Process discovery on deviant traces and other stranger things

TL;DR

This paper introduces NegDis, a novel approach for declarative process discovery that leverages both normal and deviant traces as a binary supervised learning task to produce optimal models.

Contribution

It presents a new binary supervised learning framework for process discovery that incorporates deviant traces, enhancing model accuracy and robustness.

Findings

NegDis outperforms existing methods in accuracy

It effectively utilizes deviant traces for better modeling

Results show improved model quality and performance

Abstract

As the need to understand and formalise business processes into a model has grown over the last years, the process discovery research field has gained more and more importance, developing two different classes of approaches to model representation: procedural and declarative. Orthogonally to this classification, the vast majority of works envisage the discovery task as a one-class supervised learning process guided by the traces that are recorded into an input log. In this work instead, we focus on declarative processes and embrace the less-popular view of process discovery as a binary supervised learning task, where the input log reports both examples of the normal system execution, and traces representing "stranger" behaviours according to the domain semantics. We therefore deepen how the valuable information brought by both these two sets can be extracted and formalised into a model that is "optimal" according to user-defined goals. Our approach, namely NegDis, is evaluated w.r.t. other relevant works in this field, and shows promising results as regards both the performance and the quality of the obtained solution.