Data Denormalization
Data Denormalization is a data modeling technique that involves intentionally introducing redundancy into a database schema by combining normalized tables or attributes, thereby deviating from normalization principles to improve read performance or simplify query structures.
21
Publications
791
Citations
62
Authors
32
Institutions
Publications per year
2017–2026
5
Parents
21
Demystifying Data Construction and Analysis
Frederick Erickson · Anthropology & Education Quarterly · 2004 · 149 citations
Dominique Bourn · Journal of Pure and Applied Algebra · 2002 · 59 citations
1–5 of 21
62
University at Buffalo, State University of New York, United States
2 publications · 110 citations
2
h-index
1
h-index
1 publications · 11 citations
1
h-index
1
h-index
1
h-index
1–5 of 62
32
University at Buffalo, State University of New York
Buffalo, United States
2 h-index
3 publications
166 citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
2 h-index
6 publications
120 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
2 publications
24 citations
Instituto de Telecomunicações
Lisbon, Portugal
1 h-index
1 publications
11 citations
California Department of Education
Sacramento, United States
1 h-index
1 publications
149 citations
Petra Christian University
Surabaya, Indonesia
1 h-index
2 publications
20 citations
Columbia University
New York, United States
1 h-index
3 publications
36 citations
Singapore Management University
Singapore, Singapore
1 h-index
5 publications
110 citations
1–8 of 32
1 publications · 54 citations
1
h-index
Repository · 1 publications · 12 citations
1
h-index
1 publications · 59 citations
1
h-index
1 publications · 15 citations
1
h-index
AEQ · 1 publications · 149 citations
1
h-index
1–5