MapReduce-based Solutions for Scalable SPARQL Querying

José M. Giménez-García; Javier David Fernandez Garcia; Miguel A. Martínez-Prieto

MapReduce-based Solutions for Scalable SPARQL Querying

José M. Giménez-García, Javier David Fernandez Garcia, Miguel A. Martínez-Prieto

Institute for Data, Process and Knowledge Management (AE Polleres)

Publication: Scientific journal › Journal article › peer-review

Abstract

The use of RDF to expose semantic data on the Web has seen a dramatic increase over the last few years. Nowadays, RDF datasets are so big and interconnected that, in fact, classical mono-node solutions present significant scalability problems when trying to manage big semantic data. MapReduce, a standard framework for distributed processing of great quantities of data, is earning a place among the distributed solutions facing RDF scalability issues. In this article, we survey the most important works addressing RDF management and querying through
diverse MapReduce approaches, with a focus on their main strategies, optimizations and results.

Original language	English
Pages (from-to)	1 - 18
Journal	Open Journal of Semantic Web (OJSW)
Volume	1
Issue number	1
Publication status	Published - 1 Apr 2014

Austrian Classification of Fields of Science and Technology (ÖFOS)

102015 Information systems
102
502050 Business informatics

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title = "MapReduce-based Solutions for Scalable SPARQL Querying",

abstract = "The use of RDF to expose semantic data on the Web has seen a dramatic increase over the last few years. Nowadays, RDF datasets are so big and interconnected that, in fact, classical mono-node solutions present significant scalability problems when trying to manage big semantic data. MapReduce, a standard framework for distributed processing of great quantities of data, is earning a place among the distributed solutions facing RDF scalability issues. In this article, we survey the most important works addressing RDF management and querying through diverse MapReduce approaches, with a focus on their main strategies, optimizations and results.",

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N2 - The use of RDF to expose semantic data on the Web has seen a dramatic increase over the last few years. Nowadays, RDF datasets are so big and interconnected that, in fact, classical mono-node solutions present significant scalability problems when trying to manage big semantic data. MapReduce, a standard framework for distributed processing of great quantities of data, is earning a place among the distributed solutions facing RDF scalability issues. In this article, we survey the most important works addressing RDF management and querying through diverse MapReduce approaches, with a focus on their main strategies, optimizations and results.

AB - The use of RDF to expose semantic data on the Web has seen a dramatic increase over the last few years. Nowadays, RDF datasets are so big and interconnected that, in fact, classical mono-node solutions present significant scalability problems when trying to manage big semantic data. MapReduce, a standard framework for distributed processing of great quantities of data, is earning a place among the distributed solutions facing RDF scalability issues. In this article, we survey the most important works addressing RDF management and querying through diverse MapReduce approaches, with a focus on their main strategies, optimizations and results.

UR - http://dataweb.infor.uva.es/wp-content/uploads/2014/03/ojsw14.pdf

M3 - Journal article

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JO - Open Journal of Semantic Web (OJSW)

JF - Open Journal of Semantic Web (OJSW)

IS - 1

ER -

MapReduce-based Solutions for Scalable SPARQL Querying

Abstract

Austrian Classification of Fields of Science and Technology (ÖFOS)

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