Abstract
In this paper, we propose a solution to the multi-step k-nearest neighbor (k-NN) search. The method is the reduced tolerance-based k-NN search for static queries in streaming time-series. A multi-scale filtering technique combined with a multi-resolution index structure is used in the method. We compare the proposed method to the traditional multi-step k-NN search in terms of the CPU search time and the number of distance function calls in the post-processing step. The results reveal that the reduced tolerance-based k-NN search outperforms the traditional k-NN search. Besides, applying multi-threading to the proposed method enables the system to have a fast response to high speed time-series streams for the k-NN search of static queries.
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© 2015 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Giao, B.C., Anh, D.T. (2015). Efficient k-Nearest Neighbor Search for Static Queries over High Speed Time-Series Streams. In: Vinh, P., Vassev, E., Hinchey, M. (eds) Nature of Computation and Communication. ICTCC 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-319-15392-6_9
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DOI: https://doi.org/10.1007/978-3-319-15392-6_9
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