Randomized Geometric Data Structures

Randomized Geometric Data Structures is a systematic framework for organizing spatial data that incorporates randomness in its construction or query procedures to achieve efficient expected performance and robustness in geometric computations.

11

Publications

526

Citations

15

Authors

10

Institutions

Publications per year

2017–2026

1

Authors

15

  1. TM
    Timothy M. Chan

    University of Waterloo, Canada

    2 publications · 99 citations

    2

    h-index

  2. PK
    Pankaj K. Agarwal

    Duke University, United States

    2 publications · 70 citations

    2

    h-index

  3. JP
    Jiangwei Pan

    Duke University, United States

    2 publications · 70 citations

    2

    h-index

  4. KK
    Kenichi Kanatani

    Gunma University, Japan

    1 publications · 30 citations

    1

    h-index

  5. KM
    K. Mulmuley

    University of Chicago, United States

    1 publications · 27 citations

    1

    h-index

1–5 of 15

Venues

  1. 1

    h-index

  2. Discrete Applied Mathematics

    1 publications · 10 citations

    1

    h-index

  3. Algorithmica

    1 publications · 27 citations

    1

    h-index

  4. Theory and Application of Cryptographic Techniques

    Conference · 1 publications · 21 citations

    1

    h-index

  5. Canadian Conference on Computational Geometry

    Conference · 1 publications · 12 citations

    1

    h-index

1–5