Abstract

One important intention of human-centered information visualization is to represent huge amounts of abstract data in a visual representation that allows even users from foreign application domains to interact with the visualization, to understand the underlying data, and finally, to gain new, application-related knowledge. The visualization will help experts as well as non-experts to link previously or isolated knowledge-items in their mental map with new insights.Our approach explicitly supports the process of linking knowledge-items with three concepts. At first, the representation of data items in an ontology categorizes and relates them. Secondly, the use of various visualization techniques visually correlates isolated items by graph-structures, layout, attachment, integration, or hyperlink techniques. Thirdly, the intensive use of visual metaphors relates a known source domain to a less known target domain. In order to realize a scenario of these concepts, we developed a visual interface for non-experts to maintain complex wastewater treatment plants. This domain-specific application is used to give our concepts a meaningful background.

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].