Strider R: Massive and Distributed RDF Graph Stream Reasoning

Xiangnan Ren 1, 2 Olivier Curé 1 Hubert Naacke 3 Jérémy Lhez 1 Ke Li 2
1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 Atos
3 BD - Bases de Données
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Reasoning over semantically annotated data is an emerging trend in stream processing aiming to produce sound and complete answers to a set of continuous queries. It usually comes at the cost of finding a trade-off between data throughput and the cost of expressive inferences. Strider R proposes such a trade-off and combines a scalable RDF stream processing engine with an efficient reasoning system. The main reasoning tasks are based on a query rewriting approach for SPARQL that benefits from an intelligent encoding of an extension of the RDFS (i.e., RDFS with owl:sameAs) ontology elements. Strider R runs in production at a major international water management company to detect anomalies from sensor streams. The system is evaluated along different dimensions and over multiple datasets to emphasize its performance.
Document type :
Conference papers
Complete list of metadatas
https://hal.sorbonne-universite.fr/hal-01657494
Contributor : Hubert Naacke <>
Submitted on : Wednesday, December 6, 2017 - 5:56:44 PM
Last modification on : Friday, March 22, 2019 - 9:43:52 AM

Identifiers

Collections

UPMC | CNRS | UPEC-UPEM | LIP6 | LIGM | ENPC | PARISTECH | LIGM_MOA | ESIEE-PARIS | SORBONNE-UNIVERSITE | SU-SCIENCES

Citation

Xiangnan Ren, Olivier Curé, Hubert Naacke, Jérémy Lhez, Ke Li. Strider R: Massive and Distributed RDF Graph Stream Reasoning. IEEE International Conference on Big Data, Big Data 2017, Dec 2017, Boston, United States. pp.3358-3367, ⟨10.1109/BigData.2017.8258321⟩. ⟨hal-01657494⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

469

Contact

Informations

CCSD