This project involves the development of interactive visualization and data management techniques for the exploration of very large multivariate data sets. The approach consists of extending several multivariate data visualization techniques currently implemented in an existing visualization tool (XmdvTool, developed at WPI) to support hierarchical views of the data, with support for focusing and drill-down using N-dimensional brushes. The stages of the project are as follows: (1) identify, design, and implement algorithms for hierarchical partitioning and/or clustering large multivariate data sets; (2) design and implement extended versions of existing multivariate visualization techniques to convey statistical summarizations of selected subtrees; (3) design and implement strategies for managing and querying large, hierarchical, dynamic data sets and efficiently computing summarizations of subtrees of the hierarchy; (4) design and implement interactive tools to allow focus, drill-down, consolidation, and other exploratory operations through direct and indirect manipulation; (5) evaluate and refine the visualization, data management, and interactive exploration tools using both real and synthetic data sets; and (6) assess system performance in terms of functionality, usability, and limitations on data which can be effectively explored. This project will result in the development of techniques for the qualitative mining of multivariate data sets one or two orders of magnitude larger than possible with existing visual exploration tools, providing application domains such as global change studies, space science, and decision support systems the technology to uncover trends, patterns, and anomalies within their data sets. www.cs.wpi.edu/~matt/research/XmdvTool/
