OntoQuad: Native High-Speed RDF DBMS for Semantic Web

  • Alexander Potocki
  • Anton Polukhin
  • Grigory Drobyazko
  • Daniel Hladky
  • Victor Klintsov
  • Jörg Unbehauen
Part of the Communications in Computer and Information Science book series (CCIS, volume 394)


In the last years native RDF stores made enormous progress in closing the performance gap compared to RDBMS. This albeit smaller gap, however, still prevents adoption of RDF stores in scenarios with high requirements on responsiveness. We try to bridge the gap and present a native RDF store “OntoQuad” and its fundamental design principles. Basing on previous researches, we develop a vector database schema for quadruples, its realization on index data structures, and ways to efficiently implement the joining of two and more data sets simultaneously. We also offer approaches to optimizing the SPARQL query execution plan which is based on its heuristic transformations. The query performance efficiency is checked and proved on BSBM tests. The study results can be taken into consideration during the development of RDF DBMS’s suitable for storing large volumes of Semantic Web data, as well as for the creation of large-scale repositories of semantic data.


RDF SPARQL index multiple joins query optimization 


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  1. 1.
    Harris, S., Seaborne, A.: SPARQL 1.1 Query Language. Technical report, W3C Recommendation (2013),
  2. 2.
    Harth, A., Decker, S.: Optimized Index Structures for Querying RDF from the Web. In: LA-WEB (Latin American Web Congress) (2005)Google Scholar
  3. 3.
    Harth, A., Umbrich, J., Hogan, A., Decker, S.: YARS2: A Federated Repository for Querying Graph Structured Data from the Web. In: ISWG/ASWG, pp. 211–224 (2007)Google Scholar
  4. 4.
    Harth, A., Decker, S.: Yet Another RDF Store: Perfect Index Structures for Storing Semantic Web Data With Context, DERI Technical Report (2004)Google Scholar
  5. 5.
    Baolin, L., Bo, H.: HPRD: A High Performance RDF Database. In: NPG, pp. 364–374 (2007)Google Scholar
  6. 6.
    Weiss, C., Karras, P., Bernstein, A.: Sextuple Indexing for Semantic Web Data Management. PVLDB 1(1), 1008–1019 (2008)Google Scholar
  7. 7.
    Abadi, D.J., Marcus, A., Madden, S., Hollenbach, K.J.: Scalable Semantic Web Data Management Using Vertical Partitioning. In: VLDB, pp. 411–422 (2007)Google Scholar
  8. 8.
    Wood, D., Gearon, P., Adams, T.: Kowari: A Platform for Semantic Web Storage and Analysis. In: XTeGh (2005)Google Scholar
  9. 9.
    Neumann, T., Weikum, G.: The RDF-3X Engine for Scalable Management of RDF Data. Journal: The Vldb Journal - VLDB 19(1), 91–113 (2010)CrossRefGoogle Scholar
  10. 10.
    Neumann, T., Weikum, G.: RDF-3X: a RISC-style Engine for RDF. PVLDB 1(1), 647–659 (2008)Google Scholar
  11. 11.
    Stocker, M., Seaborne, A., Bernstein, A., Kiefer, C., Reynolds, D.: SPARQL Basic Graph Pattern Optimization Using Selectivity Estimation. In: WWW 2008, pp. 595–604. ACM, New York (2008)Google Scholar
  12. 12.
    Hartig, O., Heese, R.: The SPARQL Query Graph Model for Query Optimization. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 564–578. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Pérez, J., Arenas, M., Gutierrez, C.: Semantics and Complexity of SPARQL. In: Cruz, I., Decker, S., Allemang, D., Preist, C., Schwabe, D., Mika, P., Uschold, M., Aroyo, L.M. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 30–43. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Gomathi, R., Sathya, C.: Efficient Optimization of Multiple SPARQL Queries. IOSR Journal of Computer Engineering (IOSR-JCE) 8(6), 97–101 (2013), e-ISSN: 2278-0661, p- ISSN: 2278-8727
  15. 15.
    Garcia-Molina, H., Ullman, J.D., Widom, J.: Database Systems: The Complete Book. Prentice Hall, Upper Saddle River (2002) ISBN 0130319953Google Scholar
  16. 16.
    Stonebraker, M., Çetintemel, U.: One Size Fits All: An Idea Whose Time Has Come and Gone. In: Proceedings of the International Conference on Data Engineering, IGDE (2005)Google Scholar
  17. 17.
    Stonebraker, M., Bear, C., Çetintemel, U., Cherniack, M., Ge, T., Hachem, N., Harizopoulos, S., Lifter, J., Rogers, J., Zdonik, S.: One Size Fits All? - Part 2: Benchmarking Results. In: Proc. Conference on Innovative Data Systems Research, CIDR (2007)Google Scholar
  18. 18.
    Antoshenkov, G., Ziauddin, M.: Query Processing and Optimization in Oracle Rdb. VLDB J. 5(4), 229–237 (1996)CrossRefGoogle Scholar
  19. 19.
    Graefe, G.: Query Evaluation Techniques for Large Databases. ACM Comput. Surv. 25(2), 73–170 (1993)CrossRefGoogle Scholar
  20. 20.
    Neumann, T., Weikum, G.: Scalable Join Processing on Very Large RDF Graphs. In: SIGMOD 2009, Providence, USA (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alexander Potocki
    • 1
  • Anton Polukhin
    • 1
  • Grigory Drobyazko
    • 2
  • Daniel Hladky
    • 2
  • Victor Klintsov
    • 2
  • Jörg Unbehauen
    • 3
  1. 1.EventosMoscowRussia
  2. 2.National Research University - Higher School of Economics (NRU HSE)MoscowRussia
  3. 3.Institut für InformatikUniversität LeipzigLeipzigGermany

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