The BANG file: A new kind of grid file
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
Twin grid files: space optimizing access schemes
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
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
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
The hB-tree: a multiattribute indexing method with good guaranteed performance
ACM Transactions on Database Systems (TODS)
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Modern database systems
SIGMOD '95 Proceedings of the 1995 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
Multidimensional access methods
ACM Computing Surveys (CSUR)
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
Multidimensional binary search trees used for associative searching
Communications of the ACM
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
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
SP-GiST: An Extensible Database Index for Supporting Space Partitioning Trees
Journal of Intelligent Information Systems
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Spatial Searching in Geometric Databases
Proceedings of the Fourth International Conference on Data Engineering
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
VLDB '98 Proceedings of the 24rd 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
Multidimensional Access Methods: Trees Have Grown Everywhere
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The Universal B-Tree for Multidimensional Indexing: general Concepts
WWCA '97 Proceedings of the International Conference on Worldwide Computing and Its Applications
Distributed computation of the knn graph for large high-dimensional point sets
Journal of Parallel and Distributed Computing
Towards personal high-performance geospatial computing (HPC-G): perspectives and a case study
Proceedings of the ACM SIGSPATIAL International Workshop on High Performance and Distributed Geographic 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
Hi-index | 0.01 |
Multidimensional indexing is concerned with the indexing of multi-attributed records, where queries can be applied on some or all of the attributes. Indexing multi-attributed records is referred to by the term multidimensional indexing because each record is viewed as a point in a multidimensional space with a number of dimensions that is equal to the number of attributes. The values of the point coordinates along each dimension are equivalent to the values of the corresponding attributes. In this paper, the PN-tree, a new index structure for multidimensional spaces, is presented. This index structure is an efficient structure for indexing multidimensional points and is parallel by nature. Moreover, the proposed index structure does not lose its efficiency if it is serially processed or if it is processed using a small number of processors. The PN-tree can take advantage of as many processors as the dimensionality of the space. The PN-tree makes use of B+-trees that have been developed and tested over years in many DBMSs. The PN-tree is compared to the Hybrid tree that is known for its superiority among various index structures. Experimental results show that parallel processing of the PN-tree reduces significantly the number of disk accesses involved in the search operation. Even in its serial case, the PN-tree outperforms the Hybrid tree for large database sizes.