Publication details

Binary Sketches for Secondary Filtering

Authors

MÍČ Vladimír NOVÁK David ZEZULA Pavel

Type Article in Periodical
Magazine / Source ACM Transactions on Information Systems
MU Faculty or unit

Faculty of Informatics

Citation
WWW https://dl.acm.org/citation.cfm?id=3231936
Doi http://dx.doi.org/10.1145/3231936
Keywords Top-k retrieval in databases;Retrieval efficiency;Retrieval effectiveness;Similarity measures;
Description This paper addresses the problem of matching the most similar data objects to a given query object. We adopt a generic model of similarity that involves the domain of objects and metric distance functions only. We examine the case of a large dataset in a complex data space which makes this problem inherently difficult. Many indexing and searching approaches have been proposed but they have often failed to efficiently prune complex search spaces and access large portions of the dataset when evaluating queries. We propose an approach to enhancing the existing search techniques so as to significantly reduce the number of accessed data objects while preserving the quality of the search results. In particular, we extend each data object with its sketch, a short binary string in Hamming space. These sketches approximate the similarity relationships in the original search space, and we use them to filter out non-relevant objects not pruned by the original search technique. We provide a probabilistic model to tune the parameters of the sketch-based filtering separately for each query object. Experiments conducted with different similarity search techniques and real-life datasets demonstrate that the secondary filtering can speed-up similarity search several times.
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