Data Discrepancy Detection

Data Discrepancy Detection is a systematic methodological framework within data quality assessment that identifies and characterizes inconsistencies, anomalies, or deviations in datasets by comparing observed data against established expectations, constraints, or reference standards.

48

Publications

2.8K

Citations

157

Authors

84

Institutions

Publications per year

2017–2026

17

Authors

157

  1. RB
    Renato Bruni

    Sapienza University of Rome, Italy

    2 publications · 36 citations

    2

    h-index

  2. CS
    Christina Spolsky

    University of Illinois Urbana-Champaign, United States

    2 publications · 36 citations

    2

    h-index

  3. JF
    Johann-Christoph Freytag

    Humboldt-Universität zu Berlin, Germany

    2 publications · 32 citations

    2

    h-index

  4. VR
    Vijayshankar Raman

    IBM Research - Almaden, United States

    2 publications · 509 citations

    2

    h-index

  5. UL
    Ulf Leser

    Humboldt-Universität zu Berlin, Germany

    2 publications · 32 citations

    2

    h-index

1–5 of 157

Venues

  1. Remote Sensing

    2 publications · 84 citations

    2

    h-index

  2. Annals of the New York Academy of Sciences

    2 publications · 36 citations

    2

    h-index

  3. Optimization methods & software

    1 publications · 23 citations

    1

    h-index

  4. CHI Conference on Human Factors in Computing Systems

    Conference · 1 publications · 37 citations

    1

    h-index

  5. Teaching Statistics

    1 publications · 19 citations

    1

    h-index

1–5