Abstract
In this paper, we propose a fully decentralized framework called iDISQUE to support tunable approximate similarity query of high dimensional data in DHT networks. The iDISQUE framework utilizes a distributed indexing scheme to organize data summary structures called iDisques, which describe the cluster information of the data on each peer. The publishing process of iDisques employs a locality-preserving mapping scheme. Approximate similarity queries can be resolved using the distributed index. The accuracy of query results can be tuned both with the publishing and query costs. We employ a multi-probe technique to reduce the index size without compromising the effectiveness of queries. We also propose an effective load-balancing technique based on multi-probing. Experiments on real and synthetic datasets confirm the effectiveness and efficiency of iDISQUE.
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Zhang, X., Shou, L., Tan, KL., Chen, G. (2010). iDISQUE: Tuning High-Dimensional Similarity Queries in DHT Networks. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds) Database Systems for Advanced Applications. DASFAA 2010. Lecture Notes in Computer Science, vol 5981. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12026-8_4
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DOI: https://doi.org/10.1007/978-3-642-12026-8_4
Publisher Name: Springer, Berlin, Heidelberg
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