Multidimensional access methods
ACM Computing Surveys (CSUR)
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
ACM Computing Surveys (CSUR)
Some approaches to best-match file searching
Communications of the ACM
ACM Computing Surveys (CSUR)
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Fast Indexing and Visualization of Metric Data Sets using Slim-Trees
IEEE Transactions on Knowledge and Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
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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.