Abstract
The finest way of semantic Web representation for further indexing and querying becomes robust due to the RDF structure. The magnified growth in semantic Web data, RDF query processing, emerged as complex due to numerous joins. Henceforth, to achieve scalability and robustness toward search space and time, the query joins must be optimized. The majority of existing benchmarking models have been evaluated on a single source. These methods utterly failed to optimize the queries with nested loop join, bind join, and AGJoin, which is due to the search cost only being considered as the optimization factor by all of the existing models. Hence to optimize the distribute chain queries, here in this paper we propose a novel evolutionary approach, which is based on progressive genetic evolutions to identify the optimized chain queries even for distributed triple stores. The experimental results show that the significance of the proposed model over the existing evolutionary approaches is optimal. Also, it is obvious to confirm that the metrics and evolution process introduced here in this paper motivates future research to identify the new dimensions of chain query optimization to search in distributed triple stores.
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Shailaja, K., Kumar, P.V., Durga Bhavani, S. (2017). Progressive Genetic Evolutions-Based Join Cost Optimization (PGE-JCO) for Distributed RDF Chain Queries. In: Satapathy, S., Prasad, V., Rani, B., Udgata, S., Raju, K. (eds) Proceedings of the First International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 507. Springer, Singapore. https://doi.org/10.1007/978-981-10-2471-9_23
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DOI: https://doi.org/10.1007/978-981-10-2471-9_23
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