Concepedia

Multi-attribute Uncertainty Visualization

Multi-Attribute Uncertainty Visualization is a visualization approach that represents uncertainty in data with multiple interdependent attributes by simultaneously encoding probabilistic or stochastic variations across several dimensions, distinguishing it from univariate or single-attribute uncertainty visualization methods.

2

Publications

45

Citations

7

Authors

3

Institutions

Publications per year

2017–2026

0

Parents

Publications

2

1–2 of 2

Authors

7

  1. AP
    Alex Pang

    University of California, Santa Cruz, United States

    1 publications · 17 citations

    1

    h-index

  2. CB
    Christina Bloebaum

    University at Buffalo, State University of New York, United States

    1 publications · 28 citations

    1

    h-index

  3. AN
    A.M. Naim

    University at Buffalo, State University of New York, United States

    1 publications · 28 citations

    1

    h-index

  4. PC
    Po-Wen Chiu

    University at Buffalo, State University of New York, United States

    1 publications · 28 citations

    1

    h-index

  5. LS
    Lisa Singh

    Georgetown University, United States

    1 publications · 17 citations

    1

    h-index

1–5 of 7

Institutions

3

  1. Georgetown University

    Washington, United States

    1 h-index

    1 publications

    17 citations

  2. University at Buffalo, State University of New York

    Buffalo, United States

    1 h-index

    4 publications

    112 citations

  3. University of California, Santa Cruz

    Santa Cruz, United States

    1 h-index

    2 publications

    34 citations

1–3 of 3

Venues

  1. International Journal of Product Development

    IJPD · 1 publications · 28 citations

    1

    h-index

  2. 1

    h-index

1–2