Multidimensional access methods
ACM Computing Surveys (CSUR)
Multidimensional binary search trees used for associative searching
Communications of the ACM
ACM Computing Surveys (CSUR)
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
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
A compact space decomposition for effective metric indexing
Pattern Recognition Letters
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Multidimensional Binary Search Trees in Database Applications
IEEE Transactions on Software Engineering
Dynamic spatial approximation trees
Journal of Experimental Algorithmics (JEA)
DSACL+-tree: a dynamic data structure for similarity search in secondary memory
SISAP'12 Proceedings of the 5th international conference on Similarity Search and Applications
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The metric space model allows abstracting many similarity search problems. Similarity search has multiple applications especially in the multimedia databases area. The idea is to index the database so as to accelerate similarity queries. Although there are several promising indices, few of them are dynamic, i.e., once created very few allow to perform insertions and deletions of elements at a reasonable cost. The Dynamic Spatial Approximation Trees (DSA--trees) have shown to be a suitable data structure for searching high dimensional metric spaces or queries with low selectivity (i.e., large radius), and are also completely dynamic. The performance of DSA--trees is directly related to the amount of backtracking in search time. To boost the performance in this data structure a sufficient condition is to maintain in the nodes elements close-to-each-other. In this work we propose to obtain a new data structure for searching in metric spaces, based on the DSA--trees, which holds its virtues and takes advantage of element clusters, which are present in many metric spaces, and can also make better use of available memory to improve searches. In fact, we use these element clusters to improve the spatial approximation.