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
Topological relations in the world of minimum bounding rectangles: a study with R-trees
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A model for the prediction of R-tree performance
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Node splitting algorithms in tree-structured high-dimensional indexes for similarity search
Proceedings of the 2002 ACM symposium on Applied computing
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
New Linear Node Splitting Algorithm for R-trees
SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
On using B+-tree for efficient processing for the boundary neighborhood problem
WSEAS TRANSACTIONS on SYSTEMS
A new enhancement to the R-tree node splitting
Journal of Information Science
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We introduce an improved method to split overflowed nodes of R-tree spatial index called the Corner Based Splitting (CBS) algorithm. Good splits produce an efficient R-tree which has minimal height, overlap and coverage in each node. The CBS algorithm selects the splitting axis that produces the most even split according to the number of objects, using the distance from each object centre to the nearest node's MBR corner. Experiments performed using both synthetic and real data files showed obvious performance improvement. The improvement percentage over the Quad algorithm reached 23%, while the improvement percentage over the NR algorithm reached 37%.