SP2Bench: A SPARQL Performance Benchmark

Schmidt, Michaël; Hornung, Thomas; Meier, Michael; Pinkel, Christoph; Lausen, Georg

doi:10.1007/978-3-642-04329-1_16

Cited by 155 publications

(145 citation statements)

References 24 publications

Supporting

Mentioning

140

Contrasting

Unclassified

Order By: Relevance

“…We did not exclude the queries completely from the overall assessment, since this would have affected a large number of the queries and adversely penalized stores, which complete queries within the time frame. We penalized failure queries with 180s, similar to what was done in the SP2-Benchmark [17]. Virtuoso was the only store, which completed all queries in time.…”

Section: Resultsmentioning

confidence: 99%

“…SPARQL is based on powerful graph matching allowing to bind variables to fragments in the input RDF graph. In addition, operators akin to the relational joins, unions, left outer joins, selections and projections can be used to build more expressive queries [17]. It is evident that the performance of triple stores offering a SPARQL query interface is mission critical for individual projects as well as for data integration on the Web in general.…”

Section: Introductionmentioning

confidence: 99%

“…Existing SPARQL benchmark efforts such as LUBM [15], BSBM [4] and SP 2 [17] resemble relational database benchmarks. Especially the data structures underlying these benchmarks are basically relational data structures, with relatively few and homogeneously structured classes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

DBpedia SPARQL Benchmark – Performance Assessment with Real Queries on Real Data

Morsey

Lehmann

Auer

et al. 2011

The Semantic Web – ISWC 2011

184

165

View full text Add to dashboard Cite

Abstract. Triple stores are the backbone of increasingly many Data Web applications. It is thus evident that the performance of those stores is mission critical for individual projects as well as for data integration on the Data Web in general. Consequently, it is of central importance during the implementation of any of these applications to have a clear picture of the weaknesses and strengths of current triple store implementations. In this paper, we propose a generic SPARQL benchmark creation procedure, which we apply to the DBpedia knowledge base. Previous approaches often compared relational and triple stores and, thus, settled on measuring performance against a relational database which had been converted to RDF by using SQL-like queries. In contrast to those approaches, our benchmark is based on queries that were actually issued by humans and applications against existing RDF data not resembling a relational schema. Our generic procedure for benchmark creation is based on query-log mining, clustering and SPARQL feature analysis. We argue that a pure SPARQL benchmark is more useful to compare existing triple stores and provide results for the popular triple store implementations Virtuoso, Sesame, Jena-TDB, and BigOWLIM. The subsequent comparison of our results with other benchmark results indicates that the performance of triple stores is by far less homogeneous than suggested by previous benchmarks.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

DBpedia SPARQL Benchmark – Performance Assessment with Real Queries on Real Data

Morsey

Lehmann

Auer

et al. 2011

The Semantic Web – ISWC 2011

184

165

View full text Add to dashboard Cite

show abstract

“…In the case of Serql, we use the MINUS operator of the language. [19] data generator to obtain the input RDF graphs. We implemented our approach on top of Jena v2.6.2 using the Java engine ARQ v2.8.2, SDB v1.3.1 which link ARQ to an SQL database back-end (Postgresql v8.4), the Java implementation Sesame v2.3.1 [3,7] and a relational database (Postgresql v8.4).…”

Section: Access Control Enforcementmentioning

confidence: 99%

Controlling Access to RDF Graphs

Flouris

Fundulaki

Michou

et al. 2010

Future Internet - FIS 2010

View full text Add to dashboard Cite

Abstract. One of the current barriers towards realizing the huge potential of Future Internet is the protection of sensitive information, i.e., the ability to selectively expose (or hide) information to (from) users depending on their access privileges. Given that RDF has established itself as the de facto standard for data representation over the Web, our work focuses on controlling access to RDF data. We present a high-level access control specification language that allows fine-grained specification of access control permissions (at triple level) and formally define its semantics. We adopt an annotation-based enforcement model, where a user can explicitly associate data items with annotations specifying whether the item is accessible or not. In addition, we discuss the implementation of our framework, propose a set of dimensions that should be considered when defining a benchmark to evaluate the different access control enforcement models and present the results of our experiments conducted on different Semantic Web platforms.

show abstract

“…Among the features evaluated by these testbeds we can cite: i) functional requirements supported, ii) efficiency of the implementations with different configurations of datasets and with different types of queries, or iii) resilience to changes in the configurations of these systems and the underlying datasets. The most recent and complete testbed is FedBench [8], which proposes a variety of queries in different domains and with different characteristics, including star-shaped, chain-like and hybrid queries, and complex query forms using an adaptation of SP2Bench [9]. These testbeds are steps forward towards establishing a continuous benchmarking process of federated SPARQL query engines.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Federated SPARQL Query Engines: Are Existing Testbeds Enough?

Montoya

Vidal

Corcho

et al. 2012

The Semantic Web – ISWC 2012

View full text Add to dashboard Cite

Abstract. Testbeds proposed so far to evaluate, compare, and eventually improve SPARQL query federation systems have still some limitations. Some variables and configurations that may have an impact on the behavior of these systems (e.g., network latency, data partitioning and query properties) are not sufficiently defined; this affects the results and repeatability of independent evaluation studies, and hence the insights that can be obtained from them. In this paper we evaluate FedBench, the most comprehensive testbed up to now, and empirically probe the need of considering additional dimensions and variables. The evaluation has been conducted on three SPARQL query federation systems, and the analysis of these results has allowed to uncover properties of these systems that would normally be hidden with the original testbeds.

show abstract

SP2Bench: A SPARQL Performance Benchmark

Cited by 155 publications

References 24 publications

DBpedia SPARQL Benchmark – Performance Assessment with Real Queries on Real Data

DBpedia SPARQL Benchmark – Performance Assessment with Real Queries on Real Data

Controlling Access to RDF Graphs

Benchmarking Federated SPARQL Query Engines: Are Existing Testbeds Enough?

Contact Info

Product

Resources

About