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Center for Data-Driven Discovery Projects

Dynamic Visualization and Visual Analytics for Scientific Data 

This project is focusing on research into novel streaming visualization and visual analytics paradigms. Two important user facilities at BNL: CFN and NSLS-II will participate in this research, providing the use cases that will drive our research efforts into scalable, streaming visualization and visual analytics approaches for extreme-scale data analysis. In particular, we will investigate interactive visual analysis paradigms that can deliver:

  1. Customizable approaches to synthesize information towards insight derivation from high velocity, high volume and complex data,
  2. Interaction approaches that enable scientists to interact with the data in support of their discovery and decision making process, and
  3. Support steering of experimental processes to optimize scientific outcomes.

To our knowledge, there are no existing solutions satisfying the unique requirements of NSLS-II, i.e. the streaming visualization and visual analytic environment for extreme-scale data and complex analyses so that scientists can better understand and interact with their data, analysis methods and their relationships. Moreover, it is challenging to find a tunable approach to bridge the gap between advanced visual representations and the common practice which scientists are used to and grown comfortable with.  Our system development and evaluation approach will therefore be formative, soliciting feedback from collaborating scientists at every step in the course of science discovery.

Visualization and visual analytics are indispensible for improving scientific understanding, pace of scientific discovery, and usability of data analysis methods, which are essential for the success of future NSLS-II and CFN experiments. On success of this project, we expect to secure future funding from DOE ASCR programs including streaming scientific data management, analysis and visualization at extreme scale, and performance and productivity tools. The research will also foster cross-disciplinary collaboration by extending research outcomes into other BNL science programs, such as climate sciences, biology, and nuclear physics.

data visualization