Data Model Alignment

Data Model Alignment is a methodological framework within cross-platform data harmonization and IoT interoperability that establishes semantic and structural consistency between heterogeneous data models by systematically mapping and reconciling their conceptual schemas, syntactic representations, and contextual metadata to enable seamless data exchange and integration across diverse platforms and devices.

36

Publications

1.9K

Citations

131

Authors

64

Institutions

Publications per year

2017–2026

10

Authors

131

  1. WV
    Wim Vranken

    Vrije Universiteit Brussel, Belgium

    2 publications · 141 citations

    2

    h-index

  2. WB
    Wayne Boucher

    University of Cambridge, United Kingdom

    2 publications · 141 citations

    2

    h-index

  3. RH
    Rasmus H. Fogh

    University of Cambridge, United Kingdom

    2 publications · 141 citations

    2

    h-index

  4. ED
    Ernest D. Laue

    University of Cambridge, United Kingdom

    2 publications · 141 citations

    2

    h-index

  5. VM
    Vânia Maria Ponte Vidal

    Universidade Federal do Ceará, Brazil

    1 publications · 26 citations

    1

    h-index

1–5 of 131

Venues

  1. PubMed

    Repository · 1 publications · 12 citations

    1

    h-index

  2. 1

    h-index

  3. Journal of Biomolecular NMR

    1 publications · 15 citations

    1

    h-index

  4. Nature Structural Biology

    1 publications · 126 citations

    1

    h-index

  5. Journal of Physics Conference Series

    1 publications · 53 citations

    1

    h-index

1–5