RDF Support in the Virtuoso DBMS

Erling, Orri; Mikhailov, Ivan

doi:10.1007/978-3-642-02184-8_2

Cited by 291 publications

(220 citation statements)

References 3 publications

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“…We first describe the triple stores and their configuration, followed by our experimental strategy and finally the obtained results. All experiments were conducted on a typical server machine with an AMD Opteron Triple Stores Setup We carried out our experiments by using the triple stores Virtuoso [6], Sesame [5], Jena-TDB [14], and BigOWLIM [3]. The configuration and the version of each triple store were as follows:…”

Section: Methodsmentioning

confidence: 99%

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

Morsey

Lehmann

Auer

et al. 2011

The Semantic Web – ISWC 2011

184

165

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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.

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Section: Methodsmentioning

confidence: 99%

“…We apply the DBPSB to assess the performance and scalability of the popular triple stores Virtuoso [6], Sesame [5], Jena-TDB [14], and BigOWLIM [3] and compare our results with those obtained with previous benchmarks. Our experiments reveal that the performance and scalability is by far less homogeneous than other benchmarks indicate.…”

Section: Introductionmentioning

confidence: 99%

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

Morsey

Lehmann

Auer

et al. 2011

The Semantic Web – ISWC 2011

184

165

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show abstract

“…Furthermore we assume that the closure of the KB is stored. However, we should note that Virtuoso [3], supports an extended SPARQL version with subclassOf and subproperty inference at query level. This means that triples entailed by subclass or subproperty statements are not physically stored, but they are added to the result set during query answering 5 .…”

Section: N∈sementioning

confidence: 99%

Interactive Exploration of Fuzzy RDF Knowledge Bases

Manolis

Tzitzikas

2011

Lecture Notes in Computer Science

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Abstract. In several domains we have objects whose descriptions are accompanied by a degree expressing their strength. Such degrees can have various application specific semantics, such as relevance, precision, certainty, trust, etc. In this paper we consider Fuzzy RDF as the representation framework for such "weighted" descriptions and associations, and we propose a novel model for browsing and exploring such sources, which allows formulating complex queries gradually and through plain clicks. Specifically, and in order to exploit the fuzzy degrees, the model proposes interval-based transition markers. The advantage of the model is that it significantly increases the discrimination power of the interaction, without making it complex for the end user.

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“…While there exist efficient solutions to query processing in the context of RDF for local, centralized repositories [7,5], research contributions and frameworks for distributed, federated query processing are still in the early stages. In practical terms the Sesame framework in conjunction with AliBaba 1 is one possible sample solution allowing for federations of distributed repositories and endpoints.…”

Section: Introductionmentioning

confidence: 99%

FedX: A Federation Layer for Distributed Query Processing on Linked Open Data

Schwarte

Haase

Hose

et al. 2011

The Semanic Web: Research and Applications

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Abstract. Driven by the success of the Linked Open Data initiative today's Semantic Web is best characterized as a Web of interlinked datasets. Hand in hand with this structure new challenges to query processing are arising. Especially queries for which more than one data source can contribute results require advanced optimization and evaluation approaches, the major challenge lying in the nature of distribution: Heterogenous data sources have to be integrated into a federation to globally appear as a single repository. On the query level, though, techniques have to be developed to meet the requirements of efficient query computation in the distributed setting. We present FedX, a project which extends the Sesame Framework with a federation layer that enables efficient query processing on distributed Linked Open Data sources. We discuss key insights to its architecture and summarize our optimization techniques for the federated setting. The practicability of our system will be demonstrated in various scenarios using the Information Workbench.

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RDF Support in the Virtuoso DBMS

Cited by 291 publications

References 3 publications

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

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

Interactive Exploration of Fuzzy RDF Knowledge Bases

FedX: A Federation Layer for Distributed Query Processing on Linked Open Data

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