Scalable Visualization Techniques

Scalable Visualization Techniques is a systematic approach in information visualization that enables the effective rendering, interaction, and interpretation of datasets across varying scales of data size and complexity while maintaining performance and perceptual clarity.

184

Publications

9.2K

Citations

620

Authors

205

Institutions

Publications per year

2017–2026

41

Authors

620

  1. HP
    Hanspeter Pfister

    Harvard University Press, United States

    6 publications · 603 citations

    6

    h-index

  2. LB
    Leilani Battle

    University of Washington, United States

    5 publications · 228 citations

    5

    h-index

  3. SG
    Samuel Gratzl

    Johannes Kepler University of Linz, Austria

    4 publications · 415 citations

    4

    h-index

  4. DW
    Daniel Weiskopf

    University of Stuttgart, Germany

    4 publications · 115 citations

    4

    h-index

  5. HC
    Hank Childs

    Lawrence Berkeley National Laboratory, United States

    4 publications · 170 citations

    4

    h-index

1–5 of 620

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 22 publications · 1.9K citations

    20

    h-index

  2. IEEE Computer Graphics and Applications

    10 publications · 562 citations

    10

    h-index

  3. Computer Graphics Forum

    9 publications · 569 citations

    9

    h-index

  4. Proceedings of the VLDB Endowment

    PVLDB · 4 publications · 123 citations

    4

    h-index

  5. Future Generation Computer Systems

    FGCS · 2 publications · 102 citations

    2

    h-index

1–5