Skip to main content
View expanded cover

International Semantic Web Conference

ISWC 2017: The Semantic Web – ISWC 2017 pp 471–489Cite as

The Odyssey Approach for Optimizing Federated SPARQL Queries

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10587)

Abstract

Answering queries over a federation of SPARQL endpoints requires combining data from more than one data source. Optimizing queries in such scenarios is particularly challenging not only because of (i) the large variety of possible query execution plans that correctly answer the query but also because (ii) there is only limited access to statistics about schema and instance data of remote sources. To overcome these challenges, most federated query engines rely on heuristics to reduce the space of possible query execution plans or on dynamic programming strategies to produce optimal plans. Nevertheless, these plans may still exhibit a high number of intermediate results or high execution times because of heuristics and inaccurate cost estimations. In this paper, we present Odyssey, an approach that uses statistics that allow for a more accurate cost estimation for federated queries and therefore enables Odyssey to produce better query execution plans. Our experimental results show that Odyssey produces query execution plans that are better in terms of data transfer and execution time than state-of-the-art optimizers. Our experiments using the FedBench benchmark show execution time gains of at least 25 times on average.

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-68288-4_28
  • Chapter length: 19 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   89.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-68288-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   119.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.

Notes

  1. 1.

    FCSs describing entities across multiple datasets are very rare. In FedBench, for instance, they affect less than 0.5% of all CSs.

  2. 2.

    Implementation based on Java’s HashSet and HashMap was used to measure their sizes.

References

  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. LNCS, vol. 7031, pp. 18–34. Springer, Heidelberg (2011). doi:10.1007/978-3-642-25073-6_2

    CrossRef  Google Scholar 

  2. Alexander, K., Cyganiak, R., Hausenblas, M., Zhao, J.: Describing linked datasets. In: LDOW 2009 (2009)

    Google Scholar 

  3. Buil-Aranda, C., Hogan, A., Umbrich, J., Vandenbussche, P.-Y.: SPARQL web-querying infrastructure: ready for action? In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8219, pp. 277–293. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41338-4_18

    CrossRef  Google Scholar 

  4. Basca, C., Bernstein, A.: Querying a messy web of data with avalanche. J. Web Semant. 26, 1–28 (2014)

    CrossRef  Google Scholar 

  5. Charalambidis, A., Troumpoukis, A., Konstantopoulos, S.: SemaGrow: optimizing federated SPARQL queries. In: SEMANTICS 2015, pp. 121–128 (2015)

    Google Scholar 

  6. Du, F., Chen, Y., Du, X.: Partitioned indexes for entity search over RDF knowledge bases. In: Lee, S., Peng, Z., Zhou, X., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012. LNCS, vol. 7238, pp. 141–155. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29038-1_12

    CrossRef  Google Scholar 

  7. Görlitz, O., Staab, S.: SPLENDID: SPARQL endpoint federation exploiting VOID descriptions. In: COLD 2011 (2011)

    Google Scholar 

  8. Gubichev, A., Neumann, T.: Exploiting the query structure for efficient join ordering in SPARQL queries. In: EDBT 2014, pp. 439–450 (2014)

    Google Scholar 

  9. Hagedorn, S., Hose, K., Sattler, K., Umbrich, J.: Resource planning for SPARQL query execution on data sharing platforms. In: COLD, pp. 49–60 (2014)

    Google Scholar 

  10. Harth, A., Hose, K., Karnstedt, M., Polleres, A., Sattler, K., Umbrich, J.: Data summaries for on-demand queries over linked data. In: WWW 2010, pp. 411–420 (2010)

    Google Scholar 

  11. Meimaris, M., Papastefanatos, G., Mamoulis, N., Anagnostopoulos, I.: Extended characteristic sets: graph indexing for SPARQL query optimization. In: ICDE 2017 (2017)

    Google Scholar 

  12. Neumann, T., Moerkotte, G.: Characteristic sets: accurate cardinality estimation for RDF queries with multiple joins. In: ICDE 2011, pp. 984–994 (2011)

    Google Scholar 

  13. Prasser, F., Kemper, A., Kuhn, K.A.: Efficient distributed query processing for autonomous RDF databases. In: EDBT 2012, pp. 372–383 (2012)

    Google Scholar 

  14. 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). doi:10.1007/978-3-540-68234-9_39

    CrossRef  Google Scholar 

  15. 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, Cham (2014). doi:10.1007/978-3-319-07443-6_13

    CrossRef  Google Scholar 

  16. 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., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 585–600. Springer, Heidelberg (2011). doi:10.1007/978-3-642-25073-6_37

    CrossRef  Google Scholar 

  17. Schwarte, A., Haase, P., Hose, K., Schenkel, R., Schmidt, M.: FedX: optimization techniques for federated query processing on linked data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 601–616. Springer, Heidelberg (2011). doi:10.1007/978-3-642-25073-6_38

    CrossRef  Google Scholar 

  18. Stocker, M., Seaborne, A., Bernstein, A., Kiefer, C., Reynolds, D.: SPARQL basic graph pattern optimization using selectivity estimation. In: WWW 2008, pp. 595–604 (2008)

    Google Scholar 

  19. Wang, X., Tiropanis, T., Davis, H.C.: LHD: optimising linked data query processing using parallelisation. In: LDOW (2013)

    Google Scholar 

Download references

Acknowledgments

This research was partially funded by the Danish Council for Independent Research (DFF) under grant agreement no. DFF-4093-00301.

Author information

Authors and Affiliations

  1. Aalborg University, Aalborg, Denmark

    Gabriela Montoya & Katja Hose

  2. Nantes University, Nantes, France

    Hala Skaf-Molli

Authors
  1. Gabriela Montoya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hala Skaf-Molli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Katja Hose
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gabriela Montoya .

Editor information

Editors and Affiliations

  1. University of Bari, Bari, Italy

    Claudia d'Amato

  2. KMi, The Open University, Milton Keynes, United Kingdom

    Miriam Fernandez

  3. University of Liverpool, Liverpool, United Kingdom

    Valentina Tamma

  4. Accenture Technology Labs, Dublin, Ireland

    Freddy Lecue

  5. University of Fribourg, Fribourg, Switzerland

    Philippe Cudré-Mauroux

  6. Capsenta, Inc., Austin, Texas, USA

    Juan Sequeda

  7. Universität Bonn, Bonn, Germany

    Christoph Lange

  8. Lehigh University, Bethlehem, Pennsylvania, USA

    Jeff Heflin

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Montoya, G., Skaf-Molli, H., Hose, K. (2017). The Odyssey Approach for Optimizing Federated SPARQL Queries. In: , et al. The Semantic Web – ISWC 2017. ISWC 2017. Lecture Notes in Computer Science(), vol 10587. Springer, Cham. https://doi.org/10.1007/978-3-319-68288-4_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-68288-4_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68287-7

  • Online ISBN: 978-3-319-68288-4

  • eBook Packages: Computer ScienceComputer Science (R0)