally reduces the number of queued states and provides a more accurate representation of concurrency.