Data Quality Error Detection

Data Quality Error Detection is a systematic process within data quality management that identifies deviations from accuracy, completeness, consistency, and reliability standards in datasets through algorithmic, statistical, or rule-based methods to enable corrective actions.

32

Publications

1.5K

Citations

110

Authors

58

Institutions

Publications per year

2017–2026

12

Authors

110

  1. IF
    Ihab F. Ilyas

    University of Waterloo, Canada

    5 publications · 682 citations

    5

    h-index

  2. MO
    Mourad Ouzzani

    Purdue University West Lafayette, United States

    4 publications · 642 citations

    4

    h-index

  3. NT
    Nan Tang

    Tsinghua University, China

    3 publications · 589 citations

    3

    h-index

  4. DS
    Divesh Srivastava

    AT&T (United States), United States

    2 publications · 167 citations

    2

    h-index

  5. AK
    Ahmed K. Elmagarmid

    Purdue University West Lafayette, United States

    2 publications · 352 citations

    2

    h-index

1–5 of 110

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 5 publications · 467 citations

    5

    h-index

  2. Water Science & Technology

    2 publications · 38 citations

    2

    h-index

  3. arXiv (Cornell University)

    Repository · 2 publications · 79 citations

    2

    h-index

  4. American journal of environmental engineering

    AJEE · 1 publications · 11 citations

    1

    h-index

  5. Semantic Web

    1 publications · 33 citations

    1

    h-index

1–5