Details
| Original language | English |
|---|---|
| Title of host publication | The Semantic Web |
| Subtitle of host publication | 15th International Conference, ESWC 2018, Proceedings |
| Editors | Aldo Gangemi, Raphaël Troncy, Roberto Navigli, Laura Hollink, Maria-Esther Vidal, Pascal Hitzler, Anna Tordai, Mehwish Alam |
| Publisher | Springer Verlag |
| Pages | 400-414 |
| Number of pages | 15 |
| ISBN (print) | 9783319934167 |
| Publication status | Published - 2018 |
| Event | 15th International Conference on Extended Semantic Web Conference, ESWC 2018 - Heraklion, Greece Duration: 3 Jun 2018 → 7 Jun 2018 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 10843 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
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
- Data replication, Fault tolerance, Intelligent client, Load balancing, Semantic web, Triple Pattern Fragments
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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The Semantic Web: 15th International Conference, ESWC 2018, Proceedings. ed. / Aldo Gangemi; Raphaël Troncy; Roberto Navigli; Laura Hollink; Maria-Esther Vidal; Pascal Hitzler; Anna Tordai; Mehwish Alam. Springer Verlag, 2018. p. 400-414 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10843 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Intelligent Clients for Replicated Triple Pattern Fragments
AU - Minier, Thomas
AU - Skaf-Molli, Hala
AU - Molli, Pascal
AU - Vidal, Maria Esther
N1 - Publisher Copyright: © 2018, Springer International Publishing AG, part of Springer Nature.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Data replication
KW - Fault tolerance
KW - Intelligent client
KW - Load balancing
KW - Semantic web
KW - Triple Pattern Fragments
UR - http://www.scopus.com/inward/record.url?scp=85048488935&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-93417-4_26
DO - 10.1007/978-3-319-93417-4_26
M3 - Conference contribution
AN - SCOPUS:85048488935
SN - 9783319934167
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 400
EP - 414
BT - The Semantic Web
A2 - Gangemi, Aldo
A2 - Troncy, Raphaël
A2 - Navigli, Roberto
A2 - Hollink, Laura
A2 - Vidal, Maria-Esther
A2 - Hitzler, Pascal
A2 - Tordai, Anna
A2 - Alam, Mehwish
PB - Springer Verlag
T2 - 15th International Conference on Extended Semantic Web Conference, ESWC 2018
Y2 - 3 June 2018 through 7 June 2018
ER -