In System Alignments we Trust! Explainable Alignments via Projections

TL;DR

This paper introduces a novel approach using relaxations via projections to improve process alignments, enabling better handling of imprecise logs and models and distinguishing trustworthy content for enhanced process understanding.

Contribution

It proposes the concept of relaxations through projections for alignments, addressing partial correctness and improving interpretability of process models and logs.

Findings

Relaxed alignments distinguish trustworthy from untrustworthy content.

The approach improves process understanding and quality issue detection.

Enhanced handling of imprecise logs and models.

Abstract

Alignments are a well-known process mining technique for reconciling system logs and normative process models. Evidence of certain behaviors in a real system may only be present in one representation - either a log or a model - but not in the other. Since for processes in which multiple entities, like objects and resources, are involved in the activities, their interactions affect the behavior and are therefore essential to take into account in the alignments. Additionally, both logged and modeled representations of reality may be imprecise and only partially represent some of these entities, but not all. In this paper, we introduce the concept of "relaxations" through projections for alignments to deal with partially correct models and logs. Relaxed alignments help to distinguish between trustworthy and untrustworthy content of the two representations (the log and the model) to achieve a better understanding of the underlying process and expose quality issues.