A Framework for Human-in-the-loop Monitoring of Concept-drift Detection in Event Log Stream

One of the main challenges of Cognitive Computing (CC) is reacting to evolving environments in near-real time. Therefore, it is expected that CC models provide solutions by examining a summary of past history, rather than using full historical data. This strategy has significant benefits in terms of response time and space complexity but poses new challenges in term of concept-drift detection, where both long term and short terms dynamics should be taken into account. In this paper, we introduce the Concept-Drift in Event Stream Framework (CDESF) that addresses some of these challenges for data streams recording the execution of a Web-based business process. Thanks to CDESF support for feature transformation, we perform density clustering in the transformed feature space of the process event stream, observe track concept-drift over time and identify anomalous cases in the form of outliers. We validate our approach using logs of an e-healthcare process.