International audienceIn 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

Visualization systems such as dashboards are commonly used to analyze data and support users in their decision making, in communities as different as medical care, transport and software engineering. The increasing amount of data produced and continuous development of new visualizations exacerbate the difficulty of designing such dashboards, while the visualization need is broaden to specialist and non-specialist final users. In this context, we offer a multiuser approach, based on Model Driven Engineering (MDE). The idea is for the designer to express the visualization need by characterization, according to a given taxonomy. We provide a Domain Specific Language (DSL) to design the system and a Software Product Line (SPL) to capture the technological variability of visualization widgets. We performed a user study, using a software project management use case, to validate if dashboard users and designers are able to use a taxonomy to express their visualization need.