A declustering algorithm for minimising spatial join cost

1997 ◽  
pp. 363-372 ◽  
Author(s):  
Yanchun Zhang ◽  
Jitian Xiao ◽  
Xiaofang Zhou
Keyword(s):  
Spatial Join

Spatial Join with Hadoop

2017 ◽  
pp. 2032-2036 ◽  
Author(s):  
Ahmed Eldawy ◽  
Mohamed F. Mokbel
Keyword(s):  
Spatial Join

scholarly journals A Learned Query Optimizer for Spatial Join

2021 ◽  
Author(s):  
Tin Vu ◽  
Alberto Belussi ◽  
Sara Migliorini ◽  
Ahmed Eldawy
Keyword(s):  
Spatial Join ◽  
Query Optimizer

A Taxonomy for Distance-Based Spatial Join Queries

2017 ◽  
Vol 13 (3) ◽  
pp. 1-24 ◽  
Author(s):  
Lingxiao Li ◽  
David Taniar
Keyword(s):  
Fixed Points ◽  
Spatial Data ◽  
Database Management ◽  
Spatial Databases ◽  
Family Type ◽  
Management Systems ◽  
Data Types ◽  
Full Spectrum ◽  
Spatial Join ◽  
Join Queries

Join operation is one of the most used operations in database management systems, including spatial databases. Hence, spatial join queries are very important in spatial database processing. There are many different kinds of spatial join queries, due to the richness in spatial data types and spatial operations. Therefore, it is important to understand the full spectrum of spatial join queries. The aim of this paper is to give a classification to one family type of spatial join, called the Distance-based Spatial Join. In the taxonomy, the authors divide this spatial join into three categories: (i) AllRange, (ii) All-kNN, and (iii) All-RNN. Each of these categories has its own variants. In this taxonomy, the authors confine the discussions to join queries on fixed points.

Spatial Join

2008 ◽  
pp. 1101-1101
Author(s):  
Shashi Shekhar ◽  
Hui Xiong
Keyword(s):  
Spatial Join

scholarly journals Singularities make spatial join scheduling hard

1997 ◽  
pp. 293-302 ◽  
Author(s):  
Gabriele Neyer ◽  
Peter Widmayer
Keyword(s):  
Spatial Join
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