Deviance mining with treatment learning and declare-based encoding of event logs

Abstract

Business Process Deviance Mining is a research area that aims to characterize deviations of a business process from its expected outcomes. Techniques within this area discover which features of a set of process executions are associated to changes in process performance, providing insights on which process behavior leads to the best performance and also revealing behaviors that result in undesired process outcomes. In this sense, performance may refer to time, cost, resource dimensions or to any domain-specific performance indicator. Existing techniques for business process deviance mining are based on the extraction of patterns from event logs, using different pattern mining approaches. Up to date these extraction patterns have limited expressiveness, since they are not able to capture complex relationships that may be present in highly-flexible processes. In this work, we propose a new encoding technique for vector-based representation of process instances, and then apply Treatment Learning as a novel approach in the context of Deviance Mining to identify the characteristics of a process that mostly impact its performance. The proposed encoding technique is based on the fulfillment of Declare constraint templates, which makes it able to discover more expressive treatments. We compare our proposal with current process encoding techniques in a series of experiments with publicly available event logs from real-life processes. The results showed that treatment learning, together with our proposed Declare-based encoding, produced relevant and more expressive insights from the event logs, being a practical application for process analysis.