Distributed Spatial Data Mining

Distributed Spatial Data Mining is a computational paradigm within spatial data mining that leverages distributed systems to process and analyze spatial datasets partitioned across multiple nodes, distinguished by its emphasis on scalability, parallelism, and coordination of spatial analysis tasks in a decentralized computing environment.

14

Publications

332

Citations

40

Authors

17

Institutions

Publications per year

2017–2026

7

Authors

40

  1. TL
    Tingyi Li

    Soochow University, China

    1 publications · 14 citations

    1

    h-index

  2. YX
    Yue Xia

    1 publications · 10 citations

    1

    h-index

  3. WL
    Wei Liang

    University of California, Merced, United States

    1 publications · 14 citations

    1

    h-index

  4. FM
    F. Makhmutova

    Kazan State Technical University named after A. N. Tupolev, Russia

    1 publications · 15 citations

    1

    h-index

  5. NN
    Najmeh Neysani Samany‬

    University of Tehran, Iran

    1 publications · 12 citations

    1

    h-index

1–5 of 40

Venues

  1. ISPRS International Journal of Geo-Information

    IJGI · 1 publications · 12 citations

    1

    h-index

  2. Journal Of Big Data

    1 publications · 31 citations

    1

    h-index

  3. Zenodo (CERN European Organization for Nuclear Research)

    Repository · 1 publications · 28 citations

    1

    h-index

  4. Distributed and Parallel Databases

    1 publications · 20 citations

    1

    h-index

  5. Annals of GIS

    1 publications · 11 citations

    1

    h-index

1–5