Data Model Evolution

Data Model Evolution is a formal process that systematically modifies and refines a data model over time to accommodate changing requirements, structures, or semantics while preserving data integrity and consistency within its evolving computational and organizational context.

80

Publications

4K

Citations

211

Authors

92

Institutions

Publications per year

2017–2026

12

Authors

211

  1. HF
    Henry F. Korth

    The University of Texas at Austin, United States

    3 publications · 809 citations

    3

    h-index

  2. PV
    Panos Vassiliadis

    University of Ioannina, Greece

    3 publications · 95 citations

    3

    h-index

  3. HK
    Hyoung-Joo Kim

    Seoul National University, South Korea

    3 publications · 809 citations

    3

    h-index

  4. AH
    Andrea Hillenbrand

    Darmstadt University of Applied Sciences, Germany

    3 publications · 44 citations

    3

    h-index

  5. MK
    Meike Klettke

    University of Rostock, Germany

    3 publications · 44 citations

    3

    h-index

1–5 of 211

Venues

  1. Data & Knowledge Engineering

    DKE · 7 publications · 240 citations

    7

    h-index

  2. Knowledge and Information Systems

    3 publications · 615 citations

    3

    h-index

  3. Computer Standards & Interfaces

    2 publications · 47 citations

    2

    h-index

  4. ACM SIGMOD Record

    2 publications · 168 citations

    2

    h-index

  5. Survey Review

    2 publications · 51 citations

    2

    h-index

1–5