A Scalable Nearest Neighbor Search in P2P Systems

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Authors

BATKO Michal GENNARO Claudio ZEZULA Pavel

Year of publication 2004
Type Article in Proceedings
Conference 2nd International VLDB Workshop on Databases, Information Systems and Peer-to-Peer Computing
MU Faculty or unit

Institute of Computer Science

Citation
Field Computer hardware and software
Keywords distributed data; scalable structures; similarity search; nearest neighbors
Description Similarity search in metric spaces represents an important paradigm for content-based retrieval in many applications. Existing centralized search structures can speed-up retrieval, but they do not scale up to large volume of data because the response time is linearly increasing with the size of the searched file. In this article, we study the problem of executing the nearest neighbor(s) queries in a distributed metric structure, which is based on the P2P communication paradigm and the generalized hyperplane partitioning. By exploiting parallelism in a dynamic network of computers, the query execution scales up very well considering both the number of distance computations and the hop count between the peers. Results are verified by experiments on real-life data sets.
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