Assisted navigation for large information spaces

This paper presents a new technique for visualizing large, complex collections of data. The size and dimensionality of these datasets make them challenging to display in an effective manner. The images must show the global structure of spatial relationships within the dataset, yet at the same time accurately represent the local detail of each data element being visualized. We propose combining ideas from information and scientific visualization together with a navigation assistant, a software system designed to help users identify and explore areas of interest within their data. The assistant locates data elements of potential importance to the user, clusters them into spatial regions, and builds underlying graph structures to connect the regions and the elements they contain. Graph traversal algorithms, constraint-based viewpoint construction, and intelligent camera planning techniques can then be used to design animated tours of these regions. In this way, the navigation assistant can help users to explore any of the areas of interest within their data. We conclude by demonstrating how our assistant is being used to visualize a multidimensional weather dataset.