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Scalable Saturation of Streaming RDF Triples

Farvardin, Mohammad Amin; Colazzo, Dario; Belhajjame, Khalid; Sartiani, Carlo (2020), Scalable Saturation of Streaming RDF Triples, in Hameurlain, Abdelkader; Tjoa, A Min; Lamarre, Philippe; Zeitouni, Karine, Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIV : Special Issue on Data Management – Principles, Technologies, and Applications, Springer, p. 1-40. 10.1007/978-3-662-62271-1_1

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Farvardin2020_Chapter1.pdf (3.686Mb)
Type
Chapitre d'ouvrage
Date
2020
Book title
Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIV : Special Issue on Data Management – Principles, Technologies, and Applications
Book author
Hameurlain, Abdelkader; Tjoa, A Min; Lamarre, Philippe; Zeitouni, Karine
Publisher
Springer
ISBN
978-3-662-62270-4
Number of pages
195
Pages
1-40
Publication identifier
10.1007/978-3-662-62271-1_1
Metadata
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Author(s)
Farvardin, Mohammad Amin
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Colazzo, Dario
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Belhajjame, Khalid
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Sartiani, Carlo
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 or OWL 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.
Subjects / Keywords
Big data

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