Principles for Scalable Dynamic Visual Analytics

The human eye is often capable of identifying interesting patterns and trends from a well-presented data set, whereas computational algorithms may have difficulties with such a task. Yet, there are limits to human ability, both with the scale of the data set in terms of objects and attributes and with dynamic changes over time. This project develops an analytic and computational framework to support the visual analysis of large-scale dynamic data with network structure.

The intellectual merit of this project is in the development of a family of operators with which to reduce the size both in terms of objects and attributes of the data set to be visualized; an analysis of the properties of this family of operators to enable their effective use; and the development of algorithms and data structures to support the efficient computation of these operators. By harnessing computational power to assist the human eye in seeing patterns and trends in the data, this project has the potential to transform the way in which large dynamic data sets with network structure are analyzed today.

The broader impact of the project lies in the multiple application domains where network data are ubiquitous in their presence. In particular, we plan to focus on two domains to illustrate the proposed framework; biology through protein interaction networks, and national intelligence through social networks of suspect participants. In addition, this interdisciplinary project plows the ground at the boundary of statistics and computer science, and trains graduate students at this interface, an area with great future potential.