The University of Southampton
University of Southampton Institutional Repository

LHD: optimising linked data query processing using parallelisation

Wang, Xin, Tiropanis, Thanassis and Davis, Hugh C. (2013) LHD: optimising linked data query processing using parallelisation At Linked Data on the Web (LDOW2013), Brazil. 14 May 2013.

Record type: Conference or Workshop Item (Paper)

Abstract

In the past few years as large volume of Linked Data is being publishing, processing distributed SPARQL queries over the Linked Data cloud is becoming increasingly challenging. The high data traffic cost and response time significantly affect the performance of distributed SPARQL queries as the number of SPARQL end point and the volume of data at each endpoint increase. In this context, parallelisation is promising to fully exploit the potential of connections to SPARQL endpoints and thus improve the efficiency of querying Linked Data. We propose LHD, a distributed SPARQL engine that is built on a highly parallel infrastructure and able to minimise query response time, and we evaluate its performance using a BSBM based environment.

PDF ldow13.pdf - Author's Original
Download (1MB)

More information

Published date: 14 May 2013
Venue - Dates: Linked Data on the Web (LDOW2013), Brazil, 2013-05-14 - 2013-05-14
Keywords: sparql, linked data, distributed query processing
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 350719
URI: http://eprints.soton.ac.uk/id/eprint/350719
PURE UUID: b8a3799d-77a3-4d2e-91bc-488452361ea8
ORCID for Thanassis Tiropanis: ORCID iD orcid.org/0000-0002-6195-2852
ORCID for Hugh C. Davis: ORCID iD orcid.org/0000-0002-1182-1459

Catalogue record

Date deposited: 08 Apr 2013 14:47
Last modified: 18 Jul 2017 04:31

Export record

Contributors

Author: Xin Wang
Author: Hugh C. Davis ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×