RadarViewer: Visualizing the dynamics of multivariate data

Published in Practice and Experience in Advanced Research Computing (PEARC), 2020

Recommended citation: Nguyen, Ngan, Jon Hass, Yong Chen, Jie Li, Alan Sill, and Tommy Dang. "RadarViewer: Visualizing the dynamics of multivariate data." In Practice and Experience in Advanced Research Computing, pp. 555-556. 2020. https://artlands.github.io/files/ngan-pearc-2020.pdf

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.

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Recommended citation:

@incollection{nguyen2020radarviewer,
  title={RadarViewer: Visualizing the dynamics of multivariate data},
  author={Nguyen, Ngan and Hass, Jon and Chen, Yong and Li, Jie and Sill, Alan and Dang, Tommy},
  booktitle={Practice and Experience in Advanced Research Computing},
  pages={555--556},
  year={2020}
}