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A model of data distribution based on texture analysis

A model of data distribution based on texture analysis A Model of Data Distribution Based on Texture Analysis Nab11 Kamel Roger King Department of Computer Science Umverslty of Colorado Boulder Colorado, 80309 Abstract To eatlmate the number of tuplea aatlsfyrng a certain query, The model 1s based a data dlstrlbutlon model IS proposed on a discrete approxlmat?on of the data space and belongs to the class of nonparametrlc models Using texture analysis techmquea apphed to the multl dImensIonal data apace, It 1s proposed that a segmentation of thla apace be obtaIned as a means of obtalnlng a discrete approxlmatlon Thus the Into a number of homogeneous regions space IS divided which can be later queried to obtain good estlmatea of the a new size of the response set To obtain this segmentation, of a bit pattern IS profunction to assess the homogeneity posed Test results performrd for thla function are presented to show the Inverse correlation between Its value and the resulting estimation errors models (e g network a dlatrlbutlon tldrmenslonal and hlerarchlcal) [MERR79] allows presents a muland the does not which proTheir of a model for relations between model which approximates requirements however, characterlstlea the sectors [HAT841 bit map In a way which the storage The http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A model of data distribution based on texture analysis

Association for Computing Machinery — May 1, 1985

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Datasource
Association for Computing Machinery
Copyright
Copyright © 1985 by ACM Inc.
ISBN
0-89791-160-1
doi
10.1145/318898.318929
Publisher site
See Article on Publisher Site

Abstract

A Model of Data Distribution Based on Texture Analysis Nab11 Kamel Roger King Department of Computer Science Umverslty of Colorado Boulder Colorado, 80309 Abstract To eatlmate the number of tuplea aatlsfyrng a certain query, The model 1s based a data dlstrlbutlon model IS proposed on a discrete approxlmat?on of the data space and belongs to the class of nonparametrlc models Using texture analysis techmquea apphed to the multl dImensIonal data apace, It 1s proposed that a segmentation of thla apace be obtaIned as a means of obtalnlng a discrete approxlmatlon Thus the Into a number of homogeneous regions space IS divided which can be later queried to obtain good estlmatea of the a new size of the response set To obtain this segmentation, of a bit pattern IS profunction to assess the homogeneity posed Test results performrd for thla function are presented to show the Inverse correlation between Its value and the resulting estimation errors models (e g network a dlatrlbutlon tldrmenslonal and hlerarchlcal) [MERR79] allows presents a muland the does not which proTheir of a model for relations between model which approximates requirements however, characterlstlea the sectors [HAT841 bit map In a way which the storage The

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