RadarViewer: Visualizing the dynamics of multivariate data

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

doi:10.1145/3311790.3404538

RadarViewer: Visualizing the dynamics of multivariate data

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

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	PEARC 2020 - Practice and Experience in Advanced Research Computing 2020
Subtitle of host publication	Catch the Wave
Publisher	Association for Computing Machinery
Pages	555-556
Number of pages	2
ISBN (Electronic)	9781450366892
DOIs	https://doi.org/10.1145/3311790.3404538
State	Published - Jul 26 2020
Event	2020 Conference on Practice and Experience in Advanced Research Computing: Catch the Wave, PEARC 2020 - Virtual, Online, United States Duration: Jul 27 2020 → Jul 31 2020

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2020 Conference on Practice and Experience in Advanced Research Computing: Catch the Wave, PEARC 2020
Country	United States
City	Virtual, Online
Period	07/27/20 → 07/31/20

Keywords

Radar chart
multivariate data analysis
time-series visualization

Access to Document

10.1145/3311790.3404538

Link to publication in Scopus

Cite this

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title = "RadarViewer: Visualizing the dynamics of multivariate data",

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.",

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Nguyen, N, Hass, J, Chen, Y, Li, J, Sill, A & Dang, T 2020, RadarViewer: Visualizing the dynamics of multivariate data. in PEARC 2020 - Practice and Experience in Advanced Research Computing 2020: Catch the Wave. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 555-556, 2020 Conference on Practice and Experience in Advanced Research Computing: Catch the Wave, PEARC 2020, Virtual, Online, United States, 07/27/20. https://doi.org/10.1145/3311790.3404538

RadarViewer : Visualizing the dynamics of multivariate data. / Nguyen, Ngan; Hass, Jon; Chen, Yong; Li, Jie; Sill, Alan; Dang, Tommy.

PEARC 2020 - Practice and Experience in Advanced Research Computing 2020: Catch the Wave. Association for Computing Machinery, 2020. p. 555-556 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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