A Multi-view Ensemble of Deep Models for the Detection of Deviant Process Instances

Abstract

Mining deviances from expected behaviors in process logs is a relevant problem in modern organizations, owing to their negative impact in terms of monetary/reputation losses. Most proposals to deviance mining combine the extraction of behavioral features from log traces with the induction of standard classifiers. Difficulties in capturing the multi-faceted nature of deviances with a single pattern family led to explore the possibility to mix up heterogeneous data views, obtained each with a different pattern family. Unfortunately, combining many pattern families tends to produce sparse and redundant representations that likely lead to the discovery of poor deviance-oriented classifiers. Using a multi-view ensemble learning approach to combine alternative trace representations was recently proven effective for this induction task. On the other hand, Deep Learning methods have been gaining momentum in prediction/classification tasks on process log data, owing to their flexibility and expressiveness. We here propose a novel multi-view ensemble-based framework for the discovery of deviance-oriented classifiers that profitably combines different single-view deep classifiers, sharing an ad hoc residual-like architecture (simulating fine-grain ensemble-like capabilities over each single data view). The approach, tested over real-life process log data, significantly improves previous solutions.