RadarViewer: Visualizing the dynamics of multivariate data


This showcase presents a visual approach based on clustering and superimposing to construct a high-level overview of sequential event data while balancing the amount of information and the cardinality in it. We also implement an interactive prototype, called RadarViewer , that allows domain analysts to simultaneously analyze sequence clustering, extract useful distribution patterns, drill multiple levels-of-detail to accelerate the analysis. The RadarViewer is demonstrated through case studies with real-world temporal datasets of different sizes.

In Practice and Experience in Advanced Research Computing
Jie Li
Jie Li
Ph.D. candidate in Computer Science

My research interests include High-Performance Computing, Advanced Computer Architecture, and Parallel and Distributed Computing.