Provenance Data Models

Provenance Data Models is a formalized framework that specifies the structure, semantics, and relationships of recorded data describing the origin, transformation, and lineage of visual artifacts in collaborative visualization environments, distinguishing it from generic data models by explicitly encoding contextual, temporal, and agent-based dependencies inherent in collaborative visual analysis processes.

140

Publications

6.2K

Citations

481

Authors

192

Institutions

Publications per year

2017–2026

34

Authors

481

  1. LM
    Luc Moreau

    University of Southampton, United Kingdom

    13 publications · 605 citations

    11

    h-index

  2. PG
    Paul Groth

    University of Amsterdam, Netherlands

    10 publications · 749 citations

    10

    h-index

  3. JF
    Juliana Freire

    New York University, United States

    9 publications · 1.2K citations

    9

    h-index

  4. DK
    David Koop

    University of Utah, United States

    7 publications · 967 citations

    7

    h-index

  5. PM
    Paolo Missier

    Newcastle University, United Kingdom

    7 publications · 385 citations

    7

    h-index

1–5 of 481

Venues

  1. ePrints Soton (University of Southampton)

    Repository · 6 publications · 401 citations

    6

    h-index

  2. 5

    h-index

  3. Computing in Science & Engineering

    5 publications · 753 citations

    5

    h-index

  4. Future Generation Computer Systems

    FGCS · 4 publications · 93 citations

    4

    h-index

  5. Computers & Security

    2 publications · 117 citations

    3

    h-index

1–5