Granularity Matching
Granularity Matching is a methodological principle in computational or data-intensive research that ensures correspondence between the level of detail in data representation and the scale of analysis, thereby preserving informational coherence and analytical validity.
4
Publications
61
Citations
19
Authors
8
Institutions
Publications per year
2017–2026
4
Parents
4
Wanqiu Zhang, Xander Spotbeen, Sebastiaan Vanuytven et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2023 · 18 citations · Full text
Bringing Modular Concurrency Control to the Next Level
Chunzhi Su, Natacha Crooks, Cong Ding et al. · 2017 · 18 citations · Full text
Tao Li, Lei Ma, Zheng Liu et al. · Journal of Open Innovation Technology Market and Complexity · 2020 · 15 citations · Full text
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8

Cornell University
Ithaca, United States
1 h-index
2 publications
36 citations
Guangdong Institute of Intelligent Manufacturing
Guangzhou, China
1 h-index
1 publications
17 citations
Nanjing University of Science and Technology
Nanjing, China
1 h-index
3 publications
45 citations
Liaoning Normal University
Dalian, China
1 h-index
5 publications
55 citations

Foshan University
Foshan, China
1 h-index
1 publications
17 citations
The University of Texas at Austin
Austin, United States
1 h-index
4 publications
72 citations

Nanjing University of Information Science and Technology
Nanjing, China
1 h-index
1 publications
11 citations
Cardiff Metropolitan University
Cardiff, United Kingdom
1 h-index
1 publications
15 citations
1–8 of 8
JOItmC · 1 publications · 15 citations
1
h-index
1 publications · 17 citations
1
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1 publications · 11 citations
1
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Repository · 1 publications · 18 citations
IJGI · 1 publications · 12 citations
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