Multimapping Design of Complex Sensor Data in Environmental Observatories

Abstract : Environmental resources (e.g., air quality, water quantity) are needed to understand fundamental questions such as global change. Such resources are often collected from sensors, including humans acting as sensors. Tools have emerged to manage such data in the form of time series and, in particular, the Sensor Observation Service (SOS) which offers a framework based on predefined relational database schema. Environmental observatories can be built using such frameworks, allowing to address specific key scientific questions by collecting and sharing large-scale environmental data. However, the strict schema of SOS database makes it difficult to integrate some data that cannot be directly mapped to the schema. Guidelines and best practices are offered in the literature in order to reuse standards from the Semantic Web but they do not cover all needs. In particular, they do not help to reflect the fact that a single environmental database can lead to several SOS models. Since being aware of these multiple possibilities is crucial for a better use of the observatories, we argue that some extensions of the existing works are required. In this paper, we thus propose an extension of existing vocabularies to achieve this goal. Our contribution is illustrated on the real case of the Lebanese-French O-LiFE environmental observatory.
Type de document :
Communication dans un congrès
WIMS: Web Intelligence, Mining and Semantics, Jun 2016, Nimes, France. ACM, 6th ACM International Conference on Web Intelligence, Mining and Semantics, 2016, 〈10.1145/2912845.2912856〉
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Contributeur : Anne Laurent <>
Soumis le : vendredi 14 octobre 2016 - 00:14:34
Dernière modification le : jeudi 18 octobre 2018 - 19:18:42

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Hicham Hajj-Hassan, Nicolas Arnaud, Arnaud Castelltort, Laurent Drapeau, Anne Laurent, et al.. Multimapping Design of Complex Sensor Data in Environmental Observatories. WIMS: Web Intelligence, Mining and Semantics, Jun 2016, Nimes, France. ACM, 6th ACM International Conference on Web Intelligence, Mining and Semantics, 2016, 〈10.1145/2912845.2912856〉. 〈lirmm-01381082〉

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