Large-Scale Data Exploration is a systematic computational and analytical approach to examining vast, complex datasets at scale, distinguished by its focus on enabling efficient, interactive, and insightful discovery of patterns, structures, and anomalies across high-dimensional, heterogeneous, or distributed data sources.
530
Publications
46.5K
Citations
2.3K
Authors
866
Institutions
Publications per year
2017–2026
202
Parents
Publications
530
CRITICAL QUESTIONS FOR BIG DATA
danah boyd, Kate Crawford · Information Communication & Society · 2012 · 5.3K citations
The Fourth Paradigm: Data-Intensive Scientific Discovery
Iain Buchan, John Winn, Chris Bishop · 2009 · 1.4K citations
1–5 of 530
Authors
2.3K
- KW
9
h-index
- HC
7
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- OR
6
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5
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- HS
5
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1–5 of 2.3K
Institutions
866
Lawrence Berkeley National Laboratory
Berkeley, United States
11 h-index
65 publications
4.1K citations
The Ohio State University
Columbus, United States
11 h-index
35 publications
1.3K citations
University of California, Berkeley
Berkeley, United States
10 h-index
21 publications
672 citations
Microsoft (United States)
Redmond, United States
10 h-index
47 publications
14.3K citations
Chinese Academy of Sciences
Beijing, China
10 h-index
14 publications
1.8K citations
University of Oxford
Oxford, United Kingdom
9 h-index
33 publications
1.8K citations
University of California, Davis
Davis, United States
8 h-index
24 publications
1.2K citations
University of Wisconsin–Madison
Madison, United States
8 h-index
14 publications
787 citations
1–8 of 866
Venues
- IEEE Transactions on Visualization and Computer Graphics
TVCG · 16 publications · 1.1K citations
15
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- arXiv (Cornell University)
Repository · 14 publications · 653 citations
13
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- Communications of the ACM
12 publications · 1.4K citations
12
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- PLoS ONE
10 publications · 3.2K citations
10
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- Nucleic Acids Research
6 publications · 407 citations
6
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