Composition and Configuration Patterns in Multiple-View Visualizations

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Composition and Configuration Patterns in Multiple-View Visualizations. / Chen, Xi; Zeng, Wei; Al-Maneea, Hayder Mahdi Abdullah; Roberts, Jonathan C.; Chang, Remco.

Yn: IEEE Transactions on visualization and computer graphics, Cyfrol 27, Rhif 2, 02.2021, t. 1514-1524.

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygl

HarvardHarvard

Chen, X, Zeng, W, Al-Maneea, HMA, Roberts, JC & Chang, R 2021, 'Composition and Configuration Patterns in Multiple-View Visualizations', IEEE Transactions on visualization and computer graphics, cyfrol. 27, rhif 2, tt. 1514-1524. https://doi.org/10.1109/TVCG.2020.3030338

APA

Chen, X., Zeng, W., Al-Maneea, H. M. A., Roberts, J. C., & Chang, R. (2021). Composition and Configuration Patterns in Multiple-View Visualizations. IEEE Transactions on visualization and computer graphics, 27(2), 1514-1524. https://doi.org/10.1109/TVCG.2020.3030338

CBE

Chen X, Zeng W, Al-Maneea HMA, Roberts JC, Chang R. 2021. Composition and Configuration Patterns in Multiple-View Visualizations. IEEE Transactions on visualization and computer graphics. 27(2):1514-1524. https://doi.org/10.1109/TVCG.2020.3030338

MLA

Chen, Xi et al. "Composition and Configuration Patterns in Multiple-View Visualizations". IEEE Transactions on visualization and computer graphics. 2021, 27(2). 1514-1524. https://doi.org/10.1109/TVCG.2020.3030338

VancouverVancouver

Chen X, Zeng W, Al-Maneea HMA, Roberts JC, Chang R. Composition and Configuration Patterns in Multiple-View Visualizations. IEEE Transactions on visualization and computer graphics. 2021 Feb;27(2):1514-1524. https://doi.org/10.1109/TVCG.2020.3030338

Author

Chen, Xi ; Zeng, Wei ; Al-Maneea, Hayder Mahdi Abdullah ; Roberts, Jonathan C. ; Chang, Remco. / Composition and Configuration Patterns in Multiple-View Visualizations. Yn: IEEE Transactions on visualization and computer graphics. 2021 ; Cyfrol 27, Rhif 2. tt. 1514-1524.

RIS

TY - JOUR

T1 - Composition and Configuration Patterns in Multiple-View Visualizations

AU - Chen, Xi

AU - Zeng, Wei

AU - Al-Maneea, Hayder Mahdi Abdullah

AU - Roberts, Jonathan C.

AU - Chang, Remco

PY - 2021/2

Y1 - 2021/2

N2 - Multiple-view visualization (MV) is a layout design technique often employed to help users see a large number of data attributes and values in a single cohesive representation. Because of its generalizability, the MV design has been widely adopted by the visualization community to help users examine and interact with large, complex, and high-dimensional data. However, although ubiquitous, there has been little work to categorize and analyze MVs in order to better understand its design space. As a result, there has been little to no guideline in how to use the MV design effectively. In this paper, we present an in-depth study of how MVs are designed in practice. We focus on two fundamental measures of multiple-view patterns: composition, which quantifies what view types and how many are there; and configuration, which characterizes spatial arrangement of view layouts in the display space. We build a new dataset containing 360 images of MVs collected from IEEE VIS, EuroVis, and PacificVis publications 2011 to 2019, and make fine-grained annotations of view types and layouts for these visualization images. From this data we conduct composition and configuration analyses using quantitative metrics of term frequency and layout topology. We identify common practices around MVs, including relationship of view types, popular view layouts, and correlation between view types and layouts. We combine the findings into a MV recommendation system, providing interactive tools to explore the design space, and support example-based design.

AB - Multiple-view visualization (MV) is a layout design technique often employed to help users see a large number of data attributes and values in a single cohesive representation. Because of its generalizability, the MV design has been widely adopted by the visualization community to help users examine and interact with large, complex, and high-dimensional data. However, although ubiquitous, there has been little work to categorize and analyze MVs in order to better understand its design space. As a result, there has been little to no guideline in how to use the MV design effectively. In this paper, we present an in-depth study of how MVs are designed in practice. We focus on two fundamental measures of multiple-view patterns: composition, which quantifies what view types and how many are there; and configuration, which characterizes spatial arrangement of view layouts in the display space. We build a new dataset containing 360 images of MVs collected from IEEE VIS, EuroVis, and PacificVis publications 2011 to 2019, and make fine-grained annotations of view types and layouts for these visualization images. From this data we conduct composition and configuration analyses using quantitative metrics of term frequency and layout topology. We identify common practices around MVs, including relationship of view types, popular view layouts, and correlation between view types and layouts. We combine the findings into a MV recommendation system, providing interactive tools to explore the design space, and support example-based design.

KW - multiple views

KW - design pattern

KW - quantitative analysis

KW - example-based design

U2 - 10.1109/TVCG.2020.3030338

DO - 10.1109/TVCG.2020.3030338

M3 - Article

VL - 27

SP - 1514

EP - 1524

JO - IEEE Transactions on visualization and computer graphics

JF - IEEE Transactions on visualization and computer graphics

SN - 1077-2626

IS - 2

T2 - IEEE Conference on Visualization

Y2 - 25 October 2020 through 30 October 2020

ER -