Tabular Data Interpretation

Tabular Data Interpretation is a methodological framework within data interpretation assessment that specifically involves the analysis and extraction of meaningful patterns, relationships, or insights from structured numerical or categorical data organized in rows and columns.

94

Publications

11.1K

Citations

260

Authors

126

Institutions

Publications per year

2017–2026

38

Authors

260

  1. SG
    Sumit Gulwani

    Microsoft (United States), United States

    3 publications · 348 citations

    3

    h-index

  2. WR
    William R. Harris

    Roche (United Kingdom), United Kingdom

    2 publications · 247 citations

    2

    h-index

  3. MB
    Mohit Bansal

    University of North Carolina at Chapel Hill, United States

    2 publications · 250 citations

    2

    h-index

  4. DS
    Daniel S. Nagin

    Carnegie Mellon University, United States

    2 publications · 1.9K citations

    2

    h-index

  5. PH
    Peter Hase

    University of North Carolina at Chapel Hill, United States

    2 publications · 250 citations

    2

    h-index

1–5 of 260

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 142 citations

    5

    h-index

  2. Proceedings of the VLDB Endowment

    PVLDB · 3 publications · 148 citations

    3

    h-index

  3. Biometrika

    3 publications · 254 citations

    3

    h-index

  4. Data series

    2 publications · 47 citations

    2

    h-index

  5. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 3 publications · 228 citations

    2

    h-index

1–5