Scalable Visual Analytics

Scalable Visual Analytics is a computational framework within visual data mining that integrates interactive visualization techniques with scalable algorithms to enable efficient, real-time exploration and analysis of large-scale, high-dimensional datasets.

115

Publications

5.7K

Citations

427

Authors

160

Institutions

Publications per year

2017–2026

35

Authors

427

  1. DA
    Daniel A. Keim

    University of Konstanz, Germany

    4 publications · 167 citations

    4

    h-index

  2. DS
    David S. Ebert

    Purdue University West Lafayette, United States

    4 publications · 343 citations

    4

    h-index

  3. NA
    Natalia Andrienko

    Fraunhofer Institute for Intelligent Analysis and Information Systems, Germany

    4 publications · 295 citations

    4

    h-index

  4. JF
    Jean‐Daniel Fekete

    Institut national de recherche en sciences et technologies du numérique, France

    4 publications · 135 citations

    4

    h-index

  5. GA
    Gennady Andrienko

    Fraunhofer Institute for Intelligent Analysis and Information Systems, Germany

    4 publications · 295 citations

    4

    h-index

1–5 of 427

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 15 publications · 890 citations

    14

    h-index

  2. Information Visualization

    7 publications · 206 citations

    7

    h-index

  3. Proceedings of the VLDB Endowment

    PVLDB · 4 publications · 261 citations

    4

    h-index

  4. IEEE Computer Graphics and Applications

    4 publications · 211 citations

    4

    h-index

  5. Computer Graphics Forum

    3 publications · 366 citations

    3

    h-index

1–5