Abstract

Abstract

Today, there are many systems with large amounts of complex data sets. Visualizing these systems in a way that enlightens the user and provides a profound understanding of the respective information space is one of the big information visualization research challenges. Keim [10] states that it is no longer possible to display an overview of these systems as proposed in Shneiderman's information seeking mantra [18].To overcome this incapacity and to provide a solution to the dilemma of time multiplexing-vs. space multiplexing techniques, we propose the context-sensitive use of a collection of animated 3D metaphors. These metaphors are integrated in a flexible framework called HANNAH. This provides the possibility to interconnect media of various types in order to bridge the semantic gab as required for humancentered applications according to Elgammal [6].

Current sanitation concepts of decentralised wastewater treatment and reuse raise the issue of monitoring and maintenance of such systems. To guarantee high quality of the recycled water, systems with high requirements concerning process technology are essential. With increasing numbers of decentralised treatment systems spread over far distances it will become more and more impossible and uneconomic to have expert personnel on site. Therefore, new visualisation and intelligent information systems are necessary. The paper describes the structure and 3D-demonstrations as a base for information visualisation. Up-to-date visualisation techniques, facilitating the cognition of context-adapted information, make it possible to maximise the amount of information presented to the user without overwhelming her or him. Concerning diagnosis and decision support systems in the domain of wastewater treatment, several interesting approaches are presented, estimating their applicability for decentralised wastewater treatment systems. The intelligent decision support system presented here consists of a combined ontology- and case-based reasoning system in addition to a process monitoring system. It is responsible for plausibility checks, error diagnosis, solution proposals, and optimisation suggestions.