Efficient processing of SPARQL joins in memory by dynamically restricting triple patterns

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Efficient processing of SPARQL joins in memory by dynamically restricting triple patterns

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Symposium on Applied Computing archive
Proceedings of the 2009 ACM symposium on Applied Computing table of contents

Honolulu, Hawaii

SESSION: The semantic web and applications track table of contents

Pages 1231-1238

Year of Publication: 2009

ISBN:978-1-60558-166-8

Authors

Jinghua Groppe	University of Lübeck, Lübeck, Germany
Sven Groppe	University of Lübeck, Lübeck, Germany
Sebastian Ebers	University of Lübeck, Lübeck, Germany
Volker Linnemann	University of Lübeck, Lübeck, Germany

Sponsor

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

ACM New York, NY, USA

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ABSTRACT

Since there are a lot of similar or common properties between RDF and relational databases and between SPARQL and SQL, many efforts focus on leveraging the research results of optimizing relational query languages for optimizing SPARQL queries. However, SPARQL has its own characteristics different from SQL, which are not fully exploited by existing work. Therefore, there is still much space for research on optimizing SPARQL queries. Based on the triple nature of RDF data, we create 7 indices to retrieve RDF data quickly; based on the SPARQL-specific properties and the 7 indices, we develop a new, efficient approach to computing join by dynamically restricting triple patterns. Our experimental results show the efficiency of our approach.

REFERENCES

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