Aggregation and Personalization of Infotainment – An Architecture Illustrated with a Collaborative Scenario

A user-centric architecture of infotainment content adaptation to the context is presented. The architecture uses component technologies in term of business logic and functionalities offered by social web (OpenID, FOAF) and semantic descriptions of MPEG-7 and MPEG-21. Technological alternatives are discussed and adapted to the specificity of vehicle applications in terms of scalability and platform mobility. The requirements of the architecture are motivated by the presentation of a scenario.

Personalizing the Web Content on User Perceptual Preferences

This paper introduces a new model of personalized usage of the internet that is based on technologies of user representation, artificial intelligence and semantic augmentation of the content. By taking advantage of internet’s unprecedented dynamics, compared to traditional media, this user representation model incorporates cognitive, mainly, psychology theories, combined with parameters that constitute more traditional approaches in user profiling (such as demographics, expertise, etc). The purpose of this research is to alleviate difficulties that massive approaches impose on areas such as education and information processing, by integrating intelligent adaptive characteristics into web applications; this can lead to a highly adapted to each user’s needs content and more effective, in our case, learning.

GlobeMash: a Mashup for Accessing GLOBE

In this paper, we present GlobeMash, a mashup web application that uses standardized data formats like XML, JSON, LOM, SVG, CAM and protocols like SOAP, HTTP, to enable users to access the repositories of the Global Learning Objects Brokered Exchange (GLOBE) consortium. GlobeMash uses the Google Maps API to display the repositories and results on a geographical map, the Timeline API of the SIMILE project and the federated search layer of the GLOBE infrastructure. It enables users to query all the repositories in GLOBE and to get an insight in their search history by visualizing the latter as a combination of an extended tag-cloud and a synchronized timeline.