Efficiently and Accurately Comparing Real-valued Data Streams

Data streams are pervasive in many modern applications, and there is a pressing need to develop techniques for their efficient management. In this paper we consider real-valued streams and deal with the problem of reporting in real-time all the instants in which their distance falls below a given threshold. Current distance measures, such as Euclidean and Dynamic Time Warping (DTW ), either are inaccurate or are too time-consuming to be applied in a streaming environment. We propose SDTW , a novel DTW -like distance measure which can be continuously updated in constant time and experimentally show that it improves over DTW by orders of magnitude without sacrificing accuracy.