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SPARQ\(\lambda \): A Functional Perspective on Linked Data Services

  • Christian VogelgesangEmail author
  • Torsten Spieldenner
  • René Schubotz
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11341)

Abstract

With more and more applications providing semantic data to improve interoperability, the amount of available RDF datasets is constantly increasing. The SPARQL query language is a W3C recommendation to provide query capabilities on such RDF datasets. Data integration from different RDF sources is up to now mostly task of RDF consuming clients. However, from a functional perspective, data integration boils down to a function application that consumes input data as parameters, and based on these, produces a new set of data as output. Following this notion, we introduce SPARQ\(\lambda \), an extension to the SPARQL 1.1 query language. SPARQ\(\lambda \) enables dynamic injection of RDF datasets during evaluation of the query, and by this lifts SPARQL to a tool to write templates for RDF producing functions, an important step to reduce the effort to write SPARQL queries that work on data from various sources. SPARQ\(\lambda \) is moreover suitable to directly translate to an RDF described Web service interface, which allows to lift integration of data and re-provisioning of integrated results from clients to cloud environments, and by this solving the bottleneck of RDF data integration on client side.

Keywords

SPARQL Data integration RDF Functional programming 
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Notes

Acknowledgment

This work is supported by the Federal Ministry of Education and Research of Germany in the project Hybr-iT (Förderkennzeichen 01IS16026A).

References

  1. 1.
  2. 2.
    Shape Expressions Language 2.0. https://www.w3.org/TR/shex-semantics/
  3. 3.
    Shapes Constraint Language (SHACL). https://www.w3.org/TR/shacl/
  4. 4.
  5. 5.
  6. 6.
  7. 7.
  8. 8.
    Abdelaziz, I., Harbi, R., Khayyat, Z., Kalnis, P.: A survey and experimental comparison of distributed SPARQL engines for very large RDF data. Proc. VLDB Endow. 10(13), 2049–2060 (2017)CrossRefGoogle Scholar
  9. 9.
    Atzori, M.: Call: a nucleus for a web of open functions. In: Proceedings of the 2014 International Conference on Posters & Demonstrations Track, ISWC-PD 2014, vol. 1272, pp. 17–20. CEUR-WS.org, Aachen (2014)Google Scholar
  10. 10.
    Atzori, M.: Toward the web of functions: interoperable higher-order functions in SPARQL. In: Mika, P., et al. (eds.) ISWC 2014. LNCS, vol. 8797, pp. 406–421. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11915-1_26CrossRefGoogle Scholar
  11. 11.
    Baldini, I., et al.: Serverless computing: current trends and open problems. In: Chaudhary, S., Somani, G., Buyya, R. (eds.) Research Advances in Cloud Computing, pp. 1–20. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-5026-8_1CrossRefGoogle Scholar
  12. 12.
    Barbieri, D.F., Braga, D., Ceri, S., Valle, E.D., Grossniklaus, M.: C-SPARQL: a continuous query language for RDF data streams. Int. J. Semant. Comput. 4(01), 3–25 (2010)CrossRefGoogle Scholar
  13. 13.
    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).  https://doi.org/10.1007/978-3-642-41338-4_18CrossRefGoogle Scholar
  14. 14.
    Daga, E., Panziera, L., Pedrinaci, C.: A BASILar approach for building web APIs on top of SPARQL endpoints. In: CEUR Workshop Proceedings, vol. 1359, pp. 22–32 (2015)Google Scholar
  15. 15.
    Dia, A.F., Kazi-Aoul, Z., Boly, A., Chabchoub, Y.: C-SPARQL extension for sampling RDF graphs streams. In: Pinaud, B., Guillet, F., Cremilleux, B., de Runz, C. (eds.) Advances in Knowledge Discovery and Management. SCI, vol. 732, pp. 23–40. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-65406-5_2CrossRefGoogle Scholar
  16. 16.
    Fafalios, P., Tzitzikas, Y.: SPARQL-LD: a SPARQL extension for fetching and querying linked data. In: International Semantic Web Conference (Posters & Demos) (2015)Google Scholar
  17. 17.
    Fafalios, P., Yannakis, T., Tzitzikas, Y.: Querying the web of data with SPARQL-LD. In: Fuhr, N., Kovács, L., Risse, T., Nejdl, W. (eds.) TPDL 2016. LNCS, vol. 9819, pp. 175–187. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-43997-6_14CrossRefGoogle Scholar
  18. 18.
    Fox, G.C., Ishakian, V., Muthusamy, V., Slominski, A.: Status of serverless computing and function-as-a-service (FaaS) in industry and research. arXiv preprint arXiv:1708.08028 (2017)
  19. 19.
    Jones, N.D., Gomard, C.K., Sestoft, P.: Partial Evaluation and Automatic Program Generation. Prentice-Hall International Series in Computer Science. Prentice Hall, New York (1993)zbMATHGoogle Scholar
  20. 20.
    Leng, Y., Zhikui, C., Zhong, F., Li, X., Hu, Y., Yang, C.: BRGP: a balanced RDF graph partitioning algorithm for cloud storage. Concurrency Comput.: Practice Exp. 29(14), e3896 (2017)CrossRefGoogle Scholar
  21. 21.
    Michel, F., Faron-Zucker, C., Gandon, F.: Bridging web APIs and linked data with SPARQL micro-services. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 11155, pp. 187–191. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-98192-5_35CrossRefGoogle Scholar
  22. 22.
    Michel, F., Zucker, C.F., Gandon, F.: SPARQL micro-services: lightweight integration of web APIs and linked data. In: Linked Data on the Web, LDOW 2018, pp. 1–10 (2018)Google Scholar
  23. 23.
    Millard, I., Glaser, H., Salvadores, M., Shadbolt, N.: Consuming multiple linked data sources: challenges and experiences (2010)Google Scholar
  24. 24.
    Pérez, J., Arenas, M., Gutierrez, C.: Semantics and complexity of SPARQL. In: Cruz, I., et al. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 30–43. Springer, Heidelberg (2006).  https://doi.org/10.1007/11926078_3CrossRefGoogle Scholar
  25. 25.
    Rakhmawati, N.A., Umbrich, J., Karnstedt, M., Hasnain, A., Hausenblas, M.: A comparison of federation over SPARQL endpoints frameworks. In: Klinov, P., Mouromtsev, D. (eds.) KESW 2013. CCIS, vol. 394, pp. 132–146. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-41360-5_11CrossRefGoogle Scholar
  26. 26.
    Rietveld, L., Verborgh, R., Beek, W., Vander Sande, M., Schlobach, S.: Linked data-as-a-service: the semantic web redeployed. In: Gandon, F., Sabou, M., Sack, H., d’Amato, C., Cudré-Mauroux, P., Zimmermann, A. (eds.) ESWC 2015. LNCS, vol. 9088, pp. 471–487. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-18818-8_29CrossRefGoogle Scholar
  27. 27.
    Stadtmüller, S., Speiser, S., Harth, A.: Future challenges for linked APIs. In: SALAD@ ESWC, pp. 20–27 (2013)Google Scholar
  28. 28.
    Verborgh, R., Vander Sande, M., Colpaert, P., Coppens, S., Mannens, E., Van de Walle, R.: Web-scale querying through linked data fragments. In: LDOW. Citeseer (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Christian Vogelgesang
    • 1
    Email author
  • Torsten Spieldenner
    • 1
  • René Schubotz
    • 1
  1. 1.Saarbrücken Graduate School of Computer ScienceGerman Research Center for Artifical Intelligence (DFKI)SaarbrückenGermany

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