Skip to main content
SpringerLink
Log in

How Good Is Your SPARQL Endpoint?

A QoS-Aware SPARQL Endpoint Monitoring and Data Source Selection Mechanism for Federated SPARQL Queries

  • Conference paper
On the Move to Meaningful Internet Systems: OTM 2014 Conferences (OTM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8841))

Abstract

Due to the decentralised and autonomous architecture of the Web of Data, data replication and local deployment of SPARQL endpoints is inevitable. Nowadays, it is common to have multiple copies of the same dataset accessible by various SPARQL endpoints, thus leading to the problem of selecting optimal data source for a user query based on data properties and requirements of the user or the application. Quality of Service (QoS) parameters can play a pivotal role for the selection of optimal data sources according to the user’s requirements. QoS parameters have been widely studied in the context of web service selection. However, to the best of our knowledge, the potential of associating QoS parameters to SPARQL endpoints for optimal data source selection has not been investigated.

In this paper, we define various QoS parameters associated with the SPARQL endpoints and represent a semantic model for QoS parameters and their evaluation. We present a monitoring service for the SPARQL endpoint which automatically evaluates the QoS metrics of any given SPARQL endpoint. We demonstrate the utility of our monitoring service by implementing an extension of the SPARQL query language, which caters for user requirements based on QoS parameters and selects the optimal data source for a particular user query over federated sources.

This research has been partially supported by Science Foundation Ireland (SFI) under grant No. SFI/12/RC/2289 and EU FP7 CityPulse Project under grant No.603095. http://www.ict-citypulse.eu

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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, Part I. LNCS, vol. 7031, pp. 18–34. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  2. Alexander, K., Hausenblas, M.: Describing linked datasets, the vocabulary of interlinked datasets. In: Proc. of LDOW, WWW (2009)

    Google Scholar 

  3. Ali, M.I., Pichler, R., Truong, H.L., Dustdar, S.: Data concern aware querying for the integration of data services. In: Proc. of ICEIS (1), pp. 111–119 (2011)

    Google Scholar 

  4. Ali, M.I., Pichler, R., Truong, H.-L., Dustdar, S.: Incorporating data concerns into query languages for data services. In: Zhang, R., Zhang, J., Zhang, Z., Filipe, J., Cordeiro, J. (eds.) ICEIS 2011. LNBIP, vol. 102, pp. 132–145. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  5. Aranda, C.B., Hogan, A., Umbrich, J., Vandenbussche, P.-Y.: Sparql web-querying infrastructure: Ready for action? In: Proc. of ISWC (2), pp. 277–293 (2013)

    Google Scholar 

  6. Gallego, M.A., Fernández, J.D., Martínez-Prieto, M.A., de la Fuente, P.: An empirical study of real-world sparql queries. In: Proc. of USEWOD 2011, at WWW (2011)

    Google Scholar 

  7. Görlitz, O., Staab, S.: Splendid: Sparql endpoint federation exploiting void descriptions. In: Proc. of COLD, vol. 782 (2011)

    Google Scholar 

  8. Guo, G., Yu, F., Chen, Z., Xie, D.: A method for semantic web service selection based on qos ontology. Journal of Computers 6(2) (2011)

    Google Scholar 

  9. Hose, K., Schenkel, R.: Towards benefit-based rdf source selection for sparql queries. In: Proc. of SWIM, p. 2 (2012)

    Google Scholar 

  10. Huang, A.F., Lan, C.-W., Yang, S.J.: An optimal qos-based web service selection scheme. Journal of Information Sciences 179(19), 3309–3322 (2009)

    Article  Google Scholar 

  11. Kritikos, K., Pernici, B., Plebani, P., Cappiello, C., Comuzzi, M., Benrernou, S., Brandic, I., Kertész, A., Parkin, M., Carro, M.: A survey on service quality description. ACM Computing Surveys (CSUR) 46(1), 1 (2013)

    Article  Google Scholar 

  12. Mobedpour, D., Ding, C., Chi, C.-H.: A qos query language for user-centric web service selection. In: Proc. of SCC 2010, pp. 273–280. IEEE (2010)

    Google Scholar 

  13. 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)

    Google Scholar 

  14. Rafique, I., Lew, P., Abbasi, M.Q., Li, Z.: Information quality evaluation framework: Extending iso 25012 data quality model. In: Proc. of World Academy of Science, Engineering and Technology, vol. 65 (2012)

    Google Scholar 

  15. Saleem, M., Ngonga Ngomo, A.-C., Xavier Parreira, J., Deus, H.F., Hauswirth, M.: DAW: Duplicate-aWare federated query processing over the web of data. In: Alani, H., et al. (eds.) ISWC 2013, Part I. LNCS, vol. 8218, pp. 574–590. Springer, Heidelberg (2013)

    Chapter  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, Part I. LNCS, vol. 7031, pp. 585–600. Springer, Heidelberg (2011)

    Chapter  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, Part I. LNCS, vol. 7031, pp. 601–616. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  18. Truong, H.L., Dustdar, S.: On evaluating and publishing data concerns for data as a service. In: Proc. of APSCC, pp. 363–370 (2010)

    Google Scholar 

  19. Umbrich, J., Karnstedt, M., Hogan, A., Parreira, J.X.: Hybrid SPARQL queries: Fresh vs. Fast results. In: Cudré-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., Bernstein, A., Blomqvist, E. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 608–624. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  20. Wang, X., Vitvar, T., Kerrigan, M., Toma, I.: A qos-aware selection model for semantic web services. In: Dan, A., Lamersdorf, W. (eds.) ICSOC 2006. LNCS, vol. 4294, pp. 390–401. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland

    Muhammad Intizar Ali & Alessandra Mileo

Authors
  1. Muhammad Intizar Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alessandra Mileo
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. TU Graz, Rechbauerstraße 12, 8010, Graz, Austria

    Robert Meersman

  2. CRAN, Campus Sciences, University of Lorraine, BP 70239, 54506, Vandoevre-les-Nancy, France

    Hervé Panetto

  3. Computer Science and Computer Engineering, La Trobe University, 3086, Melbourne, VIC, Australia

    Tharam Dillon

  4. Laboratory for Enterprise Knowledge and Systems (LEKS), IASI - CNR, Viale Manzoni 30, 00185, Rome, Italy

    Michele Missikoff

  5. School of Software, Tsinghua University, 100084, Beijing, China

    Lin Liu

  6. PROS Research Center, Camino de Vera, Universidad Politècnica de València, 46022, Valencia, Spain

    Oscar Pastor

  7. Department of Electronics, Computer Science and Systems, Univeristy of Calabria, Via P. Bucci, 41C, 87036, Rende, Cosenza, Italy

    Alfredo Cuzzocrea

  8. School of Computer Science and Information Technology, RMIT, Australia

    Timos Sellis

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ali, M.I., Mileo, A. (2014). How Good Is Your SPARQL Endpoint?. In: Meersman, R., et al. On the Move to Meaningful Internet Systems: OTM 2014 Conferences. OTM 2014. Lecture Notes in Computer Science, vol 8841. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45563-0_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-45563-0_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-45562-3

  • Online ISBN: 978-3-662-45563-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation