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Intelligent clients for replicated Triple Pattern Fragments

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.
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https://hal.archives-ouvertes.fr/hal-01789409
Contributor : Thomas Minier <>
Submitted on : Thursday, May 10, 2018 - 3:27:50 PM
Last modification on : Thursday, February 27, 2020 - 1:19:34 AM
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Thomas Minier, Hala Skaf-Molli, Pascal Molli, Maria-Esther Vidal. Intelligent clients for replicated Triple Pattern Fragments. 15th Extended Semantic Web Conference (ESWC 2018), Jun 2018, Heraklion, Greece. pp.400-414, ⟨10.1007/978-3-319-93417-4_26⟩. ⟨hal-01789409⟩

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