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Issue No. 01 - January/February (2012 vol. 32)
ISSN: 0272-1716
pp: 34-45
Jamal Alsakran , Kent State University
Yang Chen , University of North Carolina at Charlotte
Dongning Luo , University of North Carolina at Charlotte
Ye Zhao , Kent State University
Jing Yang , University of North Carolina at Charlotte
Wenwen Dou , University of North Carolina at Charlotte
Shixia Liu , Microsoft Research Asia
Streamit lets users explore visualizations of text streams without prior knowledge of the data. It incorporates incoming documents from a continuous source into an existing visualization context with automatic grouping and separation based on document similarities. Streamit generates document clusters to promote better understanding. To obtain different clusterings, users can adjust the keyword importance on the fly. Topic modeling represents the documents with higher-level semantic meanings. System performance has been optimized to achieve instantaneous animated visualization even for very large data collections. A powerful user interface allows in-depth data analysis. The video shows an example of applying our system on 1,000 US National Science Foundation Information and Intelligent Systems award abstracts funded between March 2000 and August 2003. The visual layout consists of a main window (left view), an animation control panel (bottom), control tools (top right), a keyword table (middle right), and document tables (bottom right). Documents are represented by pies whose size conveys the project's funding. The example shows how clusters of documents are generated and dynamically evolve (move, split, or merge) as new documents are inserted. The simulation places new documents relatively close to similar ones, creating clusters that each have an assigned color. Clusters maintain their colors, which facilitates the visual tracking of their behavior. However, when the system generates new clusters (for example, a cluster splits into two or more clusters), it assigns them unique colors to ease the visual tracking of them as they evolve. For example, in the video, the section from 00:21 to 00:25 shows how the red cluster splits into two clusters: a cluster that keeps the same red color and a new light-blue cluster. Finally, the spiral view (00:32–00:35) lets users examine the clusters' temporal trends.
Streamit, data analysis, text analysis, keyword importance, document analysis, visual analytics, force-directed model, dynamic keyword importance, GPU acceleration, computer graphics

J. Alsakran et al., "Real-Time Visualization of Streaming Text with a Force-Based Dynamic System," in IEEE Computer Graphics and Applications, vol. 32, no. , pp. 34-45, 2011.
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