Performance evaluation of grid based multi-attribute record declustering methods

Bhaskar Himatsingka, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In this study we focus on multi-attribute declustering methods which are based on some type of grid-based partitioning of the data space. Theoretical results are derived which show that no declustering method can be strictly optimal for range queries if the number of disks is greater than 5. A detailed performance evaluation is carried out to see how various declustering schemes perform under a wide range of query and database scenarios (both relative to each other and to the optimal). Parameters that are varied include shape and size of queries, database size, number of attributes and the number of disks. The results show that information about common queries on a relation is very important and ought to be used in deciding the declustering for it, and that this is especially crucial for small queries. Also, there is no clear winner, and as such parallel database systems must support a number of declustering methods.

Original languageEnglish
Title of host publicationProceedings - International Conference on Data Engineering
Editors Anon
PublisherPubl by IEEE
Pages356-365
Number of pages10
ISBN (Print)0818654007
Publication statusPublished - 1 Jan 1994
EventProceedings of the 10th International Conference on Data Engineering - Houston, TX, USA
Duration: 14 Feb 199418 Feb 1994

Publication series

NameProceedings - International Conference on Data Engineering

Other

OtherProceedings of the 10th International Conference on Data Engineering
CityHouston, TX, USA
Period14/2/9418/2/94

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Cite this

Himatsingka, B., & Srivastava, J. (1994). Performance evaluation of grid based multi-attribute record declustering methods. In Anon (Ed.), Proceedings - International Conference on Data Engineering (pp. 356-365). (Proceedings - International Conference on Data Engineering). Publ by IEEE.