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The authors present LEEWAVE - a bandwidth-efficient approach to searching range-specified k-nearest neighbors among distributed streams by LEvEl-wise distribution of WAVElet coefficients. To find the k most similar streams to a range-specified reference one, the relevant wavelet coefficients of the reference stream can be sent to the peer sites to compute the similarities. However, bandwidth can be unnecessarily wasted if the entire relevant coefficients are sent simultaneously. Instead, the authors present a level-wise approach by leveraging the multi-resolution property of the wavelet coefficients. Starting from the top and moving down one level at a time, the query initiator sends only the single-level coefficients to a progressively shrinking set of candidates.
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