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Intelligent Clients for Replicated Triple Pattern Fragments

  • Thomas Minier
  • Hala Skaf-Molli
  • Pascal Molli
  • Maria-Esther Vidal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10843)

Abstract

Following the Triple Pattern Fragments (TPF) approach, intelligent clients are able to improve the availability of the Linked Data. However, data availability is still limited by the availability of TPF servers. Although some existing TPF servers belonging to different organizations already replicate the same datasets, existing intelligent clients are not able to take advantage of replicated data to provide fault tolerance and load-balancing. In this paper, we propose Ulysses, an intelligent TPF client that takes advantage of replicated datasets to provide fault tolerance and load-balancing. By reducing the load on a server, Ulysses improves the overall Linked Data availability and reduces data hosting cost for organizations. Ulysses relies on an adaptive client-side load-balancer and a cost-model to distribute the load among heterogeneous replicated TPF servers. Experimentations demonstrate that Ulysses reduces the load of TPF servers, tolerates failures and improves queries execution time in case of heavy loads on servers.

Keywords

Semantic web Triple Pattern Fragments Intelligent client Load balancing Fault tolerance Data replication 

Notes

Acknowledgments

This work is partially supported through the FaBuLA project, part of the AtlanSTIC 2020 program.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.LS2NUniversity of NantesNantesFrance
  2. 2.TIB Leibniz Information Centre for Science and TechnologyUniversity Library & Fraunhofer IAISSankt AugustinGermany

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