skip to main content
review-article

Relational processing of RDF queries: a survey

Published:27 June 2010Publication History
Skip Abstract Section

Abstract

The Resource Description Framework (RDF) is a flexible model for representing information about resources in the web. With the increasing amount of RDF data which is becoming available, efficient and scalable management of RDF data has become a fundamental challenge to achieve the SemanticWeb vision. The RDF model has attracted the attention of the database community and many researchers have proposed different solutions to store and query RDF data efficiently. This survey focuses on using relational query processors to store and query RDF data. We provide an overview of the different approaches and classify them according to their storage and query evaluation strategies.

References

  1. Daniel J. Abadi, Adam Marcus, Samuel Madden, and Kate Hollenbach. SW-Store: a vertically partitioned DBMS for Semantic Web data management. VLDB Journal, 18(2):385--406, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Sofia Alexaki, Vassilis Christophides, Gregory Karvounarakis, Dimitris Plexousakis, and Karsten Tolle. The ICS-FORTH RDFSuite: Managing Voluminous RDF Description Bases. In Proceedings of the 2nd InternationalWorkshop on the Semantic Web (SemWeb), 2001.Google ScholarGoogle Scholar
  3. Jennifer L. Beckmann, Alan Halverson, Rajasekar Krishnamurthy, and Jeffrey F. Naughton. Extending RDBMSs To Support Sparse Datasets Using An Interpreted Attribute Storage Format. In Proceedings of the 22nd International Conference on Data Engineering (ICDE), page 58, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Christian Bizer and Andreas Schultz. Benchmarking the Performance of Storage Systems that expose SPARQL Endpoints. In Proceedings of the 4th International Workshop on Scalable Semantic Web knowledge Base Systems (SSWS)., 2008.Google ScholarGoogle Scholar
  5. Viorica Botea, Daniel Mallett, Mario A. Nascimento, and Jörg Sander. PIST: An Efficient and Practical Indexing Technique for Historical Spatio-Temporal Point Data. GeoInformatica, 12(2):143--168, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Jeen Broekstra, Arjohn Kampman, and Frank van Harmelen. Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema. In Proceedings of the First International Semantic Web Conference(ISWC), pages 54--68, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Surajit Chaudhuri and Gerhard Weikum. Rethinking Database System Architecture: Towards a Self-Tuning RISC-Style Database System. In Proceedings of 26th International Conference on Very Large Data Bases (VLDB), pages 1--10, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Eugene Inseok Chong, Souripriya Das, George Eadon, and Jagannathan Srinivasan. An Efficient SQL-based RDF Querying Scheme. In Proceedings of the 31st International Conference on Very Large Data Bases (VLDB), pages 1216--1227, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Eric Chu, Jennifer L. Beckmann, and Jeffrey F. Naughton. The case for a wide-table approach to manage sparse relational data sets. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 821--832, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. George P. Copeland and Setrag Khoshafian. A Decomposition Storage Model. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 268--279, 1985. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Torsten Grust, Sherif Sakr, and Jens Teubner. XQuery on SQL Hosts. In Proceedings of the Thirtieth International Conference on Very Large Data Bases (VLDB), pages 252--263, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Stephen Harris and Nicholas Gibbins. 3store: Efficient Bulk RDF Storage. In Proceedings of the First International Workshop on Practical and Scalable Semantic Systems (PSSS), 2003.Google ScholarGoogle Scholar
  13. Andreas Harth and Stefan Decker. Optimized Index Structures for Querying RDF from the Web. In Proceedings of the Third Latin American Web Congress (LA-WEB), pages 71--80, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Shawn R. Jeffery, Michael J. Franklin, and Alon Y. Halevy. Pay-as-you-go user feedback for dataspace systems. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 847--860, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Justin J. Levandoski and Mohamed F. Mokbel. RDF Data-Centric Storage. In Proceedings of the IEEE International Conference on Web Services (ICWS), 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Li Ma, Zhong Su, Yue Pan, Li Zhang, and Tao Liu. RStar: an RDF storage and query system for enterprise resource management. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM), pages 484--491, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Frank Manola and Eric Miller. RDF Primer, W3C Recommendation, February 2004. http://www.w3.org/TR/REC-rdf-syntax/.Google ScholarGoogle Scholar
  18. Akiyoshi Matono, Toshiyuki Amagasa, Masatoshi Yoshikawa, and Shunsuke Uemura. A Path-based Relational RDF Database. In Proceedings of the 16th Australasian Database Conference (ADC), pages 95--103, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Brian McBride. Jena: A Semantic Web Toolkit. IEEE Internet Computing, 6(6):55--59, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Thomas Neumann and Gerhard Weikum. RDF-3X: a RISC-style engine for RDF. Proceedings of the VLDB Endownment (PVLDB), 1(1):647--659, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Thomas Neumann and Gerhard Weikum. Scalable join processing on very large RDF graphs. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 627--640, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Eric Prud'hommeaux and Andy Seaborne. SPARQL Query Language for RDF, W3C Recommendation, January 2008. http://www.w3.org/TR/rdf-sparql-query/.Google ScholarGoogle Scholar
  23. Michael Schmidt, Thomas Hornung, Norbert Küchlin, Georg Lausen, and Christoph Pinkel. An Experimental Comparison of RDF Data Management Approaches in a SPARQL Benchmark Scenario. In Proceedings of the 7th International Semantic Web Conference (ISWC), pages 82--97, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Michael Schmidt, Thomas Hornung, Georg Lausen, and Christoph Pinkel. SP2Bench: A SPARQL Performance Benchmark. In Proceedings of the 25th International Conference on Data Engineering (ICDE), pages 222--233, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Lefteris Sidirourgos, Romulo Goncalves, Martin L. Kersten, Niels Nes, and Stefan Manegold. Column-store support for RDF data management: not all swans are white. Proceedings of the VLDB Endownment (PVLDB), 1(2):1553--1563, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Michael Stonebraker, Daniel J. Abadi, Adam Batkin, Xuedong Chen, Mitch Cherniack, Miguel Ferreira, Edmond Lau, Amerson Lin, Samuel Madden, Elizabeth J. O'Neil, Patrick E. O'Neil, Alex Rasin, Nga Tran, and Stanley B. Zdonik. C-Store: A Column-oriented DBMS. In Proceedings of the 31st International Conference on Very Large Data Bases (VLDB), pages 553--564, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Can Türker and Michael Gertz. Semantic integrity support in SQL: 1999 and commercial (object-)relational database management systems. VLDB Journal, 10(4):241--269, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Cathrin Weiss, Panagiotis Karras, and Abraham Bernstein. Hexastore: sextuple indexing for semantic web data management. Proceedings of the VLDB Endownment (PVLDB), 1(1):1008--1019, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Relational processing of RDF queries: a survey
          Index terms have been assigned to the content through auto-classification.

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in

          Full Access

          • Published in

            cover image ACM SIGMOD Record
            ACM SIGMOD Record  Volume 38, Issue 4
            December 2009
            44 pages
            ISSN:0163-5808
            DOI:10.1145/1815948
            Issue’s Table of Contents

            Copyright © 2010 Authors

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 27 June 2010

            Check for updates

            Qualifiers

            • review-article

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader