The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Fast parallel similarity search in multimedia databases
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on 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
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Efficient Processing of Nearest Neighbor Queries in Parallel Multimedia Databases
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Empirical evaluation of excluded middle vantage point forest on biological sequences workload
Proceedings of the 1st Workshop on New Trends in Similarity Search
A GPU-Based Implementation for Range Queries on Spaghettis Data Structure
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part I
Large-scale similarity-based join processing in multimedia databases
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
A data allocation method for efficient content-based retrieval in parallel multimedia databases
ISPA'07 Proceedings of the 2007 international conference on Frontiers of High Performance Computing and Networking
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In this study, parallel implementation of M-tree to index high dimensional metric space has been elaborated and an optimal declustering technique has been proposed. First, we have defined the optimal declustering and developed an algorithm based on this definition. Proposed declustering algorithm considers both object proximity and data load on disk/processors by executing a k-NN or a range query for each newly inserted objects. We have tested our algorithm in a database containing randomly chosen 1000 image's color histograms with 32 bins in HSV color space. Experimentation showed that our algorithm produces a very near optimal declustering.