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- W45917717 abstract "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." @default.
- W45917717 created "2016-06-24" @default.
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- W45917717 date "2005-01-01" @default.
- W45917717 modified "2023-09-27" @default.
- W45917717 title "Efficiently and Accurately Comparing Real-valued Data Streams" @default.
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