a framework for designing fisheye views to support multiple semantic contexts
In this paper we discuss the design and use of fisheye view techniques to explore semantic relationships in information. Traditional fisheye and "focus + context" techniques dynamically modify the visual rendering of data in response to the changing interest of the user. "Interesting" information is shown in more detail or visually emphasized, while less relevant information is shown in less detail, de-emphasized, or filtered. These techniques are effective for navigating through large sets of information in a constrained display, and for discovering hidden relationships in a particular representation. An open area of research with these techniques, however, is how to redefine interest as a user's tasks and information needs change. We are developing a framework for implementing fisheye views to support multiple semantic contexts. The framework is based on two components: Degree Of Interest functions, and visual emphasis algorithms to change the representation of information with respect to interest. The framework supports different contexts through the aggregation of multiple weighted distance metrics in the calculation of interest. Using this framework, we have developed a user-configurable interface for browsing tabular data that visually emphasizes objects with respect to different semantic contexts.
emphasis algorithms semantic fisheye views information visualization focus + context techniques