In this paper we present a general purpose solution to Web content perusal by means of mobile devices, named Social Context-Aware Browser. This is a novel approach for the information access based on the users' context, whose aim is to retrieve what the user needs, even if she did not issue any query. Our solution is built upon a social model that exploits the collaborative efforts of the whole community of users to control and manage contextual knowledge, related both to situations and resources. This paper presents a general survey of our solution, describing the idea and presenting an implementation approach.
The Context-Aware Browser for mobile devices senses the surrounding environment,\ud
infers the user’s current context, and proactively searches for and activates relevant Web documents and applications
Nowadays, the mobile computing paradigm and the widespread diffusion of mobile devices are quickly changing and replacing many common assumptions about software architectures and interaction/communication models. The environment, in particular, or more generally, the so-called user context is claiming a central role in everyday’s use of cellular phones, PDAs, etc. This is due to the huge amount of data “suggested” by the surrounding environment that can be helpful in many common tasks. For instance, the current context can help a search engine to refine the set of results in a useful way, providing the user with a more suitable and exploitable information. Moreover, we can take full advantage of this new data source by “pushing” active contents towards mobile devices, empowering the latter with new features (e.g., applications) that can allow the user to fruitfully interact with the current context. Following this vision, mobile devices become dynamic self-adapting tools, according to the user needs and the possibilities offered by the environment. The present work proposes MoBe: an approach for providing a basic infrastructure for pervasive context-aware applications on mobile devices, in which AI techniques (namely a principled combination of rule-based systems, Bayesian networks and ontologies) are applied to context inference. The aim is to devise a general inferential framework to make easier the development of context-aware applications by integrating the information coming from physical and logical sensors (e.g., position, agenda) and reasoning about this information in order to infer new and more abstract contexts
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