RDF query path optimization using hybrid genetic algorithms: Semantic web vs. data-intensive cloud computing

Ilyas, Qazi Mudassar and Ahmad, Muneer and Rauf, Sonia and Irfan, Danish (2022) RDF query path optimization using hybrid genetic algorithms: Semantic web vs. data-intensive cloud computing. International Journal of Cloud Applications and Computing, 12 (1). DOI https://doi.org/10.4018/IJCAC.2022010101.

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Resource description framework (RDF) inherently supports data mergers from various resources into a single federated graph that can become very large even for an application of modest size. This results in severe performance degradation in the execution of RDF queries. As every RDF query essentially traverses a graph to find the output of the query, an efficient path traversal reduces the execution time of RDF queries. Hence, query path optimization is required to reduce the execution time as well as the cost of a query. Query path optimization is an NP-hard problem that cannot be solved in polynomial time. Genetic algorithms have proven to be very useful in optimization problems. The authors propose a hybrid genetic algorithm for query path optimization. The proposed algorithm selects an initial population using iterative improvement, thus reducing the initial solution space for the genetic algorithm. The proposed algorithm makes significant improvements in the overall performance. They show that the overall number of joins for complex queries is reduced considerably, resulting in reduced cost. Copyright © 2022, IGI Global.

Item Type: Article
Funders: None
Uncontrolled Keywords: Cloud Computing; Genetic Algorithm; Information Retrieval; Query Path Optimization; Resource Description Framework; SPARQL
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 04 Oct 2023 06:47
Last Modified: 04 Oct 2023 06:47
URI: http://eprints.um.edu.my/id/eprint/43176

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