Scalable Visualization Techniques
Scalable Visualization Techniques is a systematic approach in information visualization that enables the effective rendering, interaction, and interpretation of datasets across varying scales of data size and complexity while maintaining performance and perceptual clarity.
184
Publications
9.2K
Citations
620
Authors
205
Institutions
Publications per year
2017–2026
41
Parents
184
Efficient Test and Visualization of Multi-Set Intersections
Minghui Wang, Yongzhong Zhao, Bin Zhang · Scientific Reports · 2015 · 477 citations · Full text
<i>imMens</i>: Real‐time Visual Querying of Big Data
Zhicheng Liu, Biye Jiang, Jeffrey Heer · Computer Graphics Forum · 2013 · 318 citations
VisTrails: Enabling Interactive Multiple-View Visualizations
Louis Bavoil, Steven P. Callahan, Carlos Scheidegger et al. · 2006 · 296 citations
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205
University of Utah
Salt Lake City, United States
7 h-index
29 publications
3.4K citations
University of California, Davis
Davis, United States
6 h-index
11 publications
511 citations
Argonne National Laboratory
Lemont, United States
6 h-index
18 publications
1.2K citations
Harvard University
Cambridge, United States
6 h-index
19 publications
1.2K citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
5 h-index
5 publications
539 citations

Harvard University Press
Cambridge, United States
5 h-index
10 publications
1K citations
University of Konstanz
Konstanz, Germany
5 h-index
12 publications
705 citations
Tufts University
Medford, United States
5 h-index
6 publications
213 citations
1–8 of 205
TVCG · 22 publications · 1.9K citations
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10 publications · 562 citations
10
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9 publications · 569 citations
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PVLDB · 4 publications · 123 citations
4
h-index
FGCS · 2 publications · 102 citations
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