Tackling High Variability in Video Surveillance Systems through a Model Transformation ApproachReportar como inadecuado




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1 Laboratoire d-Informatique, Signaux, et Systèmes de Sophia-Antipolis I3S - Equipe MODALIS SPARKS - Scalable and Pervasive softwARe and Knowledge Systems 2 PULSAR - Perception Understanding Learning Systems for Activity Recognition CRISAM - Inria Sophia Antipolis - Méditerranée

Abstract : This work explores how model-driven engineering techniques can support the configuration of systems in domains presenting multiple variability factors. Video surveillance is a good candidate for which we have an extensive experience. Ultimately, we wish to automatically generate a software component assembly from an application specification, using model to model transformations. The challenge is to cope with variability both at the specification and at the implementation levels. Our approach advocates a clear separation of concerns. More precisely, we propose two feature models, one for task specification and the other for software components. The first model can be transformed into one or several valid component configurations through step-wise specialization. This paper outlines our approach, focusing on the two feature models and their relations. We particularly insist on variability and constraint modeling in order to achieve the mapping from domain variability to software variability through model transformations.





Autor: Mathieu Acher - Philippe Lahire - Sabine Moisan - Jean-Paul Rigault -

Fuente: https://hal.archives-ouvertes.fr/



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