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
134
SpatialHadoop: A MapReduce framework for spatial data
Ahmed Eldawy, Mohamed F. Mokbel · 2015 · 543 citations
Mohamed F. Mokbel, Xiaopeing Xiong, Walid G. Aref · 2004 · 437 citations
Partition based spatial-merge join
Jignesh M. Patel, David J. DeWitt · 1996 · 345 citations
Edwin Jacox, Hanan Samet · ACM Transactions on Database Systems · 2007 · 245 citations
1–5 of 134
279
13
h-index
9
h-index
9
h-index
Hong Kong University of Science and Technology, Hong Kong SAR China
9 publications · 452 citations
9
h-index
7
h-index
1–5 of 279
135

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
10 h-index
15 publications
657 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
9 h-index
17 publications
1.1K citations
Aalborg University
Aalborg, Denmark
7 h-index
10 publications
620 citations

University of Toronto
Toronto, Canada
6 h-index
12 publications
638 citations
IBM (United States)
Armonk, United States
6 h-index
14 publications
740 citations

University of Minnesota
Minneapolis, United States
5 h-index
9 publications
1.8K citations
University of Michigan
Ann Arbor, United States
5 h-index
10 publications
592 citations
Philipps University of Marburg
Marburg, Germany
5 h-index
10 publications
571 citations
1–8 of 135
TKDE · 11 publications · 351 citations
11
h-index
9 publications · 662 citations
9
h-index
PVLDB · 7 publications · 714 citations
7
h-index
TODS · 4 publications · 453 citations
4
h-index
IJGI · 3 publications · 70 citations
3
h-index
1–5