Discovering Process Models that Support Desired Behavior and Avoid Undesired Behavior

TL;DR

This paper introduces a process discovery method that constructs models supporting desired behaviors while avoiding undesired ones, using both positive and negative event logs to improve process models.

Contribution

It proposes a novel inductive mining approach that incorporates negative examples, enabling the discovery of process models that favor good behavior and exclude bad behavior.

Findings

Outperforms existing methods using only positive logs

Effectively balances supporting desirable and avoiding undesirable cases

Validated on real-life event logs

Abstract

Process discovery is one of the primary process mining tasks and starting point for process improvements using event data. Existing process discovery techniques aim to find process models that best describe the observed behavior. The focus can be on recall (i.e., replay fitness) or precision. Here, we take a different perspective. We aim to discover a process model that allows for the good behavior observed, and does not allow for the bad behavior. In order to do this, we assume that we have a desirable event log ($L^+$) and an undesirable event log ($L^-$). For example, the desirable event log consists of the cases that were handled within two weeks, and the undesirable event log consists of the cases that took longer. Our discovery approach explores the tradeoff between supporting the cases in the desirable event log and avoiding the cases in the undesirable event log. The proposed framework uses a new inductive mining approach that has been implemented and tested on several real-life event logs. Experimental results show that our approach outperforms other approaches that use only the desirable event log ($L^+$). This supports the intuitive understanding that problematic cases can and should be used to improve processes.