Government Data Mining

Government Data Mining is a methodological framework within artificial intelligence in government that systematically extracts, analyzes, and interprets large-scale public sector datasets to identify patterns, trends, and relationships for policy-making, regulatory compliance, and administrative decision-making.

75

Publications

3K

Citations

202

Authors

109

Institutions

Publications per year

2017–2026

39

Authors

202

  1. JH
    James Hendler

    University of Maryland, College Park, United States

    2 publications · 69 citations

    2

    h-index

  2. TZ
    Tal Zarsky

    University of Haifa, Israel

    2 publications · 50 citations

    2

    h-index

  3. DD
    Dean Duncan

    University of North Carolina at Chapel Hill, United States

    2 publications · 34 citations

    2

    h-index

  4. HK
    Hye‐Chung Kum

    Texas A&M University, United States

    2 publications · 34 citations

    2

    h-index

  5. HW
    Hon Wai Chun

    City University of Hong Kong, Hong Kong SAR China

    2 publications · 34 citations

    2

    h-index

1–5 of 202

Venues

  1. Government Information Quarterly

    6 publications · 490 citations

    6

    h-index

  2. SSRN Electronic Journal

    Repository · 5 publications · 94 citations

    6

    h-index

  3. eYLS (Yale Law School)

    Repository · 2 publications · 50 citations

    2

    h-index

  4. International Conference on Digital Government Research

    Conference · 2 publications · 29 citations

    2

    h-index

  5. 1

    h-index

1–5