HPRD: A High Performance RDF Database

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4672)


In this paper a high performance storage system for RDF documents is introduced. The system employs optimized index structures for RDF data and efficient RDF query evaluation. The index scheme consists of 3 types of indices. Triple index manages basic RDF triples by dividing original RDF graph into several sub-graphs. Path index manages frequent RDF path patterns for long path query performance enhancement. Context index is optional for context oriented RDF data and temporal RDF data. In this paper, we describe the organization of index structures, show the process of evaluating queries based on the index structures, and provide a performance comparison with exist RDF databases through several benchmark experiments.


Database Index RDF Query 


  1. 1.
    World Wide Web Consortium: Semantic Web (2001),
  2. 2.
    World Wide Web Consortium: Resource Description Framework Model and Syntax Specification (1999),
  3. 3.
    World Wide Web Consortium: Resource Description Framework Schema Specification 1.0. (2000),
  4. 4.
    World Wide Web Consortium: Survey of RDF/Triple Data Stores (2001),
  5. 5.
    Alexaki, S., Christophides, V., Karvounarakis, G., Plexousakis, D., Tolle, K.: The RDFSuite: Managing Voluminous RDF Description Bases. Technical report, Institute of Computer Science, FORTH, Heraklion, Greece (2000),
  6. 6.
    McBride, B.: Jena: Implementing The RDF Model and Syntax Specification. In: Proceedings of the Second International Workshop on the Semantic Web - SemWeb 2001, Hongkong (2001)Google Scholar
  7. 7.
    Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema,
  8. 8.
  9. 9.
    Hayes, P.: RDF Semantics. W3C Recommendation (2004),
  10. 10.
    Manola, F., Miller, E.: RDF Primer: W3C Recommendation (2004),
  11. 11.
    Guha, R.V., McCool, R., Fikes, R.: Contexts for the Semantic Web. In: Proceedings of the 3rd International Semantic Web Conference, Hiroshima (2004)Google Scholar
  12. 12.
    Visser, U. (ed.): Intelligent Information Integration for the Semantic Web. LNCS (LNAI), vol. 3159. Springer, Heidelberg (2004)Google Scholar
  13. 13.
  14. 14.
    Manber, U., Myers, E.: Suffix Arrays: A New Method for On-Line String Searches. SIAM. J. on Computing 5, 935–948 (1993)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Gutierrez, C., Hurtado, C., Vaisman, A.: Temporal RDF. In: ECSW 2005. Proceedings of European Conference on the Semantic Web, pp. 93–107 (2005)Google Scholar
  16. 16.
    Ono, K., Lohman, G.M.: Measuring The Complexity of Join Enumeration in Query Optimization. In: Proceedings of 16th International Conference on Very Large Data Bases, pp. 314–325. Morgan Kaufmann, San Francisco (1990)Google Scholar
  17. 17.
    SWAT Projects-The Lehigh University Benchmark (LUBM),
  18. 18.
    Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proceedings of the 11th International Conference on Data Engineering, Taipei, pp. 3–14 (1995)Google Scholar
  19. 19.
    Garofalakis, M.N., Rastogi, R., Shim, K.: Spirit: Sequential Pattern Mining with Regular Expression Constraints. In: Proceedings of 25th International Conference on Very Large Data Bases, Edinburgh, pp. 223–234 (1999)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2007

Authors and Affiliations

  1. 1.Department of Computer Science and Technology, Tsinghua University, Beijing 100084P.R. China

Personalised recommendations