A Comparative Study of Spatial Indexing Techniques for Multidimensional Scientific Datasets
A Comparative Study of Spatial Indexing Techniques for Multidimensional Scientific Datasets
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Date
2004-01-29
Authors
Nam, Beomseok
Sussman, Alan
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Abstract
Scientific applications that query into very large multidimensional
datasets are becoming more common. These datasets are growing in size every
day, and are becoming truly enormous, making it infeasible to index
individual data elements. We have instead been experimenting with {\em
chunking} the datasets to index them, grouping data elements into small
chunks of a fixed, but dataset-specific, size to take advantage of spatial
locality. While spatial indexing structures based on R-trees perform
reasonably well for the rectangular bounding boxes of such chunked
datasets, other indexing structures based on KDB-trees, such as Hybrid
trees, have been shown to perform very well for point data. In this paper, we investigate how all these indexing structures perform for multidimensional
scientific datasets, and compare their features and performance with that
of {\bf SH-trees}, an extension of Hybrid trees, for indexing
multidimensional rectangles. Our experimental results show that the
algorithms for building and searching SH-trees outperform those for
R-trees, R*-trees, and X-trees for both real application and synthetic
datasets and queries. We show that the SH-tree algorithms perform well for
both low and high dimensional data, and that they scale well to high
dimensions both for building and searching the trees.
(UMIACS-TR-2004-03)