Extensions, Analysis and Experimental Assessment of a Probabilistic Ensemble-Learning Framework for Detecting Deviances in Business Process Instances

The problem of discovering an effective Deviance Detection Model (DDM) out of log data, has been attracting increasing attention in recent years in the very active research areas of Business Process Intelligence (BPI) and of Process Mining. Such a model can be used to assess whether novel instances of the business process are deviant or not, which is a hot topic in many application scenarios such as cybersecurity and fraud detection. This paper extends a previous proposal where an innovative ensemble-learning framework for mining business process deviances was introduced, hinging on multi-view learning scheme. Specifically, we introduce here an alternative meta-learning method for probabilistically combining the predictions of different base DDMs. The entire learning method is embedded into a conceptual system architecture that is meant to support the detection and analysis of deviances in a Business Process Management scenario. We also discuss a wide and comprehensive experimental analysis of the proposed approach and of a state-of-the-art DDM discovery solution. The experimental findings confirm the flexibility, reliability and effectiveness of the proposed deviance detection approach, and the improvement gained over its previous version.