Parallelizing Join Computations of SPARQL Queries for Large Semantic Web Databases

Jinghua Groppe; Sven Groppe

doi:10.1145/1982185.1982536

Parallelizing Join Computations of SPARQL Queries for Large Semantic Web Databases

Jinghua Groppe, Sven Groppe

Institut für Informationssysteme

20 Zitate (Scopus)

Abstract

While a number of optimizing techniques have been developed to efficiently process increasing large Semantic Web databases, these optimization approaches have not fully leveraged the powerful computation capability of modern computers. Today's multi-core computers promise an enormous performance boost by providing a parallel computing platform. Although the parallel relational database systems have been well built, parallel query computing in Semantic Web databases have not extensively been studied. In this work, we develop the parallel algorithms for join computations of SPARQL queries. Our performance study shows that the parallel computation of SPARQL queries significantly speeds up querying large Semantic Web databases.

Originalsprache	Englisch
Titel	Proceedings of the 2011 ACM Symposium on Applied Computing
Seitenumfang	6
Erscheinungsort	New York, NY, USA
Herausgeber (Verlag)	ACM
Erscheinungsdatum	21.03.2011
Seiten	1681-1686
ISBN (Print)	978-1-4503-0113-8
DOIs	https://doi.org/10.1145/1982185.1982536
Publikationsstatus	Veröffentlicht - 21.03.2011
Veranstaltung	26th Annual ACM Symposium on Applied Computing - TaiChung, Taiwan Dauer: 21.03.2011 → 24.03.2011 Konferenznummer: 85134

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10.1145/1982185.1982536

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title = "Parallelizing Join Computations of SPARQL Queries for Large Semantic Web Databases",

abstract = "While a number of optimizing techniques have been developed to efficiently process increasing large Semantic Web databases, these optimization approaches have not fully leveraged the powerful computation capability of modern computers. Today's multi-core computers promise an enormous performance boost by providing a parallel computing platform. Although the parallel relational database systems have been well built, parallel query computing in Semantic Web databases have not extensively been studied. In this work, we develop the parallel algorithms for join computations of SPARQL queries. Our performance study shows that the parallel computation of SPARQL queries significantly speeds up querying large Semantic Web databases. ",

author = "Jinghua Groppe and Sven Groppe",

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doi = "10.1145/1982185.1982536",

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publisher = "ACM",

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Y1 - 2011/3/21

N2 - While a number of optimizing techniques have been developed to efficiently process increasing large Semantic Web databases, these optimization approaches have not fully leveraged the powerful computation capability of modern computers. Today's multi-core computers promise an enormous performance boost by providing a parallel computing platform. Although the parallel relational database systems have been well built, parallel query computing in Semantic Web databases have not extensively been studied. In this work, we develop the parallel algorithms for join computations of SPARQL queries. Our performance study shows that the parallel computation of SPARQL queries significantly speeds up querying large Semantic Web databases.

AB - While a number of optimizing techniques have been developed to efficiently process increasing large Semantic Web databases, these optimization approaches have not fully leveraged the powerful computation capability of modern computers. Today's multi-core computers promise an enormous performance boost by providing a parallel computing platform. Although the parallel relational database systems have been well built, parallel query computing in Semantic Web databases have not extensively been studied. In this work, we develop the parallel algorithms for join computations of SPARQL queries. Our performance study shows that the parallel computation of SPARQL queries significantly speeds up querying large Semantic Web databases.

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M3 - Conference contribution

SN - 978-1-4503-0113-8

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BT - Proceedings of the 2011 ACM Symposium on Applied Computing

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CY - New York, NY, USA

T2 - 26th Annual ACM Symposium on Applied Computing

Y2 - 21 March 2011 through 24 March 2011

ER -

Parallelizing Join Computations of SPARQL Queries for Large Semantic Web Databases

Abstract

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