Journal Article10.1145/1206049.1206056
Spatial join techniques
Edwin H. Jacox,Hanan Samet +1 more
TL;DR: The goal of this survey is to describe the algorithms within each component in detail, comparing and contrasting competing methods, thereby enabling further analysis and experimentation with each component and allowing the best algorithms for a particular situation to be built piecemeal, or, even better, enabling an optimizer to choose which algorithms to use.
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Abstract: A variety of techniques for performing a spatial join are reviewed. Instead of just summarizing the literature and presenting each technique in its entirety, distinct components of the different techniques are described and each is decomposed into an overall framework for performing a spatial join. A typical spatial join technique consists of the following components: partitioning the data, performing internal-memory spatial joins on subsets of the data, and checking if the full polygons intersect. Each technique is decomposed into these components and each component addressed in a separate section so as to compare and contrast similar aspects of each technique. The goal of this survey is to describe the algorithms within each component in detail, comparing and contrasting competing methods, thereby enabling further analysis and experimentation with each component and allowing the best algorithms for a particular situation to be built piecemeal, or, even better, enabling an optimizer to choose which algorithms to use.
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Citations
Generalizing prefix filtering to improve set similarity joins
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Speeding up large-scale point-in-polygon test based spatial join on GPUs
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TL;DR: An end-to-end system completely on Graphics Processing Units to associate points with the polygons that they fall within by utilizing massively data parallel computing power of GPUs is designed and developed.
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Tanuja Joshi,Joseph M. Joy,Tobias Kellner,Udayan Khurana,Alaganandam Kumaran,Vibhuti Sengar +5 more
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A parallel spatial data analysis infrastructure for the cloud
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