Efficient Processing of SPARQL Joins in Memory by Dynamically Restricting Triple Patterns

Jinghua Groppe, Sven Groppe, Sebastian Ebers, Volker Linnemann

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.

Original languageEnglish
Title of host publicationProceedings of the 2009 ACM Symposium on Applied Computing
Number of pages8
Place of PublicationNew York, NY, USA
PublisherACM
Publication date08.03.2009
Pages1231-1238
ISBN (Print)978-1-60558-166-8
DOIs
Publication statusPublished - 08.03.2009
Event24th Annual ACM Symposium on Applied Computing
- Honolulu, United States
Duration: 08.03.200912.03.2009
Conference number: 78664

Research Areas and Centers

  • Research Area: Intelligent Systems
  • Centers: Center for Artificial Intelligence Luebeck (ZKIL)

DFG Research Classification Scheme

  • 409-04 Operating, Communication, Database and Distributed Systems

Fingerprint

Dive into the research topics of 'Efficient Processing of SPARQL Joins in Memory by Dynamically Restricting Triple Patterns'. Together they form a unique fingerprint.

Cite this