SP^2Bench: A SPARQL Performance Benchmark

By Michael Schmidt, Thomas Hornung, Georg Lausen, Christoph Pinkel. IEEE International Conference on Data Engineering, 2008

518 citations recorded by the source corpus.

Recently, the SPARQL query language for RDF has reached the W3C recommendation status. In response to this emerging standard, the database community is currently exploring efficient storage techniques for RDF data and evaluation strategies for SPARQL queries. A meaningful analysis and comparison of these approaches necessitates a comprehensive and universal benchmark platform. To this end, we have developed SP^2Bench, a publicly available, language-specific SPARQL performance benchmark. SP^2Bench is settled in the DBLP scenario and comprises both a data generator for creating arbitrarily large DBLP-like documents and a set of carefully designed benchmark queries. The generated documents mirror key characteristics and social-world distributions encountered in the original DBLP data set, while the queries implement meaningful requests on top of this data, covering a variety of SPARQL operator constellationsand RDF access patterns. As a proof of concept, we apply SP^2Bench to existing engines and discuss their strengths and weaknesses that follow immediately from the benchmark results

Subfields: Artificial Intelligence, Computer Networks and Communications, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/837f4a2ce9ad7f584615ffa73303f05cc151400f

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Citations in the corpus

Ontology Alignment Evaluation Initiative: Six Years of Experience (2011, methodology): “To this end, a number of benchmark datasets for evaluating the performance of RDF databases was defined in terms of generators that can be used to generate arbitrarily large RDF datasets based on a predefined schema [33; 6; 55].”
Diversified Stress Testing of RDF Data Management Systems (2014, background): “– SP(2)Bench [20] tests various SPARQL features such as union and optional graph patterns.”
DBpedia SPARQL Benchmark - Performance Assessment with Real Queries on Real Data (2011, methodology): “We penalized failure queries with 180s, similar to what was done in the SP2-Benchmark [18].”
FedX: Optimization Techniques for Federated Query Processing on Linked Data (2011, methodology): “As a basis for our evaluation we use FedBench(8) [17], a comprehensive benchmark suite, which in contrast to other SPARQL benchmarks [4, 18] focuses on analyzing the efficiency and effectiveness of federated query processing strategies over semantic data.”
A Native and Adaptive Approach for Unified Processing of Linked Streams and Linked Data (2011, methodology): “The data generator from SP2Bench [30] is used to create simulated DBLP datasets.”