Scalable visual hierarchy exploration

More and more modern computer applications, from business decision support to scientific data analysis, utilize visualization techniques to support exploratory activities. Most visualization tools do not scale well with regard to the size of the dataset upon which they operate. Specifically, the level of cluttering on the screen is typically unac-ceptable and the performance is poor. To solve the problem of cluttering at the interface level, visualization tools have recently been extended to support hierarchical views of the data, with support for focusing and drilling-down using interactive brushes. To solve the scalability problem, this thesis investigates how best to couple a visualization tool with a database management system without losing the real-time responsiveness required in any interactive application. This integration must be done carefully. Visual user interactions often cannot be ef-ficiently realized using standard database operations. In our context, the recursive pro-cessing required for the visual data retrieval was prohibitively expensive; responding for instance to a single user request took up to 30 minutes on a 200,000 tuple dataset. We suc