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A Novel Adaptive Cuckoo Search for Optimal Query Plan Generation
Joint Authors
Gomathi, Ramalingam
Sharmila, Dhandapani
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-14
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
The emergence of multiple web pages day by day leads to the development of the semantic web technology.
A World Wide Web Consortium (W3C) standard for storing semantic web data is the resource description framework (RDF).
To enhance the efficiency in the execution time for querying large RDF graphs, the evolving metaheuristic algorithms become an alternate to the traditional query optimization methods.
This paper focuses on the problem of query optimization of semantic web data.
An efficient algorithm called adaptive Cuckoo search (ACS) for querying and generating optimal query plan for large RDF graphs is designed in this research.
Experiments were conducted on different datasets with varying number of predicates.
The experimental results have exposed that the proposed approach has provided significant results in terms of query execution time.
The extent to which the algorithm is efficient is tested and the results are documented.
American Psychological Association (APA)
Gomathi, Ramalingam& Sharmila, Dhandapani. 2014. A Novel Adaptive Cuckoo Search for Optimal Query Plan Generation. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1050802
Modern Language Association (MLA)
Gomathi, Ramalingam& Sharmila, Dhandapani. A Novel Adaptive Cuckoo Search for Optimal Query Plan Generation. The Scientific World Journal No. 2014 (2014), pp.1-7.
https://search.emarefa.net/detail/BIM-1050802
American Medical Association (AMA)
Gomathi, Ramalingam& Sharmila, Dhandapani. A Novel Adaptive Cuckoo Search for Optimal Query Plan Generation. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-7.
https://search.emarefa.net/detail/BIM-1050802
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050802