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On the Selection of SPARQL Endpoints to Efficiently Execute Federated SPARQL Queries

  • Maria-Esther Vidal
  • Simón Castillo
  • Maribel Acosta
  • Gabriela Montoya
  • Guillermo Palma
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9620)

Abstract

We consider the problem of source selection and query decomposition in federations of SPARQL endpoints, where query decompositions of a SPARQL query should reduce execution time and maximize answer completeness. This problem is in general intractable, and performance and answer completeness of SPARQL queries can be considerably affected when the number of SPARQL endpoints in a federation increases. We devise a formalization of this problem as the Vertex Coloring Problem and propose an approximate algorithm named Fed-DSATUR. We rely on existing results from graph theory to characterize the family of SPARQL queries for which Fed-DSATUR can produce optimal decompositions in polynomial time on the size of the query, i.e., on the number of SPARQL triple patterns in the query. Fed-DSATUR scales up much better to SPARQL queries with a large number of triple patterns, and may exhibit significant improvements in performance while answer completeness remains close to 100 %. More importantly, we put our results in perspective, and provide evidence of SPARQL queries that are hard to decompose and constitute new challenges for data management.

Keywords

SPARQL Query Query Plan Source Selection Triple Pattern Query Engine 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Supplementary material

References

  1. 1.
    Acosta, M., Vidal, M.-E., Lampo, T., Castillo, J., Ruckhaus, E.: ANAPSID: an adaptive query processing engine for SPARQL endpoints. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 18–34. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Buil-Aranda, C., Hogan, A., Umbrich, J., Vandenbussche, P.-Y.: SPARQL web-querying infrastructure: ready for action? In: Alani, H. (ed.) ISWC 2013, Part II. LNCS, vol. 8219, pp. 277–293. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Basca, C., Bernstein, A.: Querying a messy web of data with Avalanche. J. Web Semant. 26, 1–28 (2014)CrossRefGoogle Scholar
  4. 4.
    Brélaz, D.: New methods to color vertices of a graph. Commun. ACM 22(4), 251–256 (1979)CrossRefzbMATHGoogle Scholar
  5. 5.
    Broder, A.Z., Charikar, M., Frieze, A.M., Mitzenmacher, M.: Min-wise independent permutations. J. Comput. Syst. Sci. 60(3), 630–659 (2000)CrossRefMathSciNetzbMATHGoogle Scholar
  6. 6.
    Buil-Aranda, C., Arenas, M., Corcho, O.: Semantics and optimization of the SPARQL 1.1 federation extension. In: Antoniou, G., Grobelnik, M., Simperl, E., Parsia, B., Plexousakis, D., De Leenheer, P., Pan, J. (eds.) ESWC 2011, Part II. LNCS, vol. 6644, pp. 1–15. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Castillo, S., Palma, G., Vidal, M.: SILURIAN: a SPARQL visualizer for understanding queries and federations. In: Proceedings of the ISWC Posters and Demonstrations Track, pp. 137–140 (2013)Google Scholar
  8. 8.
    Florescu, D., Levy, A.Y., Mendelzon, A.O.: Database techniques for the world-wide web: a survey. SIGMOD Record 27(3), 59–74 (1998)CrossRefGoogle Scholar
  9. 9.
    Fundulaki, I., Auer, S.: Linked open data - introduction to the special theme. ERCIM News 96, 2014 (2014)Google Scholar
  10. 10.
    Görlitz, O., Staab, S.: SPLENDID: SPARQL endpoint federation exploiting VOID descriptions. In: Proceedings of the International Workshop on Consuming Linked Data (COLD) (2011)Google Scholar
  11. 11.
    Halevy, A.Y.: Answering queries using views: a survey. VLDB J. 10(4), 270–294 (2001)CrossRefzbMATHGoogle Scholar
  12. 12.
    Halevy, A.Y., Rajaraman, A., Ordille, J.J.: Data integration: the teenage years. In: Proceedings of the 32nd International Conference on Very Large Data Bases (VLDB), pp. 9–16 (2006)Google Scholar
  13. 13.
    Harth, A., Hose, K., Karnstedt, M., Polleres, A., Sattler, K.-U., Umbrich, J.: Data summaries for on-demand queries over linked data. In: Proceedings of the 19th International Conference on World Wide Web (WWW), pp. 411–420 (2010)Google Scholar
  14. 14.
    Ives, Z.G., Halevy, A.Y., Mork, P., Tatarinov, I.: Piazza: mediation and integration infrastructure for semantic web data. J. Web Semant. 1(2), 155–175 (2004)CrossRefGoogle Scholar
  15. 15.
    Janczewski, R., Kubale, M., Manuszewski, K., Piwakowski, K.: The smallest hard-to-color graph for algorithm DSATUR. Discrete Math. 236(1–3), 151–165 (2001)CrossRefMathSciNetzbMATHGoogle Scholar
  16. 16.
    Kaoudi, Z., Kyzirakos, K., Koubarakis, M.: SPARQL query optimization on top of DHTs. In: Patel-Schneider, P.F. (ed.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 418–435. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Lampo, T., Vidal, M.-E., Danilow, J., Ruckhaus, E.: To cache or not to cache: the effects of warming cache in complex SPARQL queries. In: Meersman, R., Dillon, T., Herrero, P., Kumar, A., Reichert, M., Qing, L., Ooi, B.-C., Damiani, E., Schmidt, D.C., White, J., Hauswirth, M., Hitzler, P., Mohania, M. (eds.) OTM 2011, Part II. LNCS, vol. 7045, pp. 716–733. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Li, Y., Heflin, J.: Using reformulation trees to optimize queries over distributed heterogeneous sources. In: Patel-Schneider, P.F. (ed.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 502–517. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  19. 19.
    Montoya, G., Vidal, M.-E., Corcho, O., Ruckhaus, E., Buil-Aranda, C.: Benchmarking federated SPARQL query engines: are existing testbeds enough? In: Cudré-Mauroux, P. (ed.) ISWC 2012, Part II. LNCS, vol. 7650, pp. 313–324. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  20. 20.
    Montoya, G., Vidal, M.-E., Acosta, M.: A heuristic-based approach for planning federated SPARQL queries. In: Proceedings of the International Workshop on Consuming Linked Data (COLD) (2012)Google Scholar
  21. 21.
    Pérez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. ACM Trans. Database Syst. 34(3), 16 (2009)CrossRefGoogle Scholar
  22. 22.
    Quilitz, B., Leser, U.: Querying distributed RDF data sources with SPARQL. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 524–538. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  23. 23.
    Saleem, M., Ngonga Ngomo, A.-C.: HiBISCuS: hypergraph-based source selection for SPARQL endpoint federation. In: Presutti, V., d’Amato, C., Gandon, F., d’Aquin, M., Staab, S., Tordai, A. (eds.) ESWC 2014. LNCS, vol. 8465, pp. 176–191. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  24. 24.
    Saleem, M., Ngonga Ngomo, A.-C., Xavier Parreira, J., Deus, H.F., Hauswirth, M.: DAW: duplicate-AWare federated query processing over the web of data. In: Alani, H., Kagal, L., Fokoue, A., Groth, P., Biemann, C., Parreira, J.X., Aroyo, L., Noy, N., Welty, C., Janowicz, K. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 574–590. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  25. 25.
    Schmachtenberg, M., Bizer, C., Paulheim, H.: Adoption of the linked data best practices in different topical domains. In: Mika, P. (ed.) ISWC 2014, Part I. LNCS, vol. 8796, pp. 245–260. Springer, Heidelberg (2014)Google Scholar
  26. 26.
    Schmidt, M., Görlitz, O., Haase, P., Ladwig, G., Schwarte, A., Tran, T.: FedBench: a benchmark suite for federated semantic data query processing. In: Aroyo, L. (ed.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 585–600. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  27. 27.
    Schwarte, A., Haase, P., Hose, K., Schenkel, R., Schmidt, M.: FedX: optimization techniques for federated query processing on linked data. In: Aroyo, L. (ed.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 601–616. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  28. 28.
    Segundo, P.S.: A new DSATUR-based algorithm for exact vertex coloring. Comput. Oper. 39(7), 1724–1733 (2012)CrossRefMathSciNetzbMATHGoogle Scholar
  29. 29.
    Vidal, M.-E., Ruckhaus, E., Lampo, T., Martínez, A., Sierra, J., Polleres, A.: Efficiently joining group patterns in SPARQL queries. In: Aroyo, L., Antoniou, G., Hyvönen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC 2010, Part I. LNCS, vol. 6088, pp. 228–242. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  30. 30.
    Wiederhold, G.: Mediators in the architecture of future information systems. IEEE Comput. 25(3), 38–49 (1992)CrossRefGoogle Scholar
  31. 31.
    Yuan, P., Liu, P., Wu, B., Jin, H., Zhang, W., Liu, L.: Triplebit: a fast and compact system for large scale RDF data. PVLDB 6(7), 517–528 (2013)Google Scholar
  32. 32.
    Zadorozhny, V., Raschid, L., Vidal, M.-E., Urhan, T., Bright, L.: Efficient evaluation of queries in a mediator for websources. In: Proceedings of the SIGMOD Conference, pp. 85–96 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Maria-Esther Vidal
    • 1
  • Simón Castillo
    • 1
  • Maribel Acosta
    • 2
  • Gabriela Montoya
    • 3
  • Guillermo Palma
    • 1
  1. 1.Universidad Simón BolívarCaracasVenezuela
  2. 2.Institute AIFBKarlsruhe Institute of TechnologyKarlsruheGermany
  3. 3.University of NantesNantesFrance

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