Spatial Join Processing

Spatial Join Processing is a database operation that matches and combines spatially related records from two or more datasets based on their geometric relationships, distinguished by its reliance on spatial predicates such as intersection, containment, or proximity to determine tuple qualification.

134

Publications

8.3K

Citations

279

Authors

135

Institutions

Publications per year

2017–2026

23

Authors

279

  1. NM
    Nikos Mamoulis

    University of Hong Kong, Hong Kong SAR China

    15 publications · 737 citations

    13

    h-index

  2. MF
    Mohamed F. Mokbel

    University of Minnesota, United States

    9 publications · 1.4K citations

    9

    h-index

  3. BS
    Bernhard Seeger

    Philipps University of Marburg, Germany

    9 publications · 1.3K citations

    9

    h-index

  4. DP
    Dimitris Papadias

    Hong Kong University of Science and Technology, Hong Kong SAR China

    9 publications · 452 citations

    9

    h-index

  5. AE
    Ahmed Eldawy

    University of Minnesota, United States

    7 publications · 922 citations

    7

    h-index

1–5 of 279

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 11 publications · 351 citations

    11

    h-index

  2. ACM SIGMOD Record

    9 publications · 662 citations

    9

    h-index

  3. Proceedings of the VLDB Endowment

    PVLDB · 7 publications · 714 citations

    7

    h-index

  4. ACM Transactions on Database Systems

    TODS · 4 publications · 453 citations

    4

    h-index

  5. ISPRS International Journal of Geo-Information

    IJGI · 3 publications · 70 citations

    3

    h-index

1–5