RDF Query Path Optimization Using Hybrid Genetic Algorithms: Semantic Web vs. Data-Intensive Cloud Computing

Qazi Mudassar Ilyas, Muneer Ahmad, Sonia Rauf and Danish Irfan
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Qazi Mudassar Ilyas: King Faisal University, Saudi Arabia
Muneer Ahmad: School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad, Pakistan
Sonia Rauf: COMSATS University Islamabad, Abbottabad, Pakistan
Danish Irfan: COMSATS University Islamabad, Abbottabad, Pakistan

International Journal of Cloud Applications and Computing (IJCAC), 2022, vol. 12, issue 1, 1-16

Abstract: 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. We 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. We show that the overall number of joins for complex queries is reduced considerably, resulting in reduced cost.

Date: 2022
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