Causal Provenance

Causal Provenance is a type of data provenance that captures and represents the causal dependencies between data artifacts, specifically identifying which inputs and processes directly influence the generation of a given output.

15

Publications

874

Citations

27

Authors

16

Institutions

Publications per year

2017–2026

7

Authors

27

  1. DS
    Dan Suciu

    University of Washington, United States

    5 publications · 384 citations

    5

    h-index

  2. AM
    Alexandra Meliou

    University of Massachusetts Amherst, United States

    5 publications · 384 citations

    5

    h-index

  3. WG
    Wolfgang Gatterbauer

    University of Washington, United States

    4 publications · 310 citations

    4

    h-index

  4. KF
    Katherine F. Moore

    University of Washington, United States

    3 publications · 290 citations

    3

    h-index

  5. FW
    Fei Wang

    Xi'an Jiaotong University, China

    1 publications · 49 citations

    1

    h-index

1–5 of 27

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 2 publications · 259 citations

    2

    h-index

  2. Evaluation

    1 publications · 16 citations

    1

    h-index

  3. Infoscience (Ecole Polytechnique Fédérale de Lausanne)

    Repository · 1 publications · 88 citations

    1

    h-index

  4. arXiv (Cornell University)

    Repository · 1 publications · 17 citations

    1

    h-index

  5. Proceedings of the National Academy of Sciences

    PNAS · 1 publications · 71 citations

    1

    h-index

1–5