| Information visualization, a field that studies visual representations of abstract data where no spatial representation is available, has been playing an essential role in assisting people to understand the vast amount of information created by modern technology. Visualizing large complex structured data is an important area as the structured data are ubiquitous in many aspects of our lives. The large size, high complexity, and vast variety in user interests pose formidable challenges to create effective representations for those structured data.;To help users understand detailed information in the large dataset based on their changing interests, several focus-based interactive visualization methods have been described. To allow users to discover specific contextual information around the focus in large semantic graphs, we propose to use the embedded semantic queries during browsing as the main method for information discovery. In addition, to let users quickly understand the different aspects of the graph data, we propose to set up multiple contexts and enable users to quickly switch among the contexts without any abrupt layout changes. Moreover, to assist users in quickly identifying the focal entities when comparing two treemaps, we propose novel contrast techniques to highlight the key differences of the two treemaps in the context of a single treemap so that direct comparison can be done easily. Furthermore, to facilitate the study of the details of multiple foci in a treemap, we propose a focus+context technique to seamlessly enlarge multiple foci in the same view while maintaining a consistent and stable layout. The effectiveness of these approaches are evaluated by case studies and user studies, where we have clearly demonstrated that users can better understand the structured data with more details and in less amount of time. Both free exploration and task-oriented scenarios were studied in our experiments. |
