Data Quality Cleaning

Data Quality Cleaning is a methodological process within data management that systematically identifies, corrects, and eliminates inaccuracies, inconsistencies, and redundancies in datasets to ensure compliance with predefined quality standards.

269

Publications

13.6K

Citations

1.3K

Authors

530

Institutions

Publications per year

2017–2026

143

Authors

1.3K

  1. IF
    Ihab F. Ilyas

    University of Waterloo, Canada

    7 publications · 1.2K citations

    7

    h-index

  2. MO
    Mourad Ouzzani

    Purdue University West Lafayette, United States

    5 publications · 793 citations

    5

    h-index

  3. NT
    Nan Tang

    Tsinghua University, China

    5 publications · 759 citations

    5

    h-index

  4. AK
    Ahmed K. Elmagarmid

    Purdue University West Lafayette, United States

    4 publications · 656 citations

    4

    h-index

  5. PP
    Paolo Papotti

    EURECOM, France

    4 publications · 760 citations

    4

    h-index

1–5 of 1.3K

Venues

  1. PLoS ONE

    6 publications · 648 citations

    7

    h-index

  2. Proceedings of the VLDB Endowment

    PVLDB · 6 publications · 512 citations

    6

    h-index

  3. arXiv (Cornell University)

    Repository · 5 publications · 149 citations

    5

    h-index

  4. Procedia Computer Science

    3 publications · 152 citations

    3

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 3 publications · 113 citations

    3

    h-index

1–5