Algebra of RDF Graphs for Querying Large-Scale Distributed Triple-Store

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9817)

Abstract

Large-scale RDF graph databases stored in shared-nothing clusters require query processing engine that can effectively exploit highly parallel computation environment. We propose algebra of RDF graphs and its physical counterpart, physical algebra of RDF graphs, designed to implement queries as distributed dataflow programs that run on cluster of servers. Operations of algebra reflect the characteristic features of RDF graph data model while they are tied to the technology provided by relational query execution systems. Algebra of RDF graphs allows for the expression of pipelined and partitioned parallelism. Preliminary experimental results show that proposed algebra and architecture of query execution system scale well with large clusters of data servers.

References

  1. 1.
    Angles, R., Gutierrez, C.: The expressive power of SPARQL. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 114–129. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Armstrong, J.: Programming Erlang: Software for a Concurrent World. Pragmatic Bookshelf, Raleigh (2013)Google Scholar
  3. 3.
    Babu, S., Herodotou, H.: Massively parallel databases and mapreduce systems. Found. Trendsin Databases 5(1), 1–104 (2012)CrossRefGoogle Scholar
  4. 4.
    Carey, M.J., DeWitt, D.J., Richardson, J.E., Shekita, E.J.: Storage management for objects in exodus. In: Kim, W., Lochovsky, F. (eds.) Object-Oriented Concepts, Applications, and Databases. Addison-Wesley Publishing Co. (1988)Google Scholar
  5. 5.
    Codd, E.F.: A relational model of data for large shared data banks. Commun. ACM 13(6), 377–387 (1970)CrossRefMATHGoogle Scholar
  6. 6.
    Cyganiak, R.: A relational algebra for SPARQL (2005)Google Scholar
  7. 7.
    DeWitt, D., Gray, J.: Parallel database systems: the future of high performance database processing. Commun. ACM 36(6), 85–98 (1992)CrossRefGoogle Scholar
  8. 8.
    Dong, X.L., Gabrilovich, E., Heitz, G., Horn, W., Lao, N., Murphy, K., Strohmann, T., Sun, S., Zhang, W.: Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: KDD2014, ACM (2014)Google Scholar
  9. 9.
    Erling, O., Mikhailov, I.: RDF support in the virtuoso DBMS. In: Pellegrini, T., Auer, S., Tochtermann, K., Schaffert, S. (eds.) Networked Knowledge - Networked Media. SCI, vol. 221, pp. 7–24. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Graefe, G.: Query evaluation techniques for large databases. ACM Comput. Surv. 25(2), 73–169 (1993)CrossRefGoogle Scholar
  11. 11.
    Graefe, G.: Dynamic query evaluation plans: some course corrections? IEEE Data Eng. Bull. 23(2), 3–6 (2000)Google Scholar
  12. 12.
    Hoffart, J., Suchanek, F.M., Berberich, K., Weikum, G.: Yago2: a spatially and temporally enhanced knowledge base from wikipedia. Artif. Intell. 194, 28–61 (2013). Artificial Intelligence, Wikipedia and Semi-Structured ResourcesMathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Neumann, T., Weikum, G.: RDF-3X: a risc-style engine for RDF. Proc. VLDB Endow. 1(1), 647–659 (2008)CrossRefGoogle Scholar
  14. 14.
    Savnik, I., Nitta, K.: Semantic partitioning method for very large rdf graphs. University of Primorska, Technical report (In preparation), FAMNIT (2014)Google Scholar
  15. 15.
    Schmidt, M., Meier, M., Lausen, G.: Foundations of SPARQL query optimization. In: Proceedings of the 13th International Conference on Database Theory, ICDT 2010, pp. 4–33. ACM, New York (2010)Google Scholar
  16. 16.
    Stonebraker, M.: The case for shared nothing. Database Eng. Bull. 9(1), 4–9 (1986)Google Scholar
  17. 17.
    White, T.: Hadoop: The Definitive Guide. O’Reilly Media Inc., Firenze (2009)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  1. 1.Jožef Stefan InstituteUniversity of PrimorskaKoperSlovenia
  2. 2.Yahoo JAPAN ResearchTokyoJapan

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