Encyclopedia of Big Data Technologies

2019 Edition
| Editors: Sherif Sakr, Albert Y. Zomaya

Federated RDF Query Processing

  • Maribel AcostaEmail author
  • Olaf HartigEmail author
  • Juan SequedaEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-77525-8_228

Synonyms

Definitions

Federated RDF query processing is concerned with querying a federation of RDF data sources where the queries are expressed using a declarative query language (typically, the RDF query language SPARQL), and the data sources are autonomous and heterogeneous. The current literature in this context assumes that the data and the data sources are semantically homogeneous, while heterogeneity occurs at the level of data formats and access protocols.

Overview

In its initial version, the SPARQL query language did not have features to explicitly express queries over a federation of RDF data sources. To support querying such a federation without requiring the usage of specific language features, the following assumption has been made throughout the literature: The result of executing any given SPARQL query over the federation should be the same as if the query was executed over the union of all the RDF data available in all...

This is a preview of subscription content, log in to check access.

References

  1. Acosta M, Vidal M, Lampo T, Castillo J, Ruckhaus E (2011) ANAPSID: an adaptive query processing engine for SPARQL endpoints. In: The semantic web – ISWC 2011 – Proceedings of the 10th international semantic web conference, part I, Bonn, 23–27 Oct 2011, pp 18–34. https://doi.org/10.1007/978-3-642-25073-6_2CrossRefGoogle Scholar
  2. Akar Z, Halaç TG, Ekinci EE, Dikenelli O (2012) Querying the web of interlinked datasets using VOID descriptions. In: WWW2012 workshop on linked data on the web, Lyon, 16 Apr 2012. http://ceur-ws.org/Vol-937/ldow2012-paper-06.pdf
  3. Aranda CB, Arenas M, Corcho Ó, Polleres A (2013) Federating queries in SPARQL 1.1: syntax, semantics and evaluation. J Web Sem 18(1):1–17. https://doi.org/10.1016/j.websem.2012.10.001CrossRefGoogle Scholar
  4. Charalambidis A, Troumpoukis A, Konstantopoulos S (2015) Semagrow: optimizing federated SPARQL queries. In: Proceedings of the 11th international conference on semantic systems, SEMANTICS 2015, Vienna, 15–17 Sept 2015, pp 121–128. https://doi.org/10.1145/2814864.2814886
  5. Deshpande A, Ives ZG, Raman V (2007) Adaptive query processing. Found Trends Databases 1(1):1–140. https://doi.org/10.1561/1900000001zbMATHCrossRefGoogle Scholar
  6. Feigenbaum L, Williams GT, Clark KG, Torres E (2013) SPARQL 1.1 protocol. W3C recommendation. Online at https://www.w3.org/TR/sparql11-protocol/
  7. Glimm B, Ogbuji C (2013) SPARQL 1.1 entailment regimes. W3C recommendation. Online at https://www.w3.org/TR/sparql11-entailment/
  8. Görlitz O, Staab S (2011a) Federated data management and query optimization for linked open data. In: New directions in web data management 1. Springer, pp 109–137. https://doi.org/10.1007/978-3-642-17551-0_5Google Scholar
  9. Görlitz O, Staab S (2011b) SPLENDID: SPARQL endpoint federation exploiting VOID descriptions. In: Proceedings of the second international workshop on consuming linked data (COLD2011), Bonn, 23 Oct 2011. http://ceur-ws.org/Vol-782/GoerlitzAndStaab_COLD2011.pdf
  10. Harris S, Seaborne A, Prud’hommeaux E (2013) SPARQL 1.1 query language. W3C recommendation. Online at http://www.w3.org/TR/sparql11-query/
  11. Hartig O (2012) SPARQL for a web of linked data: semantics and computability. In: The semantic web: research and applications – Proceedings of the 9th extended semantic web conference, ESWC 2012, Heraklion, Crete, 27–31 May 2012, pp 8–23. https://doi.org/10.1007/978-3-642-30284-8_8Google Scholar
  12. Hartig O (2013) An overview on execution strategies for linked data queries. Datenbank-Spektrum 13(2):89–99. https://doi.org/10.1007/s13222-013-0122-1CrossRefGoogle Scholar
  13. Joshi AK, Jain P, Hitzler P, Yeh PZ, Verma K, Sheth AP, Damova M (2012) Alignment-based querying of linked open data. In: On the Move to Meaningful Internet Systems: OTM 2012, Confederated International Conferences: CoopIS, DOA-SVI, and ODBASE 2012, Rome, Italy, September 10–14, 2012. Proceedings, Part II, pp 807–824. https://doi.org/10.1007/978-3-642-33615-7_25Google Scholar
  14. Lynden SJ, Kojima I, Matono A, Tanimura Y (2010) Adaptive integration of distributed semantic web data. In: Databases in networked information systems, proceedings 6th international workshop, DNIS 2010, Aizu-Wakamatsu, 29–31 Mar 2010. pp 174–193. https://doi.org/10.1007/978-3-642-12038-1_12CrossRefGoogle Scholar
  15. Lynden SJ, Kojima I, Matono A, Tanimura Y (2011) ADERIS: an adaptive query processor for joining federated SPARQL endpoints. In: On the move to meaningful internet systems: OTM 2011 – Proceedings of the confederated international conferences: CoopIS, DOA-SVI, and ODBASE 2011, part II, Hersonissos, Crete, 17–21 Oct 2011, pp 808–817. https://doi.org/10.1007/978-3-642-25106-1_28CrossRefGoogle Scholar
  16. Mansour E, Abdelaziz I, Ouzzani M, Aboulnaga A, Kalnis P (2017) A demonstration of lusail: querying linked data at scale. In: Proceedings of the 2017 ACM international conference on management of data, SIGMOD conference 2017, Chicago, 14–19 May 2017, pp 1603–1606. https://doi.org/10.1145/3035918.3058731
  17. Oguz D, Ergenc B, Yin S, Dikenelli O, Hameurlain A (2015) Federated query processing on linked data: a qualitative survey and open challenges. Knowl Eng Rev 30(5):545–563. https://doi.org/10.1017/S0269888915000107CrossRefGoogle Scholar
  18. Pérez J, Arenas M, Gutierrez C (2009) Semantics and complexity of SPARQL. ACM Trans Database Syst 34(3):16:1–16:45. https://doi.org/10.1145/1567274.1567278CrossRefGoogle Scholar
  19. Prasser F, Kemper A, Kuhn KA (2012) Efficient distributed query processing for autonomous RDF databases. In: Proceedings of the 15th international conference on extending database technology, EDBT’12, Berlin, 27–30 Mar 2012, pp 372–383. https://doi.org/10.1145/2247596.2247640
  20. Prud’hommeaux E, Buil-Aranda C (2013) SPARQL 1.1 federated query. W3C recommendation. Online at https://www.w3.org/TR/sparql11-federated-query/
  21. Quilitz B, Leser U (2008) Querying distributed RDF data sources with SPARQL. In: The semantic web: research and applications, proceedings of the 5th European semantic web conference, ESWC 2008, Tenerife, Canary Islands, 1–5 June 2008, pp 524–538. https://doi.org/10.1007/978-3-540-68234-9_39
  22. Saleem M, Ngomo AN (2014) Hibiscus: hypergraph-based source selection for SPARQL endpoint federation. In: The semantic web: trends and challenges – proceedings of the 11th international conference, ESWC 2014, Anissaras, Crete, 25–29 May 2014, pp 176–191. https://doi.org/10.1007/978-3-319-07443-6_13Google Scholar
  23. Saleem M, Ngomo AN, Parreira JX, Deus HF, Hauswirth M (2013) DAW: duplicate-aware federated query processing over the web of data. In: The semantic web – ISWC 2013 – proceedings of the 12th international semantic web conference, part I, Sydney, 21–25 Oct 2013, pp 574–590. https://doi.org/10.1007/978-3-642-41335-3_36CrossRefGoogle Scholar
  24. Schwarte A, Haase P, Hose K, Schenkel R, Schmidt M (2011) Fedx: optimization techniques for federated query processing on linked data. In: The semantic web – ISWC 2011 – proceedings of the 10th international semantic web conference, part I, Bonn, 23–27 Oct 2011, pp 601–616. https://doi.org/10.1007/978-3-642-25073-6_38CrossRefGoogle Scholar
  25. Stolpe A (2015) A logical characterisation of SPARQL federation. Semantic Web 6(6):565–584. https://doi.org/10.3233/SW-140160MathSciNetCrossRefGoogle Scholar
  26. Verborgh R, Sande MV, Hartig O, Herwegen JV, Vocht LD, Meester BD, Haesendonck G, Colpaert P (2016) Triple pattern fragments: a low-cost knowledge graph interface for the web. J Web Sem 37–38:184–206. https://doi.org/10.1016/j.websem.2016.03.003CrossRefGoogle Scholar
  27. Vidal M, Ruckhaus E, Lampo T, Martínez A, Sierra J, Polleres A (2010) Efficiently joining group patterns in SPARQL queries. In: The semantic web: research and applications, proceedings of the 7th extended semantic web conference, part I, ESWC 2010, Heraklion, Crete, 30 May–3 June, 2010, pp 228–242. https://doi.org/10.1007/978-3-642-13486-9_16Google Scholar
  28. Vidal M, Castillo S, Acosta M, Montoya G, Palma G (2016) On the selection of SPARQL endpoints to efficiently execute federated SPARQL queries. Trans Large-Scale Data- Knowl Cent Syst 25:109–149. https://doi.org/10.1007/978-3-662-49534-6_4CrossRefGoogle Scholar
  29. Wang X, Tiropanis T, Davis HC (2013) LHD: optimising linked data query processing using parallelisation. In: Proceedings of the WWW2013 workshop on linked data on the web, Rio de Janeiro, 14 May 2013. http://ceur-ws.org/Vol-996/papers/ldow2013-paper-06.pdf
  30. Wang X, Tiropanis T, Davis HC (2014) Optimising linked data queries in the presence of co-reference. In: The semantic web: trends and challenges – proceedings of the 11th international conference, ESWC 2014, Anissaras, Crete, 25–29 May 2014, pp 442–456. https://doi.org/10.1007/978-3-319-07443-6_30Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute AIFBKarlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.Linköping UniversityLinköpingSweden
  3. 3.CapsentaAustinUSA