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A Qualitative Comparison Study of Data Structures for Large Line Segment Databases*
BibTeX
@MISC{Hoely_aqualitative,
author = {Erik G. Hoely and Hanan Samet},
title = {A Qualitative Comparison Study of Data Structures for Large Line Segment Databases*},
year = {}
}
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Abstract
A qualitative comparative study is performed of the performance of three popular spatial indexing methods { the r-tree, r +-tree, and the pmr quadtree { in the context of processing spatial queries in large line segment databases. The data is drawn from the tiger/Line les used by the Bureau of the Census to deal with the road networks in the US. The goal is not to nd the best data structure as this is not generally possible. Instead, their comparability is demonstrated and an indication is given as to when and why their performance di ers. Tests are conducted with a number of large datasets and performance is tabulated in terms of the complexity of the disk activity in building them, their storage requirements, and the complexity of the disk activity foranumber of tasks that include point and window queries, as well as nding the nearest line segment toa given point and an enclosing polygon. 1Introduction Spatial data consists of points, lines, regions, rectangles, surfaces, volumes, and even data of higher dimension which includes time (e.g., [19,20]). The conventional approach to dealing with spatial data is to store it explicitly by parametrizing it and thereby obtaining a reduction to a point in a possibly higher dimensional space. This is usually quite easy to do in a conventional database management system since it is just a collection of records, where each record has many elds. In particular, we simply add a eld (or several elds) to the record that deals with the desired item of spatial information. This approach is ne if we just want to perform a simple retrieval of the data. However, if our query involves the space occupied by the data (and hence other records by virtue of their proximity), then the situation is not so straightforward. In such a case we need to be able to retrieve records
Keyphrases
data structure large line segment database qualitative comparison study conventional database management system dimensional space simple retrieval conventional approach window query spatial data consists popular spatial indexing method spatial data desired item disk activity enclosing polygon pmr quadtree line segment toa large datasets many eld performance di er road network storage requirement several eld spatial query qualitative comparative study spatial information tiger line le disk activity foranumber
