On historical diagnosis of sensor streams

Abstract

In this paper, we will examine the problem of historical storage and diagnosis of massive numbers of simultaneous streams. Such streams are common in very large sensor systems which collect many data streams simultaneously. For example, in a typical monitoring application, we may desire to determine specific abnormalities at sensor nodes or diagnose local regions of abnormal behavior. In other applications, a user may wish to query the streams for specific behavior of the data over arbitrary time horizons. This can be a very difficult task if it is not possible to store the voluminous sensor information at different nodes. In many cases, it is only possible to store aggregated data over different nodes. In this paper, we discuss the problem of storage-efficient monitoring and diagnosis of sensor networks with the use of summary representations. The goal of the summary representation is to providing worst-case guarantees on query functions computed over the sensor stream, while storing the streams compactly. We present experimental results on a number of real data sets showing the effectiveness of the approach.