Reference installation for the German grid initiative D-Grid

Buehler, W; Dulov, O; Molina‐García, Ángel; Jejkal, Thomas; Jrad, F.; Marten, H.; Mol, X; Nilsen, D; Schneider, O.

doi:10.1088/1742-6596/219/6/062044

Cited by 2 publications

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“…Some of these data workflow systems are presented in [18,12,19]. These systems have been successfully used in a variety of data intensive scenarios like analyzing data from the Southern California Earthquake Center [20], data from biological domains like post genomic research [21], analysis of proteins and peptides from tandem mass spectrometry data [22], cancer research [23], meteorological phenomena [24] or used in the German grid platform [25]. In these scenarios, the systems accessed and processed petabytes of data, and we are convinced that the approach they use is the most suitable for managing the large amounts of data present in the LOD cloud.…”

Section: Implementation Of Sparql-dqp and Welldesigned Patterns Optimmentioning

confidence: 99%

Federating queries in SPARQL 1.1: Syntax, semantics and evaluation

Buil-Aranda

Arenas²,

Corcho

et al. 2013

Journal of Web Semantics

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Section: Implementation Of Sparql-dqp and Welldesigned Patterns Optimmentioning

confidence: 99%

Federating queries in SPARQL 1.1: Syntax, semantics and evaluation

Buil-Aranda

Arenas²,

Corcho

et al. 2013

Journal of Web Semantics

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Section: Implementation Of Sparql-dqp and Welldesigned Patterns Optimmentioning

confidence: 99%

Federating Queries in SPARQL1.1: Syntax, Semantics and Evaluation

et al. 2013

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Given the sustained growth that we are experiencing in the number of SPARQL endpoints available, the need to be able to send federated SPARQL queries across these has also grown. To address this use case, the W3C SPARQL working group is defining a federation extension for SPARQL 1.1 which allows for combining graph patterns that can be evaluated over several endpoints within a single query. In this paper, we describe the syntax of that extension and formalize its semantics. Additionally, we describe how a query evaluation system can be implemented for that federation extension, describing some static optimization techniques and reusing a query engine used for data-intensive science, so as to deal with large amounts of intermediate and final results. Finally we carry out a series of experiments that show that our optimizations speed up the federated query evaluation process.Recent years have witnessed a large and constant growth in the amount of RDF data available on the Web, exposed by means of Linked Data-enabled dereferenceable URIs in various formats (such as RDF/XML, Turtle, RDFa, etc.) and -of particular interest for the present paper -by SPARQL endpoints. Several nonexhaustive, and sometimes out-of-date or not continuously maintained, lists of SPARQL endpoints or data catalogs are available in different formats like CKAN 1 , The Data Hub 2 , the W3C wiki 3 , etc. Most of these datasets are interlinked, as depicted graphically in the well-known Linked Open Data Cloud diagram 4 , which allows navigating through them and facilitates buildThis work has been performed in the context of the ADMIRE project (EU FP7 ICT-215024), and was supported by the Science Foundation Ireland project Lion-2 (Grant No. SFI/08/CE/I1380), as well as the Net2 project (FP7 Marie Curie IRSES 247601). We would like to thank, among many others, the OGSA-DAI team, specially to Ally Hume, for their advice in the development of the data workflows. Marc-Alexandre Nolin for his help with the bio2rdf queries and Jorge Pe´rez for his advice in theorem proving. ing complex queries by combining data from different, sometimes heterogeneous and often physically distributed datasets. SPARQL endpoints are RESTful services that accept queries over HTTP written in the SPARQL query language [1,2] adhering to the SPARQL protocol [3], as defined by the respective W3C recommendation documents. However, the current SPARQL recommendation has an important limitation in terms of defining and executing queries that span across distributed datasets, since it hides the physical distribution of data across endpoints, and has normally been used for querying isolated endpoints. Hence users willing to federate queries across a number of SPARQL endpoints have been forced to create ad-hoc extensions of the query language and protocol, to include additional information about data sources in the configuration of their SPARQL endpoint servers [4,5,6] or to devise engineering solutions where data from remote endpoints is copied into the endpoint being queried. Gi...

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Reference installation for the German grid initiative D-Grid

Cited by 2 publications

References 1 publication

Federating queries in SPARQL 1.1: Syntax, semantics and evaluation

Federating queries in SPARQL 1.1: Syntax, semantics and evaluation

Federating Queries in SPARQL1.1: Syntax, Semantics and Evaluation

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