Multidimensional Data Filtering

Multidimensional Data Filtering is a data filtering paradigm that operates on datasets characterized by multiple interrelated dimensions or attributes, distinguishing itself through the simultaneous application of constraints across these dimensions to enable interactive exploration and refinement of complex, high-dimensional data spaces.

47

Publications

1.6K

Citations

122

Authors

75

Institutions

Publications per year

2017–2026

3

Variants

Multivariate Data Filtering

Authors

122

  1. SB
    Salah Bourennane

    Institut Fresnel, France

    4 publications · 355 citations

    4

    h-index

  2. PT
    Philippas Tsigas

    Chalmers University of Technology, Sweden

    2 publications · 129 citations

    2

    h-index

  3. NE
    Niklas Elmqvist

    University of Maryland, College Park, United States

    2 publications · 129 citations

    2

    h-index

  4. DM
    Damien Muti

    Centre National de la Recherche Scientifique, France

    2 publications · 131 citations

    2

    h-index

  5. JS
    John Stasko

    Georgia Institute of Technology, United States

    2 publications · 129 citations

    2

    h-index

1–5 of 122

Venues

  1. Proceedings of the IEEE

    2 publications · 42 citations

    2

    h-index

  2. 2

    h-index

  3. KTH Publication Database DiVA (KTH Royal Institute of Technology)

    Repository · 2 publications · 77 citations

    2

    h-index

  4. IEEE Transactions on Medical Imaging

    1 publications · 17 citations

    1

    h-index

  5. Mathematische Zeitschrift

    1 publications · 21 citations

    1

    h-index

1–5