Data Incident Detection

Data Incident Detection is a methodological framework within data observability that identifies deviations from expected data behavior by systematically monitoring data pipelines for anomalies, inconsistencies, or quality violations in real time.

6

Publications

251

Citations

30

Authors

15

Institutions

Publications per year

2017–2026

1

Authors

30

  1. ZK
    Zbigniew Kalbarczyk

    University of Illinois Urbana-Champaign, United States

    1 publications · 33 citations

    1

    h-index

  2. SD
    Sheng Di

    Argonne National Laboratory, United States

    1 publications · 14 citations

    1

    h-index

  3. KC
    Kennis Chan

    Atac (United States), United States

    1 publications · 11 citations

    1

    h-index

  4. ML
    Mao Lin Huang

    University of Technology Sydney, Australia

    1 publications · 16 citations

    1

    h-index

  5. DC
    Domenico Cotroneo

    University of Naples Federico II, Italy

    1 publications · 33 citations

    1

    h-index

1–5 of 30

Venues

  1. Decision Support Systems

    1 publications · 164 citations

    1

    h-index

  2. 2013 Aviation Technology, Integration, and Operations Conference

    Conference · 1 publications · 11 citations

    1

    h-index

  3. 1

    h-index

  4. Sustainable Computing Informatics and Systems

    1 publications · 14 citations

    1

    h-index

1–4