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Une approche sémantique pour l’exploitation de données environnementales : application aux données d’un observatoire

Abstract : The need to collect long-term observations for research on environmental issues led to the establishment of "Zones Ateliers" by the CNRS. Thus, for several years, many databases of a spatio-temporal nature are collected by different teams of researchers. To facilitate transversal analysis of different observations, it is desirable to cross-reference information from these data sources. Nevertheless, these sources are constructed independently of each other, which raise problems of data heterogeneity in the analysis.Therefore, this thesis proposes to study the potentialities of ontologies as both objects of modeling, inference, and interoperability. The aim is to provide experts in the field with a suitable method for exploiting heterogeneous data. Being applied in the environmental domain, ontologies must take into account the spatio-temporal characteristics of these data. As the need for modeling concepts and spatial and temporal operators, we rely on the solution of reusing the ontologies of time and space. Then, a spatial-temporal data integration approach with a reasoning mechanism on the relations of these data has been introduced. Finally, data mining methods have been adapted to spatio-temporal RDF data to discover new knowledge from the knowledge-base. The approach was then applied within the Geminat prototype, which aims to help understand farming practices and their relationships with the biodiversity in the "zone atelier Plaine and Val de Sèvre". From data integration to knowledge analysis, it provides the necessary elements to exploit heterogeneous spatio-temporal data as well as to discover new knowledge.
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Submitted on : Friday, June 1, 2018 - 11:52:06 AM
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  • HAL Id : tel-01804963, version 1



Ba-Huy Tran. Une approche sémantique pour l’exploitation de données environnementales : application aux données d’un observatoire. Base de données [cs.DB]. Université de La Rochelle, 2017. Français. ⟨NNT : 2017LAROS025⟩. ⟨tel-01804963⟩



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