On Abnormality Detection in Spuriously Populated Data Streams

In recent years, advances in hardware technology have made it increasingly easy to collect large amounts of multidimensional data in an automated way. Such databases continuously grow over time, and are referred to as data streams. The behavior of such streams is sensitive to the underlying events which create the stream. In many applications, it is useful to predict abnormal events in the stream in a fast and online fashion. This is often a difficult goal in a fast data stream because of the time criticality of the detection process. Furthermore, the rare events may often be embedded with other spurious abnormalities, which affect the stream in similar ways. Therefore, it is necessary to be able to distinguish between different kinds of events in order to create a credible detection system. This paper discusses a method for supervised abnormality detection from multi-dimensional data streams, so that high specificity of abnormality detection is achieved. We present empirical results illustrating the effectiveness of our method.