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Towards a Scalable Semantic-Based Distributed Approach for SPARQL Query Evaluation

: Sejdiu, G.; Graux, D.; Khan, I.; Lytra, I.; Jabeen, H.; Lehmann, J.

Volltext ()

Acosta, M.:
Semantic Systems. The Power of AI and Knowledge Graphs. Proceedings : 15th International Conference, SEMANTiCS 2019, Karlsruhe, Germany, September 9-12, 2019
Cham: Springer Nature, 2019 (Lecture Notes in Computer Science 11702)
ISBN: 978-3-030-33219-8 (Print)
ISBN: 978-3-030-33220-4 (Online)
International Conference on Semantic Systems (SEMANTiCS) <15, 2019, Karlsruhe>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()

Over the last two decades, the amount of data which has been created, published and managed using Semantic Web standards and especially via Resource Description Framework (RDF) has been increasing. As a result, efficient processing of such big RDF datasets has become challenging. Indeed, these processes require, both efficient storage strategies and query-processing engines, to be able to scale in terms of data size. In this study, we propose a scalable approach to evaluate SPARQL queries over distributed RDF datasets using a semantic-based partition and is implemented inside the state-of-the-art RDF processing framework: SANSA. An evaluation of the performance of our approach in processing large-scale RDF datasets is also presented. The preliminary results of the conducted experiments show that our approach can scale horizontally and perform well as compared with the previous Hadoop-based system. It is also comparable with the in-memory SPARQL query evaluators when there is less shuffling involved.