Distributed Query Plan Generation using Particle Swarm Optimization

T.V. Vijay Kumar, Amit Kumar and Rahul Singh
Additional contact information
T.V. Vijay Kumar: School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India
Amit Kumar: School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India
Rahul Singh: School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India

International Journal of Swarm Intelligence Research (IJSIR), 2013, vol. 4, issue 3, 58-82

Abstract: A large number of queries are posed on databases spread across the globe. In order to process these queries efficiently, optimal query processing strategies that generate efficient query processing plans are being devised. In distributed relational database systems, due to replication of relations at multiple sites, the relations required to answer a query may necessitate accessing of data from multiple sites. This leads to an exponential increase in the number of possible alternative query plans for processing a query. Though it is not computationally feasible to explore all possible query plans in such a large search space, the query plan that provides the most cost-effective option for query processing is considered necessary and should be generated for a given query. In this paper, an attempt has been made to generate such optimal query plans using Set based Comprehensive Learning Particle Swarm Optimization (S-CLPSO). Experimental comparisons of this algorithm with the GA based distributed query plan generation algorithm shows that for higher number of relations, the S-CLPSO based algorithm is able to generate comparatively better quality Top-K query plans.

Date: 2013
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/ijsir.2013070104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jsir00:v:4:y:2013:i:3:p:58-82

Access Statistics for this article

International Journal of Swarm Intelligence Research (IJSIR) is currently edited by Yuhui Shi

More articles in International Journal of Swarm Intelligence Research (IJSIR) from IGI Global
Bibliographic data for series maintained by Journal Editor ().