Anomaly Detection in Business Process based on Data Stream Mining
Gabriel Marques Tavares, Victor G. Turrisi da Costa, Vinicius Eiji Martins, Paolo Ceravolo, Sylvio Barbon · 2018
Identifying fraudulent or anomalous business procedures is today a key challenge for organisations of any dimension. Nevertheless, the continuous nature of business conveys to the continuous acquisition of data in support of business process monitoring. In light of this, we propose a method for online anomaly detection in business processes. From a stream of events, our approach extract cases descriptors and applies a density-based clustering technique to detect outliers. We applied our method to a real-life dataset, and we used streaming clustering measures for evaluating performances. In particular, we obtained Cluster Mapping Measure of 95.3% and Homogeneity of 98.1% discovering anomalous cases in real-time.