How Good Is Your SPARQL Endpoint?
- Muhammad Intizar AliAffiliated withInsight Centre for Data Analytics, National University of Ireland
- , Alessandra MileoAffiliated withInsight Centre for Data Analytics, National University of Ireland
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.
- How Good Is Your SPARQL Endpoint?
- Book Title
- On the Move to Meaningful Internet Systems: OTM 2014 Conferences
- Book Subtitle
- Confederated International Conferences: CoopIS, and ODBASE 2014, Amantea, Italy, October 27-31, 2014, Proceedings
- pp 491-508
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
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- Editor Affiliations
- 15. TU Graz
- 16. CRAN, Campus Sciences, University of Lorraine
- 17. Computer Science and Computer Engineering, La Trobe University
- 18. Laboratory for Enterprise Knowledge and Systems (LEKS), IASI - CNR
- 19. School of Software, Tsinghua University
- 20. PROS Research Center, Camino de Vera, Universidad Politècnica de València
- 21. Department of Electronics, Computer Science and Systems, Univeristy of Calabria
- 22. School of Computer Science and Information Technology, RMIT
- Author Affiliations
- 23. Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland
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