Context-aware Service Adaptation Research Articles

A metamodel and taxonomy to facilitate context-aware service adaptation

Increasingly, companies are adopting service-oriented architectures to respond to rapid changes in the market. Even though there are excellent tools and frameworks for service-oriented architecture adoption and service development, the latest adaptation to context has not been properly dealt with yet. Current approaches are mostly focused on solving context-aware issues for web applications only, focusing mainly on client-side adaptation, and there is a clear lack of context taxonomies which facilitate context-aware applications. In our previous work we proposed Model-Driven Adaptable Services (MoDAS): a methodology and tool for the development of context-aware services. In this paper, we propose two key improvements on MoDAS: firstly, leveraging the proposal’s abstraction level, facilitating the use of a larger collection of contexts through the definition of an extensible context taxonomy by means of a metamodel; secondly, providing additional opportunities for code generation.

Context-Aware Service Adaptation: An Approach Based on Fuzzy Sets and Service Composition

With the pervasive use of mobile devices and the need for ubiquitous computing, the issue of ”context” now becomes a hot topic in human computer interaction research and development. Further, the interface of the interaction beyond the desktop is moving from humans vs. computers to humans vs. context-aware environments. This leads the context to constitute an underlying part of service behavior, especially when interaction with end-users is involved, and consequently this demands the context-aware computing systems to be designed to automatically adapt its behavior to changing environment. Thus, pervasive computing applications need to be more autonomous and sensitive to context. However, in real-life systems context information is naturally dynamic, vague and complex, which lead to an inexact match between provided and required service capabilities. In this vision, we propose in this paper a three-phases adaptation approach: firstly we select the suitable services to the current context and we recommend them to the adaptation process, in the service adaptation phase we perform adaptation by using fuzzy sets represented with linguistic variables and membership degrees to define the user's context and the rules for adopting the policies of implementing a service. Finally we deal with the complex requirements of the user by the composition of the obtained adaptable atomics services.

모바일 환경에서 맞춤형 콘텐츠 전달을 위한 비디오 적응성 모델

이동하면서도 디지털 방송을 볼 수 있는 모바일 TV가 세계에 보급되기 시작하였다. TV 또는 HDTV와 같은 대화형 데이터 서비스를 제공 하기 위해서 데이터 표현, 전송 및 처리가 아주 중요하다. 특히 제한된 대역폭과 낮은 성능의 단말과 같은 모바일 방송통신 환경에서 오디오, 비디오, 이미지 등의 다양한 멀티미디어 서비스를 제공 받기 위해서는 효율적으로 전송 및 적응형 데이터 서비스 기술이 필수적으로 요구된다. 본 논문에서모바일 환경에서 사용자 중심으로 비디오서비스 대한 미들웨어 기술을 효율적으로 개발하기 위하여 사용자 환경상황인식(Usage Environment), 개인화(Personalization)를 위한 데이터 수정 및 처리 기술, 사용자 중심으로 맞춤형 적응 서비스 미들웨어 UP-SAM(User Personalization Context-Aware Service Adaptation Middleware) 모델을 제안한다. Lately the usage of multimedia equipment with small LCD displays is rapidly increasing. Although many people use devices like this, videos intended for TV or HDTV are sent to these mobile devices. Therefore cases where it is hard for the user to view the desired scenes are growing more frequent. Currently, most services simply reduce the size of the content to fit the screen when they offer it for mobile devices. However, especially with sports broadcasts, there are many areas that cannot be seen very well because it was simply reduced in size. We therefore consider this weakness and are researching how to let the user choose an area of interest and then sending it to the user in a way that fits the device. In this paper, we address the problem of video delivery and personalization. For the delivered video content, we suggest the UP-SAM User Personalized Context-Aware Service Adaptation Middleware) model that uses the video content description and MPEG-21 multimedia framework.