Alignment Approximation for Process Trees

TL;DR

This paper introduces a novel hierarchical approximation framework for process tree alignments, significantly reducing computational complexity while maintaining accuracy, thus enhancing conformance checking in process mining.

Contribution

It presents a new method exploiting process tree structures to approximate alignments efficiently, addressing the state space explosion problem in conformance checking.

Findings

Balances accuracy and computation time effectively

Reduces complexity of alignment calculations

Applicable to process mining with process trees

Abstract

Comparing observed behavior (event data generated during process executions) with modeled behavior (process models), is an essential step in process mining analyses. Alignments are the de-facto standard technique for calculating conformance checking statistics. However, the calculation of alignments is computationally complex since a shortest path problem must be solved on a state space which grows non-linearly with the size of the model and the observed behavior, leading to the well-known state space explosion problem. In this paper, we present a novel framework to approximate alignments on process trees by exploiting their hierarchical structure. Process trees are an important process model formalism used by state-of-the-art process mining techniques such as the inductive mining approaches. Our approach exploits structural properties of a given process tree and splits the alignment computation problem into smaller sub-problems. Finally, sub-results are composed to obtain an alignment. Our experiments show that our approach provides a good balance between accuracy and computation time.