UNC- Charlotte - Visualization Center
We have developed visual analysis methods to investigate hundreds of thousands or more bank transactions over extended periods of time. Transactions are clustered and reclustered according to their similarities and then explored for patterns of keywords, distribution within and between clusters, amount of activity or amount of money transferred, and other factors.
Real world knowledge gathering and investigative tasks are very complex because the problem-solving context is constantly evolving, and the data may be incomplete, unreliable and/or conflicting. We are developing a mixed-initiative reasoning agent that will assist homeland security analysts to choose from and reason about enormous databases of text, imagery, video and webcast.
A multimodal data analysis is applied to concurrent visual signals, auditory signals, and, when available, closed caption text. The analysis is general and unstructured; it can be applied, for example, to broadcast video in any language. We have applied the analysis to automatically identify and segment news broadcasts. However, the methods can be applied to identify and segment other broadcast types as well.
The integration of intelligent image content analysis and interactive visualization has produced a complete visual analytics tool. Image content analysis is automatically applied to a set of images with unknown content. Concepts within the images can be found from a pre-trained set or added by user selection on a training set of images. Images are then clustered according to their properties and according to the concepts they contain so that like images will be close to one another. This work is led by Jianping Fan and Jing Yang.
The above video analysis produces rich and complex patterns over time. We are developing a variety of analysis techniques to attack this ever-changing stream of topics, allowing the user to quickly get an overview of what is being discussed and then home in on the most relevant stories associated with topics of interest.
Parallel Sets is a powerful technique for the interactive visual analysis of categorical data. In addition to visualizing the existing data, parallel sets allow the user to create new data dimensions by combining existing ones, thus tailoring the data to the analysis process and the user’s model.
Georgia Tech- GVU Visualization Center
We are developing a system that will help analysts browse through large collections of intelligence reports. The system begins by parsing plain text reports and constructing XML nodes with fields for the important report items such as people mentioned, dates, places, and activities.
We are developing computational techniques for recognizing story plots connecting (apparently isolated) events. The goal is to recognize story plots early enough to make useful predictions about future events. Work is currently done on the VAST dataset synthesized by PNNL, which contains news stories about numerous events, including some 120 events pertaining to illegal or unethical activities in a hypothetical community. This work is led by Ashok Goel.
We are developing a Visual Analytics Digital Library (VADL) to support instructors building new visual analytics courses and students and others who simply seek to learn more about the area. The VADL includes articles, course notes, lecture slides, video lectures, homework exercises and other useful items. By accumulating all these materials at one site, we provide a convenient and easy-to-access collection. This project is led by Jim Foley.