Automated Data Quality Rules

Automated Data Quality Rules is a formal method in data management that systematically enforces predefined data quality constraints through algorithmic validation and correction processes to ensure consistency, accuracy, and completeness of data without manual intervention.

9

Publications

528

Citations

26

Authors

20

Institutions

Publications per year

2017–2026

4

Authors

26

  1. PP
    Paolo Papotti

    EURECOM, France

    2 publications · 348 citations

    2

    h-index

  2. IF
    Ihab F. Ilyas

    University of Waterloo, Canada

    2 publications · 348 citations

    2

    h-index

  3. XL
    Xiaochun Li

    Indiana University – Purdue University Indianapolis, United States

    1 publications · 15 citations

    1

    h-index

  4. JG
    Jianwen Guo

    Chinese Academy of Sciences, China

    1 publications · 15 citations

    1

    h-index

  5. JZ
    Jingren Zhou

    Alibaba Group (United States), United States

    1 publications · 40 citations

    1

    h-index

1–5 of 26

Venues

  1. Studies in health technology and informatics

    Book series · 1 publications · 15 citations

    1

    h-index

  2. Information & Management

    1 publications · 44 citations

    1

    h-index

  3. ICIQ

    1 publications · 12 citations

    1

    h-index

  4. IEEE Geoscience and Remote Sensing Letters

    GRSL · 1 publications · 15 citations

    1

    h-index

  5. Proceedings of the VLDB Endowment

    PVLDB · 1 publications · 40 citations

    1

    h-index

1–5