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2017 | OriginalPaper | Buchkapitel

Model-Driven Context Management for Self-adaptive User Interfaces

verfasst von : Enes Yigitbas, Silas Grün, Stefan Sauer, Gregor Engels

Erschienen in: Ubiquitous Computing and Ambient Intelligence

Verlag: Springer International Publishing

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Abstract

The user interfaces (UIs) of interactive systems become increasingly complex since many heterogeneous and dynamically changing context-of-use parameters regarding user profile, platform, and usage environment have to be supported. Self-adaptive UIs have been promoted as a solution for context variability due to their ability to automatically adapt to the context-of-use at runtime. Context modeling and context management are important prerequisites for supporting self-adaptive UIs, but introduce additional complexity since context information has to be captured using sensors from heterogeneous sources and dynamic context changes have to be monitored to enable UI adaptation at runtime. To overcome the complex and cumbersome task of context management, we present a model-driven approach for developing a flexible context manager supporting self-adaptive UIs. Our approach consists of a new context modeling language, named ContextML, for specification of various context-of-use situations. Based on the specified context model, our approach enables automatic generation of context services for monitoring context-of-use parameters. The benefit of our approach is demonstrated by a case study, where generated context services provide context information and trigger the adaptation of UIs for a university library web application.

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Metadaten
Titel
Model-Driven Context Management for Self-adaptive User Interfaces
verfasst von
Enes Yigitbas
Silas Grün
Stefan Sauer
Gregor Engels
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-67585-5_61

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