RadarViewer: Visualizing the dynamics of multivariate data

Ngan Nguyen, Jon Hass, Yong Chen, Jie Li, Alan Sill, Tommy Dang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationPEARC 2020 - Practice and Experience in Advanced Research Computing 2020
Subtitle of host publicationCatch the Wave
PublisherAssociation for Computing Machinery
Pages555-556
Number of pages2
ISBN (Electronic)9781450366892
DOIs
StatePublished - Jul 26 2020
Event2020 Conference on Practice and Experience in Advanced Research Computing: Catch the Wave, PEARC 2020 - Virtual, Online, United States
Duration: Jul 27 2020Jul 31 2020

Publication series

NameACM International Conference Proceeding Series

Conference

Conference2020 Conference on Practice and Experience in Advanced Research Computing: Catch the Wave, PEARC 2020
Country/TerritoryUnited States
CityVirtual, Online
Period07/27/2007/31/20

Keywords

  • Radar chart
  • multivariate data analysis
  • time-series visualization

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