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STREAMIT: Dynamic visualization and interactive exploration of text streams

Jamal Alsakran, Yang Chen, Ye Zhao, Jing Yang, Dongning Luo
Kent State University
IEEE Pacific Visualization Symposium (PacificVis), 2011

@inproceedings{alsakran2011streamit,

   title={STREAMIT: Dynamic visualization and interactive exploration of text streams},

   author={Alsakran, J. and Chen, Y. and Zhao, Y. and Yang, J. and Luo, D.},

   booktitle={Pacific Visualization Symposium (PacificVis), 2011 IEEE},

   pages={131–138},

   year={2011},

   organization={IEEE}

}

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Text streams demand an effective, interactive, and on-the-fly method to explore the dynamic and massive data sets, and meanwhile extract valuable information for visual analysis. In this paper, we propose such an interactive visualization system that enables users to explore streaming-in text documents without prior knowledge of the data. The system can constantly incorporate incoming documents from a continuous source into existing visualization context, which is "physically" achieved by minimizing a potential energy defined from similarities between documents. Unlike most existing methods, our system uses dynamic keyword vectors to incorporate newly-introduced keywords from data streams. Furthermore, we propose a special keyword importance that makes it possible for users to adjust the similarity on-the-fly, and hence achieve their preferred visual effects in accordance to varying interests, which also helps to identify hot spots and outliers. We optimize the system performance through a similarity grid and with parallel implementation on graphics hardware (GPU), which achieves instantaneous animated visualization even for a very large data collection. Moreover, our system implements a powerful user interface enabling various user interactions for in-depth data analysis. Experiments and case studies are presented to illustrate our dynamic system for text stream exploration.
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