Abstract
With the advent in pervasive computing, the development of pervasive applications is more and more complex. Such applications require adaptive user interfaces that are able to cope with the heterogeneity of the context of use, regarding the user, the platform, and the environment. Many works have sparked their interest on model-driven approaches to create adaptive user interfaces. Even, while these approaches provide support for user interface modeling and transformation to final applications, they omit the use of the semantic in their user interface models and do not consider reusing knowledge within their design and generation process. In this paper, we integrate model-driven and ontology-based approaches to promote knowledge reuse and enhance the semantics of the developed user interfaces. More specifically, we introduce a model-driven ontology-based architecture for generating adaptive user interfaces. The present architecture is based on semantic models, including the user interface, the user context, and the adaptation rule ontology models. In particular, the user interface ontology model, using knowledge from the context and adaptation rule models, is first adapted to the current context of use, then transformed to a platform independent model, and finally generated to a final source code. In order to illustrate the feasibility and the usefulness of the proposed architecture, we present a case study, where an adaptive user interface is generated.
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Braham, A., Khemaja, M., Buendía, F., Gargouri, F. (2022). Towards a Model-Driven Ontology-Based Architecture for Generating Adaptive User Interfaces. In: Novais, P., Carneiro, J., Chamoso, P. (eds) Ambient Intelligence – Software and Applications – 12th International Symposium on Ambient Intelligence. ISAmI 2021. Lecture Notes in Networks and Systems, vol 483. Springer, Cham. https://doi.org/10.1007/978-3-031-06894-2_13
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DOI: https://doi.org/10.1007/978-3-031-06894-2_13
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