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
Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 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
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
A space-partitioning-based indexing method for multidimensional non-ordered discrete data spaces
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Database Systems (TODS)
Unified framework for fast exact and approximate search in dissimilarity spaces
ACM Transactions on Database Systems (TODS)
New dynamic construction techniques for M-tree
Journal of Discrete Algorithms
Improving the performance of M-tree family by nearest-neighbor graphs
ADBIS'07 Proceedings of the 11th East European conference on Advances in databases and information systems
Efficient k-nearest neighbor searches for parallel multidimensional index structures
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
BM+-Tree: a hyperplane-based index method for high-dimensional metric spaces
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
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In this paper, we propose a new metric index, called M+ -tree, which is a tree dynamically organized for large datasets in metric spaces. The proposed M+-tree takes full advantages of M-tree and MVP-tree, with a new concept called key dimension, which effectively reduces response time for similarity search. The main idea behind the key dimension is to make the fanout of tree larger by partitioning a subspace further into two subspaces, called twin-nodes. We can double the filtering effectiveness by utilizing the twin-nodes. In addition, for the purpose of ensuring high space utilization, we also conduct data reallocation between the twin nodes dynamically. Our experiment shows that higher filtering efficiency can be obtained by using the key dimensions for r-neighbor search and k-NN (k-nearest neighbor). We will report our experimental results in this paper.