References of "Fouquet, François 50003376"
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See detailKevoree Modeling Framework (KMF): Efficient modeling techniques for runtime use
Fouquet, François UL; Nain, Grégory UL; Morin, Brice et al

Report (2014)

The creation of Domain Specific Languages(DSL) counts as one of the main goals in the field of Model-Driven Software Engineering (MDSE). The main purpose of these DSLs is to facilitate the manipulation of ... [more ▼]

The creation of Domain Specific Languages(DSL) counts as one of the main goals in the field of Model-Driven Software Engineering (MDSE). The main purpose of these DSLs is to facilitate the manipulation of domain specific concepts, by providing developers with specific tools for their domain of expertise. A natural approach to create DSLs is to reuse existing modeling standards and tools. In this area, the Eclipse Modeling Framework (EMF) has rapidly become the defacto standard in the MDSE for building Domain Specific Languages (DSL) and tools based on generative techniques. However, the use of EMF generated tools in domains like Internet of Things (IoT), Cloud Computing or Models@Runtime reaches several limitations. In this paper, we identify several properties the generated tools must comply with to be usable in other domains than desktop-based software systems. We then challenge EMF on these properties and describe our approach to overcome the limitations. Our approach, implemented in the Kevoree Modeling Framework (KMF), is finally evaluated according to the identified properties and compared to EMF. [less ▲]

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See detailReactive Security for Smart Grids Using Models@run.time-Based Simulation and Reasoning
Hartmann, Thomas UL; Fouquet, François UL; Klein, Jacques UL et al

in Proceedings of the Second Open EIT ICT Labs Workshop on Smart Grid Security (SmartGridSec14) (2014, April)

Smart grids leverage modern information and communication technology to offer new perspectives to electricity consumers, producers, and distributors. However, these new possibilities also increase the ... [more ▼]

Smart grids leverage modern information and communication technology to offer new perspectives to electricity consumers, producers, and distributors. However, these new possibilities also increase the complexity of the grid and make it more prone to failures. Moreover, new advanced features like remotely disconnecting meters create new vulnerabilities and make smart grids an attractive target for cyber attackers. We claim that, due to the nature of smart grids, unforeseen attacks and failures cannot be effectively countered relying solely on proactive security techniques. We believe that a reactive and corrective approach can offer a long-term solution and is able to both minimize the impact of attacks and to deal with unforeseen failures. In this paper we present a novel approach combining a Models@run.time-based simulation and reasoning engine with reactive security techniques to intelligently monitor and continuously adapt the smart grid to varying conditions in near real-time. [less ▲]

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See detailGeneric Cloud Platform Multi-objective Optimization Leveraging Models@run.time
El Kateb, Donia UL; Fouquet, François UL; Nain, Grégory UL et al

Scientific Conference (2014, March)

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See detailA Native Versioning Concept to Support Historized Models at Runtime
Hartmann, Thomas UL; Fouquet, François UL; Nain, Grégory UL et al

in Dingel, Juergen; Schulte, Wolfram; Ramos, Isidro (Eds.) et al Model-Driven Engineering Languages and Systems - 17th International Conference, MODELS 2014, Valencia, Spain, September 28 - October 3, 2014. Proceedings (2014)

Models@run.time provides semantically rich reflection layers enabling intelligent systems to reason about themselves and their surrounding context. Most reasoning processes require not only to explore the ... [more ▼]

Models@run.time provides semantically rich reflection layers enabling intelligent systems to reason about themselves and their surrounding context. Most reasoning processes require not only to explore the current state, but also the past history to take sustainable decisions e.g. to avoid oscillating between states. Models@run.time and model-driven engineering in general lack native mechanisms to efficiently support the notion of history, and current approaches usually generate redundant data when versioning models, which reasoners need to navigate. Because of this limitation, models fail in providing suitable and sustainable abstractions to deal with domains relying on history-aware reasoning. This paper tackles this issue by considering history as a native concept for modeling foundations. Integrated, in conjunction with lazy load/storage techniques, into the Kevoree Modeling Framework, we demonstrate onto a smart grid case study, that this mechanisms enable a sustainable reasoning about massive historized models. [less ▲]

Detailed reference viewed: 174 (28 UL)