Data Type Inference

Data Type Inference is a data profiling method that identifies the syntactic and semantic type of a column in a dataset by analyzing its values and patterns, distinguishing it from other schema discovery techniques through its focus on automated type classification.

223

Publications

13.7K

Citations

448

Authors

205

Institutions

Publications per year

2017–2026

34

Authors

448

  1. JP
    Jens Palsberg

    University of California, Los Angeles, United States

    15 publications · 894 citations

    13

    h-index

  2. MS
    Martin Sulzmann

    National University of Singapore, Singapore

    7 publications · 263 citations

    7

    h-index

  3. MI
    Michael I. Schwartzbach

    Aarhus University, Denmark

    7 publications · 510 citations

    7

    h-index

  4. SP
    Simon Peyton Jones

    Microsoft Research (United Kingdom), United Kingdom

    6 publications · 581 citations

    6

    h-index

  5. DV
    Dimitrios Vytiniotis

    Microsoft Research (United Kingdom), United Kingdom

    5 publications · 532 citations

    5

    h-index

1–5 of 448

Venues

  1. ACM SIGPLAN Notices

    13 publications · 349 citations

    11

    h-index

  2. 8

    h-index

  3. Journal of Functional Programming

    7 publications · 546 citations

    7

    h-index

  4. Information and Computation

    6 publications · 402 citations

    6

    h-index

  5. arXiv (Cornell University)

    Repository · 6 publications · 900 citations

    6

    h-index

1–5