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Efficiently supporting ad hoc queries in large datasets of time sequences

Efficiently supporting ad hoc queries in large datasets of time sequences Efficiently Supporting Ad Hoc Queries Sequences H. V. Jagaciish in Large Datasets of Time Flip Kern  Christos Falotdsos~ Dept. of Computer Park, Science AT&T Florham Laboratories Park, NJ 07932 att. com Dept. Inst. of Computer for Systems Park, University Science Research MD 20742 umd. edu and University College flip@cs of Maryland MD 20742 .umd. edu j ag@research. of Maryland College christos@cs. Abstract introduce a way to do this, for numerical (time sequence) data, at the cost of a small loss in numerical accuracy. When the data.set is very large, accessing specific data values is a difficult problem. For inst ante, if the data is on tape, such access is next to impossible. When the data is all on disk, the cost of disk storage, even with today ™s falling disk prices, is typically a major concern, and anything one can do to decrease the amount of disk storage required is of value. We, the authors, ourselves have experience with more than one dataset that ran into hundreds of gigabytes, making storage of the data on disk prohibitively expensive. Unfortunately, most data compression techniques require large blocks of data to be effective, so that random access to arbitrary http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Efficiently supporting ad hoc queries in large datasets of time sequences

Association for Computing Machinery — Jun 1, 1997

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References (31)

Datasource
Association for Computing Machinery
Copyright
Copyright © 1997 by ACM Inc.
ISBN
0-89791-911-4
doi
10.1145/253260.253332
Publisher site
See Article on Publisher Site

Abstract

Efficiently Supporting Ad Hoc Queries Sequences H. V. Jagaciish in Large Datasets of Time Flip Kern  Christos Falotdsos~ Dept. of Computer Park, Science AT&T Florham Laboratories Park, NJ 07932 att. com Dept. Inst. of Computer for Systems Park, University Science Research MD 20742 umd. edu and University College flip@cs of Maryland MD 20742 .umd. edu j ag@research. of Maryland College christos@cs. Abstract introduce a way to do this, for numerical (time sequence) data, at the cost of a small loss in numerical accuracy. When the data.set is very large, accessing specific data values is a difficult problem. For inst ante, if the data is on tape, such access is next to impossible. When the data is all on disk, the cost of disk storage, even with today ™s falling disk prices, is typically a major concern, and anything one can do to decrease the amount of disk storage required is of value. We, the authors, ourselves have experience with more than one dataset that ran into hundreds of gigabytes, making storage of the data on disk prohibitively expensive. Unfortunately, most data compression techniques require large blocks of data to be effective, so that random access to arbitrary

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