Linked Data in the RDF format can be viewed as a set of interlinked data on the web. Particular tasks, which are computed upon this data includes text based searching for entities, relations or performing various queries using querying languages like SPARQL. Such interlinked data can be interpreted as a graph with edges and vertexes. For the current SPARQL 1.1 standard, there is support for graph traversal, proposed and announced by SPARQL working group. Regarding performance, the property path task is the problem in current solutions. This paper describes an innovative and time efficient method of the graph traversal task - SRelation. First we discuss current approaches for SPARQL 1.1 graph traversal. For data access we mention local and distributed solutions, disk-based, mixed and whole-in-memory data storage aspects. We debate pros and cons of various approaches and suggest our new method SRelation to fit in the field of in-memory concept. To support this, we present our experiments on selected Linked Data datasets.


RDF graph graph traversal property path SPARQL 1.1 Jena Sesame OWLIM-Lite in-memory 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Berners-Lee, T.: Linked Data - Design Issues, (retrieved May 28, 2013)
  2. 2.
    W3C consortium, (retrieved May 28, 2013)
  3. 3.
    Schmidt, M., Hornung, T., Lausen, G., Pinkel, C.: SP^2Bench: a SPARQL Performance Benchmark. In: The 25th IEEE International Conference on Data Engineering, pp. 222–233. IEEE (2009)Google Scholar
  4. 4.
    Bizer, C., Schultz, A.: The Berlin Sparql Benchmark. International Journal on Semantic Web and Information Systems 5(2), 1–24 (2009)CrossRefGoogle Scholar
  5. 5.
    Even, S.: Graph algorithms. Cambridge University Press (2011)Google Scholar
  6. 6.
    Bahmani, B., Goel, A.: Partitioned Multi-Indexing: Bringing Order to Social Search. In: The 21st International Conference on World Wide Web, pp. 399–408. ACM (2012)Google Scholar
  7. 7.
    Hatcher, E., Gospodnetic, O., Mccandless, M.: Lucene in Action. Manning Publications Co., Greenwich (2004)Google Scholar
  8. 8.
    Lumsdaine, A., Gregor, D., Hendrickson, B., Berry, J.: Challenges in Parallel Graph Processing. Parallel Processing Letters 17(1), 5–20 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Schmidt, M., Hornung, T., Küchlin, N., Lausen, G., Pinkel, C.: An Experimental Comparison of RDF Data Management Approaches in a SPARQL Benchmark Scenario. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 82–97. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Abadi, D.J., Marcus, A., Madden, S.R., Hollenbach, K.: Scalable Semantic Web Data Management Using Vertical Partitioning. In: The 33rd International Conference on Very Large Data Bases, pp. 411–422 (2007)Google Scholar
  11. 11.
    Arenas, M., Conca, S., Pérez, J.: Counting Beyond a Yottabyte, or How SPARQL 1.1 Property Paths Will Prevent Adoption of the Standard. In: Proceedings of the 21st International Conference on World Wide Web, pp. 629–638 (2012)Google Scholar
  12. 12.
    Mituzas, D.: Wikipedia: Site Internals, Configuration and Code Examples and Management Issues (2007)Google Scholar
  13. 13.
    DB-Engines Ranking, (retrieved July 9, 2013)
  14. 14. (retrieved July 8, 2013)
  15. 15.
    Heese, R., Leser, U., Quilitz, B., Rothe, C.: Index Support for SPARQL. In: European Semantic Web Conference, Innsbruck, Austria (2007) Google Scholar
  16. 16.
    Lehman, P.L.: Efficient Locking for Concurrent Operations on B-Trees. ACM Transactions on Database Systems (TODS) 6(4), 650–670 (1981)CrossRefzbMATHGoogle Scholar
  17. 17.
    Bayer, R.: Symmetric Binary B-Trees: Data Structure and Maintenance Algorithms. Acta Informatica 1(4), 290–306 (1972)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Oracle Documents, (retrieved July 1, 2013)
  19. 19.
    Malewicz, G., Austern, M.H., Bik, A.J., Dehnert, J.C., Horn, I., Leiser, N., Czajkowski, G.: Pregel: A System for Large-Scale Graph Processing. In: The ACM SIGMOD International Conference on Management of Data, pp. 135–146 (2010)Google Scholar
  20. 20.
    Yang, S., Yan, X., Zong, B., Khan, A.: Towards effective partition management for large graphs. In: The ACM SIGMOD International Conference on Management of Data, pp. 517–528 (2012)Google Scholar
  21. 21.
    Semantic Web Challenge, (retrieved June 18, 2013)
  22. 22.
    W3C consortium, SPARQL 1.1 Property Paths, (retrieved July 18, 2013)
  23. 23.
    W3C consortium, SPARQL 1.1 Query Language, (retrieved July 18, 2013)
  24. 24.
    Web Data Commons, (retrieved July 25, 2013)

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ján Mojžiš
    • 1
  • Michal Laclavík
    • 1
  1. 1.Institute of InformaticsSlovak Academy of SciencesBratislavaSlovakia

Personalised recommendations