LHD: optimising linked data query processing using
parallelisation
LHD: optimising linked data query processing using
parallelisation
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.
sparql, linked data, distributed query processing
Wang, Xin
735297cd-af6a-430e-bf68-8550d1a2f60b
Tiropanis, Thanassis
d06654bd-5513-407b-9acd-6f9b9c5009d8
Davis, Hugh C.
1608a3c8-0920-4a0c-82b3-ee29a52e7c1b
14 May 2013
Wang, Xin
735297cd-af6a-430e-bf68-8550d1a2f60b
Tiropanis, Thanassis
d06654bd-5513-407b-9acd-6f9b9c5009d8
Davis, Hugh C.
1608a3c8-0920-4a0c-82b3-ee29a52e7c1b
Wang, Xin, Tiropanis, Thanassis and Davis, Hugh C.
(2013)
LHD: optimising linked data query processing using
parallelisation.
Linked Data on the Web (LDOW2013), Rio de Janeiro, 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.
Text
ldow13.pdf
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More information
Published date: 14 May 2013
Venue - Dates:
Linked Data on the Web (LDOW2013), Rio de Janeiro, 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
Catalogue record
Date deposited: 08 Apr 2013 14:47
Last modified: 15 Mar 2024 03:31
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Contributors
Author:
Xin Wang
Author:
Thanassis Tiropanis
Author:
Hugh C. Davis
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