A novel adaptive Cuckoo search for optimal query plan generation.

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,...

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Publicado en:Scientific World Journal pp. 727658 - 727659
Autores principales: Gomathi, Ramalingam, Sharmila, Dhandapani
Formato: Journal Article
Publicado: Wiley-Blackwell 2014
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 2014
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        atl: A novel adaptive Cuckoo search for optimal query plan generation.
      aug:
        au:
          Gomathi, Ramalingam
          Sharmila, Dhandapani
        affil: Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam 638401, India.
      sug:
        subj:
          Models, Theoretical
          Algorithms
      ab: 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.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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