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Streaming saturation for large RDF graphs with dynamic schema information

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technical-paper.pdf (1.499Mb)
Date
2019
Dewey
Programmation, logiciels, organisation des données
Sujet
RDF saturation; RDF streams; Big Data; Spark
DOI
http://dx.doi.org/10.1145/3315507.3330201
Conference name
17th ACM SIGPLAN International Symposium on Database Programming Languages (DBPL 2019)
Conference date
06-2019
Conference city
Phoenix, AZ
Conference country
United States
Book title
Proceedings of the 17th ACM SIGPLAN International Symposium on Database Programming Languages (DBPL 2019)
Author
Cheung, Alvin; Nguyễn, Kim
Publisher
ACM - Association for Computing Machinery
Publisher city
New York, NY
Year
2019
ISBN
978-1-4503-6718-9
URI
https://basepub.dauphine.fr/handle/123456789/19938
Collections
  • LAMSADE : Publications
Metadata
Show full item record
Author
Farvardin, Mohammad Amin
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Colazzo, Dario
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Belhajjame, Khalid
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Sartiani, Carlo
262138 Dipartimento di Matematica Informatica ed Economia [DiMIE]
Type
Communication / Conférence
Item number of pages
42-52
Abstract (EN)
In the Big Data era, RDF data are produced in high volumes. While there exist proposals for reasoning over large RDF graphs using big data platforms, there is a dearth of solutions that do so in environments where RDF data are dynamic, and where new instance and schema triples can arrive at any time. In this work, we present the first solution for reasoning over large streams of RDF data using big data platforms. In doing so, we focus on the saturation operation, which seeks to infer implicit RDF triples given RDF schema constraints. Indeed, unlike existing solutions which saturate RDF data in bulk, our solution carefully identifies the fragment of the existing (and already saturated) RDF dataset that needs to be considered given the fresh RDF statements delivered by the stream. Thereby, it performs the saturation in an incremental manner. Experimental analysis shows that our solution outperforms existing bulk-based saturation solutions.

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