GV: EAGER: Navigation, Exploration and Visualization Tools for Knowledge Discovery in High Dimensional Data Space

Very few real-life phenomena are ever as simple as A causes B ? a bivariate relationship. Take for example economic forecasting: it is a function of unemployment, consumer confidence, inflation, interest rates, and many other factors. There is not one single variable that can solely predict the state of the economy in the next few months. Similar is true in the study of global warming, in the derivation of gene interactions, in the analysis of customer recommendation systems, and so on. Multivariate relationships are ubiquitous and they have always existed. However, with the growth in sensor technology, whatever the data collection mechanism might be (electronic media, physical devices, etc.); we now have a wealth of data available to study in many domains, small and large. Currently, automated and unsupervised methods often fail once the number of variables (dimensions) grows beyond a dozen or even less; hence visualization techniques for user-assisted analysis play an important role. Responding to this need, this exploratory project develops a novel framework that makes high-dimensional (multivariate) data visualization more accessible to all. It couples powerful data analysis with an intuitive exploration and way-finding paradigm ? akin to a tourist map ? to help users navigate high-dimensional data spaces with ease. The overall goal of the project is to facilitate intuitive navigation and exploration of high-dimensional data spaces, improving comprehensibility and reducing unnecessary complexity. This is achieved by: (1) unrolling the high-dimensional space into a landscape map; (2) enabling users to navigate the map and local subspaces of the data via an interactive data projection utility controlled by a touchpad interface; (3) allowing users to insert interesting observations (i.e., data projections) into this map; (4) augmenting the map with background overlays depicting informative globally defined data; and (5) conveying the data within a level-of-detail illustrative visualization framework. The system is evaluated and refined via formal user studies, both with domain scientists in interviews and in a crowd-sourced setting over the web. This novel information visualization approach will provide support to both scientists and casual users to explore high dimensional data spaces in an intuitive navigation paradigm. The project webpage (http://www.cs.sunysb.edu/~mueller/TripAdvisorND) will be used for results dissemination, including data analysis capabilities within a web-enabled version of the software and also used to invite to participation in evaluation studies. This exploratory research project provides a rich research and educational experience to students.