Abstract
This paper proposes a new distributed data structure based on binary trees to support k-nearest neighbor queries over very large databases. The indexing structure is distributed across a network of “peers”, where each one hosts a part of the tree and communication among nodes is realized by message passing. The advantages of this kind of approach are mainly two: it is possible to (i) handle a larger number of nodes and points than a single peer based architecture and (ii) to manage in an efficient way computation of multiple queries. In particular, we propose a novel version of the k-nearest neighbor algorithm that is able to start the query in a randomly chosen peer. Preliminary experiments have demonstrated that in about 65 % of cases a query, which starts in random node, does not involve the peer containing the root of the tree.
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Gargiulo, F., Amato, F., Moscato, V., Picariello, A., Sperli’, G. (2016). Nearest Query on Distributed Binary Trees Starting from a Random Node. In: Ngonga Ngomo, AC., Křemen, P. (eds) Knowledge Engineering and Semantic Web. KESW 2016. Communications in Computer and Information Science, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-45880-9_20
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DOI: https://doi.org/10.1007/978-3-319-45880-9_20
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