Distributed Join Query Processing for Big RDF Data

The expansion of the services of the Semantic Web and the evolution of cloud computing technologies have significantly enhanced the capability of preserving and publishing information in standard open web formats, such that data can be both human-readable and machine-processable. This situation meet...

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Main Authors:	Elzein, Nahla Mohammed, Mazlina, Abdul Majid, Fakherldin, Mohammed, Hashem, Ibrahim Abaker Targio
Format:	Article
Language:	English
Published:	American Scientific Publisher 2018
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/20172/ http://umpir.ump.edu.my/id/eprint/20172/ http://umpir.ump.edu.my/id/eprint/20172/ http://umpir.ump.edu.my/id/eprint/20172/1/Distributed%20Join%20Query%20Processing%20for%20Big%20RDF%20Data.pdf

id	ump-20172
recordtype	eprints
spelling	ump-201722018-11-27T01:55:34Z http://umpir.ump.edu.my/id/eprint/20172/ Distributed Join Query Processing for Big RDF Data Elzein, Nahla Mohammed Mazlina, Abdul Majid Fakherldin, Mohammed Hashem, Ibrahim Abaker Targio QA76 Computer software The expansion of the services of the Semantic Web and the evolution of cloud computing technologies have significantly enhanced the capability of preserving and publishing information in standard open web formats, such that data can be both human-readable and machine-processable. This situation meets the challenge in the current big data era to effectively store, retrieve, and analyze resource description framework (RDF) data in swarms. Moreover, efficient data storage and retrieval that can scale to large amounts of possibly schema-less data have proven to be quite difficult to achieve, specifically, RDF data storage with complex and large graph patterns for representing semantic data, and SPARQL query languages. In this paper, we provide comprehensive discussion about the proposed algorithms of Join.Query processing of RDF data by considering MapReduce Framework in a distributed environment. Moreover, we introduced a framework for RDF query processing and the benchmark that is used for the performance evaluation. Finally, we offer an evaluation discussion on distributed join query processing for big RDF data. American Scientific Publisher 2018-11 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/20172/1/Distributed%20Join%20Query%20Processing%20for%20Big%20RDF%20Data.pdf Elzein, Nahla Mohammed and Mazlina, Abdul Majid and Fakherldin, Mohammed and Hashem, Ibrahim Abaker Targio (2018) Distributed Join Query Processing for Big RDF Data. Advanced Science Letters, 24 (10). p. 96. ISSN 1936-6612 https://doi.org/10.1166/asl.2018.13013 doi: 10.1166/asl.2018.13013
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Elzein, Nahla Mohammed Mazlina, Abdul Majid Fakherldin, Mohammed Hashem, Ibrahim Abaker Targio Distributed Join Query Processing for Big RDF Data
description	The expansion of the services of the Semantic Web and the evolution of cloud computing technologies have significantly enhanced the capability of preserving and publishing information in standard open web formats, such that data can be both human-readable and machine-processable. This situation meets the challenge in the current big data era to effectively store, retrieve, and analyze resource description framework (RDF) data in swarms. Moreover, efficient data storage and retrieval that can scale to large amounts of possibly schema-less data have proven to be quite difficult to achieve, specifically, RDF data storage with complex and large graph patterns for representing semantic data, and SPARQL query languages. In this paper, we provide comprehensive discussion about the proposed algorithms of Join.Query processing of RDF data by considering MapReduce Framework in a distributed environment. Moreover, we introduced a framework for RDF query processing and the benchmark that is used for the performance evaluation. Finally, we offer an evaluation discussion on distributed join query processing for big RDF data.
format	Article
author	Elzein, Nahla Mohammed Mazlina, Abdul Majid Fakherldin, Mohammed Hashem, Ibrahim Abaker Targio
author_facet	Elzein, Nahla Mohammed Mazlina, Abdul Majid Fakherldin, Mohammed Hashem, Ibrahim Abaker Targio
author_sort	Elzein, Nahla Mohammed
title	Distributed Join Query Processing for Big RDF Data
title_short	Distributed Join Query Processing for Big RDF Data
title_full	Distributed Join Query Processing for Big RDF Data
title_fullStr	Distributed Join Query Processing for Big RDF Data
title_full_unstemmed	Distributed Join Query Processing for Big RDF Data
title_sort	distributed join query processing for big rdf data
publisher	American Scientific Publisher
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/20172/ http://umpir.ump.edu.my/id/eprint/20172/ http://umpir.ump.edu.my/id/eprint/20172/ http://umpir.ump.edu.my/id/eprint/20172/1/Distributed%20Join%20Query%20Processing%20for%20Big%20RDF%20Data.pdf
first_indexed	2023-09-18T22:28:55Z
last_indexed	2023-09-18T22:28:55Z
_version_	1777416153954516992

Distributed Join Query Processing for Big RDF Data

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