A Parallel Processing Architecture to Optimize Runtime in Aggregated SPARQL Queries

Author:

Rabhi Ahmed1,Fissoune Rachida1,Tabaa Mohamed2,Badir Hassan1

Affiliation:

1. Abdelmalek Essaadi University, Tangier, Morocco

2. EMSI Tanger, Morocco

Publisher

ACM

Reference17 articles.

1. Matteo Cossu , Michael Färber , and Georg Lausen . 2018 . Prost: Distributed execution of sparql queries using mixed partitioning strategies. arXiv preprint arXiv:1802.05898 (2018). Matteo Cossu, Michael Färber, and Georg Lausen. 2018. Prost: Distributed execution of sparql queries using mixed partitioning strategies. arXiv preprint arXiv:1802.05898 (2018).

2. Fan Feng , Weikang Zhou , Ding Zhang , and Jinhui Pang . 2020 . Highly Parallel SPARQL Engine for RDF. In International Conference of Pioneering Computer Scientists, Engineers and Educators. Springer, 61--71 . Fan Feng, Weikang Zhou, Ding Zhang, and Jinhui Pang. 2020. Highly Parallel SPARQL Engine for RDF. In International Conference of Pioneering Computer Scientists, Engineers and Educators. Springer, 61--71.

3. Olaf Görlitz and Steffen Staab . 2011 . SPLENDID: SPARQL endpoint federation exploiting VOID descriptions. COLD 782 (2011). Olaf Görlitz and Steffen Staab. 2011. SPLENDID: SPARQL endpoint federation exploiting VOID descriptions. COLD 782 (2011).

4. Evaluating SPARQL queries on massive RDF datasets

5. Data Partitioning Scheme for Efficient Distributed RDF Querying Using Apache Spark

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