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Abstract: This paper surveys the data structures that were proposed for efficient management of spatial data. These data structures were designed having in mind the types of queries asked on spatial data, which are usually proximity-based. It is thus essential to store together, in one sense or another, "nearby" objects. This condition is crucial for performance when the databases are very large---which is almost always the case---and data accesses require lengthy I/O operations. (Update)
Context of citations to this paper: More
.... for proximity search group objects by proximity into hierarchies, and store them in another data structure such as a kd tree or R tree [13]. These conventional data structures do not work well for our purpose. First, kd tree or quad tree, do not support e#cient sequential...
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BibTeX entry: (Update)
O. Procopiuc. Data structures for spatial database systems. http://citeseer.ist.psu.edu/procopiuc97data.html More
@misc{ procopiuc-data,
author = "O. Procopiuc",
title = "Data structures for spatial database systems",
text = "O. Procopiuc. Data structures for spatial database systems.",
url = "citeseer.ist.psu.edu/procopiuc97data.html" }
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