Scalable Spatial Computing

Scalable Spatial Computing is a distributed computing paradigm that enables efficient processing and analysis of large-scale geospatial data by dynamically allocating spatial computation resources across heterogeneous networked systems to maintain performance and responsiveness as data volume and user demand grow.

30

Publications

1.3K

Citations

95

Authors

56

Institutions

Publications per year

2017–2026

9

Authors

95

  1. SP
    Satish Puri

    Georgia State University, United States

    2 publications · 64 citations

    2

    h-index

  2. SB
    Sudipto Banerjee

    University of Minnesota, United States

    2 publications · 80 citations

    2

    h-index

  3. SK
    Sushil K. Prasad

    Georgia State University, United States

    2 publications · 64 citations

    2

    h-index

  4. JB
    Jacob Beal

    RTX (United States), United States

    2 publications · 28 citations

    2

    h-index

  5. SW
    Shaowen Wang

    University of Illinois Urbana-Champaign, United States

    2 publications · 125 citations

    2

    h-index

1–5 of 95

Venues

  1. The Computer Journal

    2 publications · 26 citations

    2

    h-index

  2. 2

    h-index

  3. Analysis and Metaphysics

    1 publications · 32 citations

    1

    h-index

  4. The VLDB Journal

    1 publications · 11 citations

    1

    h-index

  5. GeoInformatica

    1 publications · 161 citations

    1

    h-index

1–5