Academic Editor: Youssef EL FOUTAYENI
Received |
Accepted |
Published |
31 January 2020 |
15 February 2020 |
10 March 2020 |
Abstract: RDF (Resource Description Framework) is a graph model for representing information on the Web, while SPARQL (Protocol and RDF Query Language) is a query language for manipulating RDF data, but the latter generates a problem on the difficulty of executing complex queries when there is a big volume of RDF data, to solve this problem, different approaches [1-5] are intervening, to improve the queries of RDF data, in an optimized way with a minimal response time. In this survey, we provide an extensive literature on the capacity of existing approaches in a Big Data world.References[1]M. Hassan and S. K. Bansal. Semantic Data Querying ove NoSQL Databases with Apache Spark. InInternational Conference on Information Reuse and Integration for Data Science (IRI), Salt Lake City, USA,(2018) 364–371.[2]Z. Xu, W. Chen, L. Gai, and T. Wang. Sparkrdf: In-memory distributed rdf management framework for largescale social data, In International Conference on Web-Age Information Management, (2015), 337–349.[3] A. Schätzle, M. Przyjaciel-Zablocki, T. Hornung,, G. Lausen. PigSPARQL: A SPARQL query processing baselinefor big data. In Proceedings of the ISWC 2013 Posters & Demonstrations Track. (2013) 241–244.[4] L. Cheng, S. Kotoulas, Scale-out processing of large RDF datasets, IEEE Trans. Big Data 1, 4 (2015) 138-150.[5] JH. Du, HF. Wang, Y. Ni, Y. Yu , HadoopRDF: A Scalable Semantic Data Analytical Engine, Appl. Math. Sci., 4Intelligent Computing Theories and Applications. ICIC 2012. Lecture Notes in Computer Science, vol 7390.Springer, Berlin, Heidelberg (2012) 633-641
