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

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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXV

Part of the book series: Lecture Notes in Computer Science ((TLDKS,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.

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Notes

  1. 1.

    http://www.earthobservatory.eu/.

  2. 2.

    https://www.openphacts.org/.

  3. 3.

    http://geoknow.eu/.

  4. 4.

    http://www.w3.org/TR/rdf-sparql-protocol/.

  5. 5.

    http://lov.okfn.org/dataset/lov.

  6. 6.

    http://xmlns.com/foaf/spec/.

  7. 7.

    http://www.w3.org/TR/void/.

  8. 8.

    http://www.linkedmdb.org/.

  9. 9.

    http://dbpedia.org/About.

  10. 10.

    http://www.geonames.org/.

  11. 11.

    http://data.nytimes.com/.

  12. 12.

    http://data.semanticweb.org/.

  13. 13.

    http://dbtune.org/jamendo/.

  14. 14.

    http://www.drugbank.ca/.

  15. 15.

    http://www.genome.jp/kegg/.

  16. 16.

    http://www.ebi.ac.uk/chebi/.

  17. 17.

    SPARQL queries with different SPARQL operators, e.g., UNION or OPTIONAL.

  18. 18.

    http://iwb.fluidops.com:7879/resource/Datasets, November 2011.

  19. 19.

    pred(t) returns the predicate of the triple pattern t.

  20. 20.

    cost(D) is monotonic w.r.t. number of subgoals in DP, if and only if, the values of cost(D) monotonically increase with the number of subgoals in DP, i.e., for all \(D=(DP,f,g)\) and \(D'=(DP',f',g')\) such that \(\mid DP \mid < \mid DP' \mid \) one has cost(D) \(<\) \(\textit{cost}(D')\). A sufficient condition for the function cost(.) to be monotonic is that the query comprises only triple pattern that can be evaluated against only one endpoint.

  21. 21.

    http://iwb.fluidops.com:7879/resource/Datasets, November 2011.

  22. 22.

    http://silurian.thalassa.cbm.usb.ve/.

  23. 23.

    As indicated in Theorem 2, these decompositions can be optimal depending on the property of monotonicity of the function cost(.).

  24. 24.

    http://jena.sourceforge.net/ARQ.

  25. 25.

    https://github.com/seagent/WoDQA/.

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Author information

Authors and Affiliations

  1. Universidad Simón Bolívar, Caracas, Venezuela

    Maria-Esther Vidal, Simón Castillo & Guillermo Palma

  2. Institute AIFB, Karlsruhe Institute of Technology, Karlsruhe, Germany

    Maribel Acosta

  3. University of Nantes, Nantes, France

    Gabriela Montoya

Authors
  1. Maria-Esther Vidal
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  2. Simón Castillo
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  3. Maribel Acosta
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  4. Gabriela Montoya
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  5. Guillermo Palma
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Corresponding author

Correspondence to Maria-Esther Vidal .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Josef Küng

  3. FAW, University of Linz, Linz, Austria

    Roland Wagner

A Additional Complex Queries

A Additional Complex Queries

We have defined a set of ten additional queries which comprise a large number of triple patterns, basic graph patterns, and different SPARQL operators. Extended setup evaluates the effects of selectivity of BGPs, large number of triple patterns, and number of SPARQL operators. The additionally queries are composed of between 6 and 46 triple patterns and can be decomposed into up to 9 subqueries.

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Vidal, ME., Castillo, S., Acosta, M., Montoya, G., Palma, G. (2016). On the Selection of SPARQL Endpoints to Efficiently Execute Federated SPARQL Queries. In: Hameurlain, A., Küng, J., Wagner, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXV. Lecture Notes in Computer Science(), vol 9620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49534-6_4

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  • DOI: https://doi.org/10.1007/978-3-662-49534-6_4

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