Extending largeRDFBench for multi-source data at scale for SPARQL endpoint federation
Hasnain, Ali Saleem, Muhammad Ngomo, Axel-Cyrille Ngonga Rebholz-Schuhmann, Dietrich
Hasnain, Ali
Saleem, Muhammad
Ngomo, Axel-Cyrille Ngonga
Rebholz-Schuhmann, Dietrich
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Publication Date
2018
Type
Conference Paper
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Citation
Hasnain, Ali, Saleem, Muhammad, Ngomo, Axel-Cyrille Ngonga, & Rebholz-Schuhmann, Dietrich. (2018). Extending LargeRDFBench for multi-source data at scale for SPARQL endpoint federation. Paper presented at the 17th International Semantic Web Conference 2018 (ISWC2018), Monterey, California, USA, 08-12 October, in Studies on the Semantic Web, Volume 36: Emerging Topics in Semantic Technologies. doi: 10.3233/978-1-61499-894-5-203
Abstract
Querying the Web of Data is highly motivated by the use of federation approaches mainly SPARQL query federation when the data is available through endpoints. Different benchmarks have been proposed to exploit the full potential of SPARQL query federation approaches in real world scenarios with their limitations in size and complexity. Previously, we introduced LargeRDFBench – a billion-triple benchmark for SPARQL query federation. In this work, we pinpoint some of of the limitation of LargeRDFBench and propose an extension with 8 additional queries. Our evaluation results of the state-of-the-art federation engines revealed interesting insights, when tested on these additional queries
Publisher
IOS Press
Publisher DOI
10.3233/978-1-61499-894-5-203
Rights
Attribution-NonCommercial-NoDerivs 3.0 Ireland