Data-driven Pattern Generation
Data-Driven Pattern Generation is a generative pattern design method that systematically derives and constructs visual or structural patterns through the direct utilization of empirical data, algorithmic inference, and statistical modeling to differentiate it from rule-based or intuition-driven approaches.
20
Publications
605
Citations
57
Authors
26
Institutions
Publications per year
2017–2026
3
Parents
20
Towards a Software Engineering Process for Developing Data-Driven Applications
Marc Hesenius, Nils Schwenzfeier, Ole Meyer et al. · 2019 · 47 citations
1–5 of 20
57
1
h-index
1
h-index
1
h-index
1
h-index
1
h-index
1–5 of 57
26
Curtin University
Perth, Australia
2 h-index
2 publications
67 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
3 publications
64 citations
University of California, Irvine
Irvine, United States
1 h-index
1 publications
42 citations
Binghamton University
Binghamton, United States
1 h-index
1 publications
13 citations

Goldsmiths University of London
London, United Kingdom
1 h-index
2 publications
24 citations
University of Washington
Seattle, United States
1 h-index
1 publications
13 citations

University of Arkansas at Fayetteville
Fayetteville, United States
1 h-index
2 publications
102 citations

Berry College
Rome, United States
1 h-index
1 publications
19 citations
1–8 of 26
1 publications · 80 citations
1
h-index
1 publications · 19 citations
1
h-index
1 publications · 13 citations
1
h-index
1 publications · 51 citations
1
h-index
1 publications · 26 citations
1
h-index
1–5