Optimal partial-match retrieval when fields are independently specified
ACM Transactions on Database Systems (TODS)
System R: relational approach to database management
ACM Transactions on Database Systems (TODS)
Hashing and trie algorithms for partial match retrieval
ACM Transactions on Database Systems (TODS)
Attribute based file organization in a paged memory environment
Communications of the ACM
Multidimensional binary search trees used for associative searching
Communications of the ACM
Join processing in relational databases
ACM Computing Surveys (CSUR)
Multikey retrieval from K-d trees and QUAD-trees
SIGMOD '85 Proceedings of the 1985 ACM SIGMOD international conference on Management of data
Query Processing for Multi-Attribute Clustered Records
VLDB '90 Proceedings of the 16th International Conference on Very Large Data Bases
Efficient Search of Multi-Dimensional B-Trees
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
| Hi-index | 0.00 |
In this study, a technique of performing multiple attribute clustering in dynamic databases has been investigated. We have transformed the problem of performing multiple attribute clustering into a problem of dynamically partitioning the attribute space. The optimal number of partitioning of the attribute space in a dynamic database environment has been analyzed, the partitioning direction is controlled by a discriminator sequence. The design of the discriminator sequence to obtain the optimal partitioning is presented. The selection of the directory attributes has also been discussed. Using the extended K-d tree to direct the partitioning, we have presented the extended K-d tree method. Empirical results have justified the improvement of the performance using the extended K-d tree method, when compared with that using the single attribute clustering or using inverted file method without any clustering index.