Information for "Querying the Web of Data with Graph Theory-based Techniques"

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Display titleQuerying the Web of Data with Graph Theory-based Techniques
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Page creatorSaid (talk | contribs)
Date of page creation12:10, 26 March 2018
Latest editorSaid (talk | contribs)
Date of latest edit21:56, 11 July 2018
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Retrieved from "https://www.openresearch.org/wiki/Querying_the_Web_of_Data_with_Graph_Theory-based_Techniques"
Facts about "Querying the Web of Data with Graph Theory-based Techniques"
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Access API{{{API}}} +
Event in seriesWeb and Internet Science +
Has Challenges{{{Challenges}}} +
Has DataCatalouge{{{Catalogue}}} +
Has DescriptionWe deploy 6 SPARQL endpoints (Sesame 2.4.0) on 5 remote

virtual machines. About 400,000 triples (generated by BSBM) are distributed to these endpoints following Gaussian

distribution. +
Has DimensionsPerformance +
Has DocumentationURLhttp://code.google.com/p/gdsparal/ +
Has Downloadpagehttp://code.google.com/p/gdsparal/ +
Has EvaluationEvaluation of GDS, DARQ and FedX +
Has EvaluationMethodcomparing the three distributed SPARQL engines +
Has ExperimentSetupThe three engines are run independently

on a machine having an Intel Xeon W3520 processor, 12 GB

memory and 1Gbps LAN. +
Has GUINo +
Has Hypothesis{{{Hypothesis}}} +
Has ImplementationGDS +
Has InfoRepresentation{{{InfoRepresentation}}} +
Has Limitations{{{Limitations}}} +
Has NegativeAspects{{{NegativeAspects}}} +
Has PositiveAspects{{{PositiveAspects}}} +
Has Requirements{{{Requirements}}} +
Has Results{{{Results}}} +
Has Version{{{Version}}} +
Has abstractThe increasing amount of Linked Data on th …
The increasing amount of Linked Data on the Web enables users to retrieve quality and complex information and to deploy innovative, added-value applications. The volume of available Linked Data and their spread across a large number of repositories make a strong case for ecient distributed SPARQL queries. However, in practice, current distributed SPARQL query processing techniques face issues on performance and scalability. In our previous work we provided initial evidence that graph theory-based techniques can address performance issues better than other approaches such as DARQ. Here we further exploit the potential of graph algorithms and we show how they can address performance and scalability for distributed SPARQL queries even better. To that end, we present an improved engine called GDS and we evaluate it by providing a detailed comparison to existing approaches for distributed queries (i.e. DARQ and FedX). By analyzing the evaluation results, we try to identify promising techniques for distributed SPARQL processing, and to outline the problems that need to be addressed in future research.
t need to be addressed in future research. +
Has authorsXin Wang +, Thanassis Tiropanis + and Hugh C. Davis +
Has keywordsSPARQL, Linked Data, distributed query processing. +
Has motivation{{{Motivation}}} +
Has platformJava VM +
Has subjectQuerying Distributed RDF Data Sources +
Has vendor{{{vendor}}} +
Has year2006 +
ImplementedIn ProgLangJava +
Proposes Algorithm{{{ProposesAlgorithm}}} +
TitleQuerying the Web of Data with Graph Theory-based Techniques +
Uses FrameworkJena +
Uses Methodology{{{Methodology}}} +
Uses Toolbox{{{Toolbox}}} +