Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A cost model for similarity queries in metric spaces
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Indexing large metric spaces for similarity search queries
ACM Transactions on Database Systems (TODS)
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
VLDB '94 Proceedings of the 20th 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
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The problem of defining and computing proximity of regions constraining objects from generic metric spaces is investigated. Approximate, computationally fast, approach is developed for pairs of metric ball regions, which covers the needs of current systems for processing data through distances. The validity and precision of proposed solution is verified by extensive simulation on three substantially diffierent data files. The precision of obtained results is very satisfactory. Besides other possibilities, the proximity measure can be applied to improve the performance of metric trees, developed for multimedia similarity search indexing. Specific system areas concern splitting and merging of regions, pruning regions during similarity retrieval, ranking regions for best case matching, and declustering regions to achieve parallelism.