Next Generation Context Aware Adaptive Services

Situational information can enrich the interactions between a user and the services they wish to utilize. Such information encompasses details about the user, the physical environment and the computing resources. There are at least three key aspects in addressing this issue. Firstly, it is important to accurately capture or infer the requirements of the users in a timely fashion. Without precise information on what the users are hoping to achieve it is difficult to identify suitable services or sub-services that may fulfill (in part or fully) their information needs. Secondly, the nature of the available services determines the modes in which they may be adapted to the users’ needs. Rigid, inflexible services may be difficult to tune to the information requirements of the users. Adaptive services, on the other hand, are well suited to dynamically modifying their behavior, within defined constraints. The third issue to be addressed is the on-the-fly combination of services to meet the users’ requirements. This paper argues that current modeling (both of users and services) techniques, adaptive axes and personalization techniques used in current personalized information services, such as Adaptive Hypermedia Systems, may supply the basis for next generation adaptive collaborative services

Modeling Cost of Interruption (COI) to Manage Unwanted Interruptions for Mobile Devices

Unwanted and untimely interruptions have been a major cause in the loss of productivity in recent years. It has been found that they are mostly detrimental to the immediate task at hand. Multiple approaches have been proposed to address the problem of interruption by calculating cost of it. The Cost Of Interruption (COI) gives a measure of the probabilistic value of harmfulness of an inopportune interruption. Bayesian Inference stands as the premier model so far to calculate this COI. However, Bayesian-based models suffer from not being able to model context accurately in situations where a priori, conditional probabilities and uncertainties exist while utilizing context information. Hence, this thesis introduces the Dempster-Shafer Theory of Evidence to model COI. Along the way, it identifies specific contexts that are necessary to take into account. Simulation results and performance evaluation suggest that this is a very good approach to decision making. The thesis also discusses an illustrative example of a mobile interruption management application where the Dempster-Shafer theory is used to get a better measurement of whether or not to interrupt