Our Mission

The Smart Visual Analytics Team at IBM China Research Lab is a research group, working on weaving information visualization technology into analytics. In particular, we help users to employ visualization technologies and tools in their analytic tasks, especially for visually analyzing huge complex data/information, thus allow the user to well observe and understand huge amount of information for quick information finding, pattern discovery, and decision making.

Our mission is to build information visualization techniques for various analysis-oriented tasks to impact on the academic research and create business values for IBM products and customers. Our current working focuses include:

1. Interactive, visual social network analysis
   Research and develop interactive visual metaphors and tools to allow an average Joe to quickly visualize and analyze his social networks, especially for visually analyzing huge, complex, and dynamic social data/information.
2. Interactive, visual text analysis
   Research and develop interactive visual metaphors and tools to help users visually explore and analyze text information, and then find the right information from a range of text collection to help make decisions.
3. Designing novel interaction techniques for analysis-oriented tasks
   Research and design 1) animated transition between different visualization forms; 2) smart and content-based visual mashup. These novel interaction techniques enable users to find useful patterns quickly and easily.

Our Strength

Due to the interdisciplinary nature of our mission, SVA is made up of researchers from multiple research disciplines. In particular, our strength lies in the following areas: text visualization, visual network analysis for both huge and dynamic social network data, intelligent user interfaces, information visualization framework, and visualization application design. In addition to the close intra-departmental collaboration, we have also established collaboration ties with researchers inside and outside of IBM research.




Last updated 28 Sep 2009