Data-driven Surveillance

Data-Driven Surveillance is a methodological framework within production surveillance that systematically employs empirical data and analytical algorithms to monitor, detect, and evaluate deviations in production processes, distinguishing itself by its reliance on quantitative evidence and automated inference rather than predefined rules or subjective oversight.

39

Publications

1.6K

Citations

124

Authors

68

Institutions

Publications per year

2017–2026

27

Authors

124

  1. JB
    Jan Briers

    Shell (Netherlands), Netherlands

    3 publications · 50 citations

    3

    h-index

  2. PV
    Peter Van Overschee

    KU Leuven, Belgium

    3 publications · 50 citations

    3

    h-index

  3. CE
    Charlie E. Moncur

    Shell (Netherlands), Netherlands

    2 publications · 40 citations

    2

    h-index

  4. MA
    Mark Andrejevic

    Monash University, Australia

    2 publications · 357 citations

    2

    h-index

  5. KG
    Keat-Choon Goh

    University of Southern California, United States

    2 publications · 40 citations

    2

    h-index

1–5 of 124

Venues

  1. Surveillance & Society

    5 publications · 526 citations

    5

    h-index

  2. Big Data & Society

    2 publications · 57 citations

    2

    h-index

  3. Proceedings of the ACM on Human-Computer Interaction

    PACMHCI · 1 publications · 13 citations

    1

    h-index

  4. SPE Economics & Management

    1 publications · 11 citations

    1

    h-index

  5. The Sociological Review

    1 publications · 20 citations

    1

    h-index

1–5