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Abstract: A large number of index structures for high-dimensional data have been proposed previously. In order to tune and compare such index structures, it is vital to have efficient cost prediction techniques for these structures. Previous techniques either assume uniformity of the data or are not applicable to high-dimensional data. We propose the use of sampling to predict the number of accessed index pages during a query execution. Sampling is independent of the dimensionality and preserves clusters ... (Update)
Context of citations to this paper: More
...since either the number of histogram regions becomes too large or these regions contain too much empty space and become inaccurate. In [16], sampling is used to overcome this problem. In contrast to this paper, the sample is used to predict the overall query cost of a given...
...distinct experimental results appear in the diagrams with the shapes from Table 4.1. Furthermore, we use the sampling method proposed in [LS01] for our experiments. In this way, we ran 1000 queries with distinct query points for each measurement reading and computed an average...
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3: tree: An index structure for high-dimensional data (context) - Berchtold, Keim et al. - 1996
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BibTeX entry: (Update)
Christian A. Lang and Ambuj K. Singh. Modeling high-dimensional index structures using sampling. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2001. http://citeseer.ist.psu.edu/lang01modeling.html More
@article{ lang01modeling,
author = "Christian A. Lang and Ambuj K. Singh",
title = "Modeling high-dimensional index structures using sampling",
journal = "SIGMOD Record (ACM Special Interest Group on Management of Data)",
volume = "30",
number = "2",
pages = "389--400",
year = "2001",
url = "citeseer.ist.psu.edu/lang01modeling.html" }
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