The Berlin SPARQL Benchmark

By Christian Bizer, Andreas Schultz. International Journal on Semantic Web and Information Systems (IJSWIS), 2009

750 citations recorded by the source corpus.

The SPARQL Query Language for RDF and the SPARQL Protocol for RDF are implemented by a growing number of storage systems and are used within enterprise and open Web settings. As SPARQL is taken up by the community, there is a growing need for benchmarks to compare the performance of storage systems that expose SPARQL endpoints via the SPARQL protocol. Such systems include native RDF stores as well as systems that rewrite SPARQL queries to SQL queries against non-RDF relational databases. This article introduces the Berlin SPARQL Benchmark (BSBM) for comparing the performance of native RDF stores with the performance of SPARQL-to-SQL rewriters across architectures. The benchmark is built around an e-commerce use case in which a set of products is offered by different vendors and consumers have posted reviews about products. The benchmark query mix emulates the search and navigation pattern of a consumer looking for a product. The article discusses the design of the BSBM benchmark and presents the results of a benchmark experiment comparing the performance of four popular RDF stores (Sesame, Virtuoso, Jena TDB, and Jena SDB) with the performance of two SPARQL-to-SQL rewriters (D2R Server and Virtuoso RDF Views) as well as the performance of two relational database management systems (MySQL and Virtuoso RDBMS).

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/0efcd1d38ad020da7c01613b7818eb123cb34121

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Citations in the corpus

Linked Data: Evolving the Web into a Global Data Space (2011, background): “Detailed benchmark results for the SPARQL query performance of different RDF stores are provided on the Berlin SPARQL Benchmark [33] Web site223 as well as in the ESW wiki224.”
Triple Pattern Fragments: A low-cost knowledge graph interface for the Web (2016, methodology): “As a benchmark for the experiment, we chose the Berlin SPARQL Benchmark (BSBM) [56], for the following reasons:”
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.”
SP^2Bench: A SPARQL Performance Benchmark (2008, background): “Figure 2(a) exempliﬁes our analysis for attribute cite .”
TriAL (2018, background): “Most well known SPARQL benchmarks do not include queries with property paths (see, e.g., LUBM [30] or BSBM [13]).”
DBpedia SPARQL Benchmark - Performance Assessment with Real Queries on Real Data (2011, methodology): “However, BSBM data and queries are artificial and the data schema is very homogeneous and resembles a relational database.”
Diversified Stress Testing of RDF Data Management Systems (2014, methodology): “2c. BSBM contains queries that are a little bit more diverse in their join vertex degrees, but it does not test the two extremes as SP2Bench does.”
Knowledge-based Systems (2009, neutral): “The comparison between the two case studies shows that the regions characterising the space of ethical principles depend on the domain and the data.”
LinkedGeoData: A core for a web of spatial open data (2012, methodology): “We chose Virtuoso as the backend for LinkedGeoData because of its good performance [2] and our assumption that point geometries would be sufficient for most of our interlinking tasks.”