Discovery of cross-similarity in data streams

In this paper, we focus on the problem of finding partial similarity between data streams. Our solution relies on dynamic time warping (DTW) as a similarity measure, which computes the distance between sequences whose lengths and/or sampling rates are different. Instead of straightforwardly using DTW that requires a high computation cost, we propose a streaming method that efficiently detects partial similarity between sequences. Our experiments demonstrate that our method detects pairs of optimal subsequences correctly and that it significantly reduces resources in terms of time and space.