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

2Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

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.

Cite

CITATION STYLE

APA

Sejdiu, G., Graux, D., Khan, I., Lytra, I., Jabeen, H., & Lehmann, J. (2019). Towards a Scalable Semantic-Based Distributed Approach for SPARQL Query Evaluation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11702 LNCS, pp. 295–309). Springer. https://doi.org/10.1007/978-3-030-33220-4_22

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free