No abstract

Early Warning Systems (EWS) are an essential component of effective disaster management. In the past decade we witnessed the evolvement of highly sophisticated warning systems, in particular for large-scale natural disasters such as floods, storms, and tsunamis. These systems increasingly require the use of advanced information processing for accurate monitoring and prediction as well as targeted alerting under near real-time requirements. Yet, a common reference architecture for such EWS is missing that provides a solid basis for the application of advanced information processing methods. In this paper, we propose a general reference architecture that provides a systematic and common understanding of the functional elements of an EWS solution. Beside providing the foundation for better EWSspecific systems engineering, this architecture can serve as a basis for the identification of application potentials, effective design, implementation, deployment, operation, evolution and re-use of computational intelligence modules within an integrated EWS solution.

No abstract

Demand-orientation is of crucial importance in mobile and pervasive information services in right place. Increasing attention has been devoted to the notion of personalized services that take the situation of the user into account. The trade-off is to ensure appropriate information supply while preventing information overload. Comparing situations predicted by the system with the expectations of a user yields information about "so-far unexpected" changes the user should be informed about. This paper describes an approach to support situation-awareness and introduces an underlying model that additionally handles information from various sources. Our approach is illustrated in a driving assistance application

Demand-orientation is of crucial importance in mobile and pervasive information services in order to ensure the delivery of the right information at the right time and at the right place. In the past years, increasing attention has been devoted to the notion of personalized services that take the situation of the user into account. The trade-off is to ensure appropriate information supply while preventing information overload. Comparing situations predicted by the system with the expectations of a user yields information about "so-farunexpected" changes the user should be informed about. This paper describes an approach to identify and to resolve these knowledge discrepancies by informing the user about them in order to support his or her situation-awareness. Our approach is illustrated in two applications.