Data Quality Completeness

Data Quality Completeness is a data quality dimension that pertains to the extent to which all required data elements are present in a dataset, distinguishing itself from other dimensions by focusing specifically on the absence of missing values relative to a defined domain or business rule.

29

Publications

1.4K

Citations

105

Authors

44

Institutions

Publications per year

2017–2026

17

Authors

105

  1. IT
    Ikbal Taleb

    United Arab Emirates University, United Arab Emirates

    3 publications · 166 citations

    3

    h-index

  2. MA
    Mohamed Adel Serhani

    United Arab Emirates University, United Arab Emirates

    3 publications · 166 citations

    3

    h-index

  3. SJ
    Suraj Juddoo

    Middlesex University, Mauritius

    2 publications · 52 citations

    2

    h-index

  4. FN
    Felix Naumann

    Hasso Plattner Institute, Germany

    2 publications · 272 citations

    2

    h-index

  5. HH
    Hazar Harmouch

    University of Potsdam, Germany

    2 publications · 272 citations

    2

    h-index

1–5 of 105

Venues

  1. PLoS ONE

    2 publications · 68 citations

    2

    h-index

  2. Big Data and Cognitive Computing

    1 publications · 32 citations

    1

    h-index

  3. arXiv (Cornell University)

    Repository · 1 publications · 137 citations

    1

    h-index

  4. Journal of the American Medical Informatics Association

    JAMIA · 1 publications · 55 citations

    1

    h-index

  5. Applied System Innovation

    1 publications · 40 citations

    1

    h-index

1–5