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RadarViewer : Visualizing the dynamics of multivariate data

Authors:

Jie Li,

Tommy DangAuthors Info & Claims

PEARC '20: Practice and Experience in Advanced Research Computing 2020: Catch the Wave

Pages 555 - 556

https://doi.org/10.1145/3311790.3404538

Published: 26 July 2020 Publication History

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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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Kale BSun MPapka M(2023)The State of the Art in Visualizing Dynamic Multivariate NetworksComputer Graphics Forum10.1111/cgf.1485642:3(471-490)Online publication date: 27-Jun-2023
https://doi.org/10.1111/cgf.14856
Dang TNguyen N(2022)MultiProjector: Temporal Projection for Multivariates Time SeriesAdvances in Visual Computing10.1007/978-3-031-20713-6_7(91-102)Online publication date: 3-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-20713-6_7
Dang TNguyen HNguyen N(2021)VixLSTM: Visual Explainable LSTM for Multivariate Time SeriesProceedings of the 12th International Conference on Advances in Information Technology10.1145/3468784.3471603(1-5)Online publication date: 29-Jun-2021
https://dl.acm.org/doi/10.1145/3468784.3471603
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PEARC '20: Practice and Experience in Advanced Research Computing 2020: Catch the Wave

July 2020

556 pages

ISBN:9781450366892

DOI:10.1145/3311790

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 26 July 2020

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PEARC '20: Practice and Experience in Advanced Research Computing

July 26 - 30, 2020

OR, Portland, USA

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Kale BSun MPapka M(2023)The State of the Art in Visualizing Dynamic Multivariate NetworksComputer Graphics Forum10.1111/cgf.1485642:3(471-490)Online publication date: 27-Jun-2023
https://doi.org/10.1111/cgf.14856
Dang TNguyen N(2022)MultiProjector: Temporal Projection for Multivariates Time SeriesAdvances in Visual Computing10.1007/978-3-031-20713-6_7(91-102)Online publication date: 3-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-20713-6_7
Dang TNguyen HNguyen N(2021)VixLSTM: Visual Explainable LSTM for Multivariate Time SeriesProceedings of the 12th International Conference on Advances in Information Technology10.1145/3468784.3471603(1-5)Online publication date: 29-Jun-2021
https://dl.acm.org/doi/10.1145/3468784.3471603
Nguyen BHewett RDang T(2021)NetScatter: Visual analytics of multivariate time series with a hybrid of dynamic and static variable relationships2021 IEEE 14th Pacific Visualization Symposium (PacificVis)10.1109/PacificVis52677.2021.00015(52-60)Online publication date: Apr-2021
https://doi.org/10.1109/PacificVis52677.2021.00015
Dang TNguyen NChen Y(2021)HiperView: real-time monitoring of dynamic behaviors of high-performance computing centersThe Journal of Supercomputing10.1007/s11227-021-03724-577:10(11807-11826)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1007/s11227-021-03724-5

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