The LSD tree: spatial access to multidimensional and non-point objects
VLDB '89 Proceedings of the 15th international conference on Very large data bases
The buddy tree: an efficient and robust access method for spatial data base
Proceedings of the sixteenth international conference on Very large databases
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
A general solution of the n-dimensional B-tree problem
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
Efficient indexing of high-dimensional data through dimensionality reduction
Data & Knowledge Engineering
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
A retrieval technique for high-dimensional data and partially specified queries
Data & Knowledge Engineering
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
An Implementation and Performance Analysis of Spatial Data Access Methods
Proceedings of the Fifth International Conference on Data Engineering
Implementing KDB-Trees to Support High-Dimensional Data
IDEAS '01 Proceedings of the International Database Engineering & Applications Symposium
KDBKD-Tree: A Compact KDB-Tree Structure for Indexing Multidimensional Data
ITCC '03 Proceedings of the International Conference on Information Technology: Computers and Communications
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Storing and querying high-dimensional data are important problems in designing an information retrieval system. Two crucial issues, time and space efficiencies, must be considered when evaluating the performance of such a system. The KDB-tree and its variants have been reported to have good performance by using them as the index structure for retrieving multidimensional data. However, they all suffer from low storage utilization problem caused by imperfect ''splitting policies.'' Unnecessary splits increase the size of the index structure and deteriorate the performance of the system. In this paper, a new data insertion algorithm with a better splitting policy was proposed, which arranges data entries in the leaf nodes as many as possible. Our new index scheme can increase the storage utilization up to nearly 100% and reduce the index size to a smaller scale. As a result, both time and space efficiencies are significantly improved. Analytical and experimental results show that our indexing method outperforms the traditional KDB-tree and its variants.