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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15914
Title: DBM*-Tree: An efficient metric acces method
Authors: Ocsa, Alexander
Cuadros Vargas, Ernesto
Keywords: Algorithms;Cost functions;Data reduction;Information retrieval;Measurement theory;Query processing;Metric access methods;Multidimensional index;Similarity information retrieval;Similarity queries;Trees (mathematics)
Issue Date: 2007
Publisher: Scopus
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-34248384446&doi=10.1145%2f1233341.1233414&partnerID=40&md5=7022fb2e97ba4e28b96bb7593f347dc1
Abstract: In this paper we propose a new dynamic Metric Access Method (MAM) called DBM*-Tree, which uses precomputed distances to reduce the construction cost avoiding repeated calculus of distance. Making use of the pre-calculated distances cost of similarity queries are also reduced by taking various local representative objects in order to increment the pruning of irrelevant elements during the query. We also propose a new algorithm to select the suitable subtree in the insertion operation, which is an evolution of the previous methods. Empiric tests on real and synthetic data have shown evidence that DBM*-Tree requires 25 % less average distance computing than Density Based Metric Tree (DBM-Tree) which is one of the most efficient and recent MAM found in the literature. © Copyright 2007 ACM.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15914
ISBN: urn:isbn:9781595936295
Appears in Collections:Artículos - Ciencia de la computación

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