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

Authors

62

  1. GL
    G. Lawrence Sanders

    University at Buffalo, State University of New York, United States

    2 publications · 110 citations

    2

    h-index

  2. GS
    Guangyu Shen

    Purdue University West Lafayette, United States

    1 publications · 12 citations

    1

    h-index

  3. JN
    Jari Nurmi

    1 publications · 11 citations

    1

    h-index

  4. GP
    Guillaume Pierre

    Vrije Universiteit Amsterdam, Netherlands

    1 publications · 23 citations

    1

    h-index

  5. LS
    Lucas Serrano

    Singapore Management University, Singapore

    1 publications · 22 citations

    1

    h-index

1–5 of 62

Venues

  1. Decision Support Systems

    1 publications · 54 citations

    1

    h-index

  2. arXiv (Cornell University)

    Repository · 1 publications · 12 citations

    1

    h-index

  3. Journal of Pure and Applied Algebra

    1 publications · 59 citations

    1

    h-index

  4. IEEE Transactions on Computers

    1 publications · 15 citations

    1

    h-index

  5. Anthropology & Education Quarterly

    AEQ · 1 publications · 149 citations

    1

    h-index

1–5