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Sparklify: A Scalable Software Component for Efficient Evaluation of SPARQL Queries over Distributed RDF Datasets

  • Claus StadlerEmail author
  • Gezim Sejdiu
  • Damien Graux
  • Jens Lehmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11779)

Abstract

One of the key traits of Big Data is its complexity in terms of representation, structure, or formats. One existing way to deal with it is offered by Semantic Web standards. Among them, RDF – which proposes to model data with triples representing edges in a graph – has received a large success and the semantically annotated data has grown steadily towards a massive scale. Therefore, there is a need for scalable and efficient query engines capable of retrieving such information. In this paper, we propose Sparklify: a scalable software component for efficient evaluation of SPARQL queries over distributed RDF datasets. It uses Sparqlify as a SPARQL-to-SQL rewriter for translating SPARQL queries into Spark executable code. Our preliminary results demonstrate that our approach is more extensible, efficient, and scalable as compared to state-of-the-art approaches. Sparklify is integrated into a larger SANSA framework and it serves as a default query engine and has been used by at least three external use scenarios.

Resource type Software Framework

Website http://sansa-stack.net/sparklify/

Permanent URL  https://doi.org/10.6084/m9.figshare.7963193

Notes

Acknowledgment

This work was partly supported by the EU Horizon2020 projects BigDataOcean (GA no. 732310), Boost4.0 (GA no. 780732), SLIPO (GA no. 731581) and QROWD (GA no. 723088); and by the ADAPT Centre for Digital Content Technology funded under the SFI Research Centres Programme (Grant 13/RC/2106) and co-funded under the European Regional Development Fund.

References

  1. 1.
    Aluç, G., Hartig, O., Özsu, M.T., Daudjee, K.: Diversified stress testing of RDF data management systems. In: Mika, R., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 197–212. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11964-9_13CrossRefGoogle Scholar
  2. 2.
    Armbrust, M., et al.: Spark SQL: relational data processing in Spark. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, SIGMOD 2015, pp. 1383–1394. ACM, New York (2015)Google Scholar
  3. 3.
    Calvanese, D., et al.: Ontop: answering SPARQL queries over relational databases. Semant. Web 8, 471–487 (2017)CrossRefGoogle Scholar
  4. 4.
    Erling, O., Mikhailov, I.: Virtuoso: RDF support in a native RDBMS. In: de Virgilio, R., Giunchiglia, F., Tanca, L. (eds.) Semantic Web Information Management, pp. 501–519. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-04329-1_21CrossRefGoogle Scholar
  5. 5.
    Ermilov, I., et al.: The Tale of Sansa Spark. In 16th International Semantic Web Conference, Poster & Demos (2017)Google Scholar
  6. 6.
    Faye, D.C., Curé, O., Blin, G.: A survey of RDF storage approaches. Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées 15, 11–35 (2012)Google Scholar
  7. 7.
    Graux, D., Jachiet, L., Genevès, P., Layaïda, N.: SPARQLGX: efficient distributed evaluation of SPARQL with Apache Spark. In: Groth, P., et al. (eds.) The Semantic Web - ISWC 2016. LNCS, pp. 80–87. Springer International Publishing, Cham (2016).  https://doi.org/10.1007/978-3-319-46547-0_9CrossRefGoogle Scholar
  8. 8.
    Graux, D., Jachiet, L., Geneves, P., Layaïda, N.: A multi-criteria experimental ranking of distributed SPARQL evaluators. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 693–702. IEEE (2018)Google Scholar
  9. 9.
    Guo, Y., Pan, Z., Heflin, J.: LUBM: a benchmark for owl knowledge base systems. J. Web Semant. 3, 158–182 (2005)CrossRefGoogle Scholar
  10. 10.
    Kaoudi, Z., Manolescu, I.: RDF in the clouds: a survey. VLDB J.-Int. J. Very Large Data Bases 24(1), 67–91 (2015)CrossRefGoogle Scholar
  11. 11.
    Lehmann, J., et al.: Distributed semantic analytics using the SANSA stack. In: d’Amato, C., et al. (eds.) ISWC 2017. LNCS, vol. 10588, pp. 147–155. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-68204-4_15CrossRefGoogle Scholar
  12. 12.
    Neumann, T., Weikum, G.: RDF-3X: a RISC-style engine for RDF. Proc. VLDB Endow. 1(1), 647–659 (2008)CrossRefGoogle Scholar
  13. 13.
    Punnoose, R., Crainiceanu, A., Rapp, D.: Rya: a scalable RDF triple store for the clouds. In: Proceedings of the 1st International Workshop on Cloud Intelligence, Cloud-I 2012, pp. 4:1–4:8. ACM, New York (2012)Google Scholar
  14. 14.
    Rohloff, K., Schantz, R.E.: High-performance, massively scalable distributed systems using the MapReduce software framework: the SHARD triple-store. In: Programming Support Innovations for Emerging Distributed Applications, PSI EtA 2010, pp. 4:1–4:5. ACM, New York (2010)Google Scholar
  15. 15.
    Schätzle, A., Przyjaciel-Zablocki, M., Lausen, G.: PigSPARQL: mapping SPARQL to Pig Latin. In: Proceedings of the International Workshop on Semantic Web Information Management, SWIM 2011, pp. 4:1–4:8. ACM, New York (2011)Google Scholar
  16. 16.
    Schätzle, A., Przyjaciel-Zablocki, M., Neu, A., Lausen, G.: Sempala: interactive SPARQL query processing on Hadoop. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8796, pp. 164–179. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11964-9_11CrossRefGoogle Scholar
  17. 17.
    Schätzle, A., Przyjaciel-Zablocki, M., Skilevic, S., Lausen, G.: S2RDF: RDF querying with SPARQL on Spark. Proc. VLDB Endow. 9(10), 804–815 (2016)CrossRefGoogle Scholar
  18. 18.
    Stadler, C., Unbehauen, J., Westphal, P., Sherif, M.A., Lehmann, J.: Simplified RDB2RDF mapping. In: Proceedings of the 8th Workshop on Linked Data on the Web, LDOW 2015, Florence, Italy (2015)Google Scholar
  19. 19.
    Zaharia, M., et al.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation. USENIX (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Claus Stadler
    • 1
    Email author
  • Gezim Sejdiu
    • 2
  • Damien Graux
    • 3
    • 4
  • Jens Lehmann
    • 2
    • 3
  1. 1.Institute for Applied Informatics (InfAI)University of LeipzigLeipzigGermany
  2. 2.Smart Data AnalyticsUniversity of BonnBonnGermany
  3. 3.Enterprise Information SystemsFraunhofer IAISSankt AugustinGermany
  4. 4.ADAPT Centre, Trinity College of DublinDublinIreland

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