Anomaly Detection and Explanation Discovery on Event Streams

Abstract

As enterprise information systems are collecting event streams from various sources, the ability of a system to automatically detect anomalous events and further provide human readable explanations is of paramount importance. In this position paper, we argue for the need of a new type of data stream analytics that can address anomaly detection and explanation discovery in a single, integrated system, which not only offers increased business intelligence, but also opens up opportunities for improved solutions. In particular, we propose a two-pass approach to building such a system, highlight the challenges, and offer initial directions for solutions.