Filtrage, stockage et raisonnement sur de grands volumes de triplets RDF ordonnancés

Abstract : With the developpement and the expansion of connected devices in every domain, several projects on stream processing have been developped. This thesis has been realized as part of the FUI Waves, a reasoning stream processing engine distributed. The use case for the developement was the processing of data streamed from a potable water distribution network, more specifically the detection of anomalies in the quality measures and their contextualisation using external data. Several contributions have been realized and integrated in different stages of the project, wih evaluations and publications witnessing their relevance. These contributions use an ontology that has been designed thanks to collaboration with domain experts working for our water data management project partner. The use of geographical data allowed to realize a profiling system aiming at improving the anomaly contextualisation process. An ontology encoding approach, adapted to RDF stream processing, has been developped to support RDFS inferences enriched with owl : sameAs. Conjointly, a compressed formalism (PatBin) has been designed to represent streams. PatBin is based on the regularity of patterns found in incoming streams. Moreover, a query language has been conceived from PatBin, namely PatBinQL. It integrates a reasoning strategy that combines both materialization and query rewritting. Finally, given deductions coming from a Waves machine learning component, a query generation tool has been developped. These diferent contributions have been evaluated on both real-world and synthetic datasets
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Jérémy Lhez. Filtrage, stockage et raisonnement sur de grands volumes de triplets RDF ordonnancés. Autre [cs.OH]. Université Paris-Est, 2018. Français. ⟨NNT : 2018PESC1122⟩. ⟨tel-02084022⟩

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