Knowledge-Assisted Visualization

Visualization is an exploratory process. For example, given a data set, a user first de cides which visualization tools to use to explore it. The user then experiments with different controls, such as styles, layout, viewing position, color maps, and transfer functions, until he or she obtains a collection of satisfactory visualization results. For complex visualizations, interaction alone often can’t reduce the search space rapidly. So, we need to advance the visualization technology, from today’s interactive visualization to tomorrow’s knowledge-assisted visualization. Knowledge-assisted visualization’s objectives include sharing domain knowledge among different users and reducing the burden on users to acquire knowledge about complex visualization techniques. It also aims to enable the visualization community to learn and model the best practices, so that powerful visualization infrastructures can evolve. 1 Knowledge-assisted visualization is in its infancy. Most visualization techniques and systems don’t yet utilize the knowledge captured from domain experts or the visualization process. In several recent developments, researchers have made noticeable effort to capture and make use of knowledge in visualization. These developments confirm the technical feasibility of knowledge-assisted visualization and indicate its great potential.