Context-aware framework for collaborative applications

Future pervasive environments will take into consideration not only individual user’s interest, but also social relationships. In this way, pervasive communities can lead the user to participate beyond traditional pervasive spaces, enabling the cooperation among groups and taking into account not only individual interests, but also the collective and social context. Social applications in CSCW (Computer Supported Cooperative Work) field represent new challenges and possibilities in terms of use of social context information for adaptability in pervasive environments. In particular, the research describes the approach in the design and development of a context.aware framework for collaborative applications (CAFCA), utilizing user’s context social information for proactive adaptations in pervasive environments. In order to validate the proposed framework an evaluation was conducted with a group of users based on enterprise scenario. The analysis enabled to verify the impact of the framework in terms of functionality and efficiency in real-world conditions. The main contribution of this thesis was to provide a context-aware framework to support collaborative applications in pervasive environments. The research focused on providing an innovative socio-technical approach to exploit collaboration in pervasive communities. Finally, the main results reside in social matching capabilities for session formation, communication and coordinations of groupware for collaborative activities.

Semantic features for context organization

In recent years the technological world has grown by incorporating billions of small sensing devices, collecting and sharing real-world information. As the number of such devices grows, it becomes increasingly difficult to manage all these new information sources. There is no uniform way to share, process and understand context information. In previous publications we discussed efficient ways to organize context information that is independent of structure and representation. However, our previous solution suffers from semantic sensitivity. In this paper we review semantic methods that can be used to minimize this issue, and propose an unsupervised semantic similarity solution that combines distributional profiles with public web services. Our solution was evaluated against Miller-Charles dataset, achieving a correlation of 0.6.