Sensor Data Visualisation: A Composition-Based Approach to Support Domain Variability

Abstract : In the context of the Internet of Things, sensors are surrounding our environment. These small pieces of electronics are inserted in everyday life's elements (e.g., cars, doors, radiators, smartphones) and continuously collect information about their environment. One of the biggest challenges is to support the development of accurate monitoring dashboard to visualise such data. The one-size-fits-all paradigm does not apply in this context, as user's roles are variable and impact the way data should be visualised: a building manager does not need to work on the same data as classical users. This paper presents an approach based on model composition techniques to support the development of such monitoring dashboards, taking into account the domain variability. This variability is supported at both implementation and modelling levels. The results are validated on a case study named SmartCampus, involving sensors deployed in a real academic campus.
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Communication dans un congrès
European Conference on Modelling Foundations and Applications (ECMFA 2014), Jul 2014, York, United Kingdom. Springer, Modelling Foundations and Applications 10th European Conference, ECMFA 2014, Held as Part of STAF 2014, York, UK, July 21-25, 2014. Proceedings, 8569, pp.101-116, 2014, <10.1007/978-3-319-09195-2_7>
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Ivan Logre, Sébastien Mosser, Philippe Collet, Michel Riveill. Sensor Data Visualisation: A Composition-Based Approach to Support Domain Variability. European Conference on Modelling Foundations and Applications (ECMFA 2014), Jul 2014, York, United Kingdom. Springer, Modelling Foundations and Applications 10th European Conference, ECMFA 2014, Held as Part of STAF 2014, York, UK, July 21-25, 2014. Proceedings, 8569, pp.101-116, 2014, <10.1007/978-3-319-09195-2_7>. <hal-01322526>

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