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LargeRDFBench: A Billion Triples Benchmark for SPARQL Endpoint Federation

50 Pages Posted: 9 Jul 2018 First Look: Accepted

See all articles by Muhammad Saleem

Muhammad Saleem

University of Leipzig - Agile Knowledge Engineering and Semantic Web (AKSW)

Ali Hasnain

National University of Ireland, Galway (NUIG) - Insight Centre for Data Analytics

Axel-Cyrille Ngonga Ngomo

University of Leipzig - Agile Knowledge Engineering and Semantic Web (AKSW)

Abstract

Gathering information from the distributed Web of Data is commonly carried out by using SPARQL query federation approaches. However, the fitness of current SPARQL query federation approaches for real applications is difficult to evaluate with current benchmarks as they are either synthetic, too small in size and complexity or do not provide means for a fine-grained evaluation. We propose LargeRDFBench, a billion-triple benchmark for SPARQL query federation which encompasses real data as well as real queries pertaining to real bio-medical use cases. We evaluate state-of-the-art SPARQL endpoint federation approaches on this benchmark with respect to their query runtime, triple pattern-wise source selection, number of endpoints requests, and result completeness and correctness. Our evaluation results suggest that the performance of current SPARQL query federation systems on simple queries (in terms of total triple patterns, query result set sizes, execution time, use of SPARQL features etc.) does not reflect the systems' performance on more complex queries. Moreover, current federation systems seem unable to deal with real queries that involve processing large intermediate result sets or lead to large result sets.

Keywords: Benchmark, SPARQL, Federated Queries, Linked Data, RDF

Suggested Citation

Saleem, Muhammad and Hasnain, Ali and Ngonga Ngomo, Axel-Cyrille, LargeRDFBench: A Billion Triples Benchmark for SPARQL Endpoint Federation (2018). Journal of Web Semantics First Look. Available at SSRN: https://ssrn.com/abstract=3199316 or http://dx.doi.org/10.2139/ssrn.3199316

Muhammad Saleem (Contact Author)

University of Leipzig - Agile Knowledge Engineering and Semantic Web (AKSW) ( email )

Augustusplatz 10/11
Leipzig, 04109
Germany

Ali Hasnain

National University of Ireland, Galway (NUIG) - Insight Centre for Data Analytics ( email )

The DERI Building IDA Business Park
Galway
Ireland

Axel-Cyrille Ngonga Ngomo

University of Leipzig - Agile Knowledge Engineering and Semantic Web (AKSW) ( email )

Augustusplatz 10/11
Leipzig, 04109
Germany

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