XStream : Explaining Anomalies in Event Stream Monitoring

In this paper, we present the XStream system that provides high-quality explanations for anomalous behaviors that users annotate on CEP-based monitoring results. Given the new requirements for explanations, namely, conciseness, consistency with human interpretation, and prediction power, most existing techniques cannot produce explanations that satisfy all three of them. The key technical contributions of this work include a formal definition of optimally explaining anomalies in CEP monitoring, and three key techniques for generating su cient feature space, characterizing the contribution of each feature to the explanation, and selecting a small subset of features as the optimal explanation, respectively. Evaluation using two real-world use cases shows that XStream can outperform existing techniques significantly in conciseness and consistency while achieving comparable high prediction power and retaining a highly e cient implementation of a data stream system.