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Author Ross, K.A. ♦ Zaman, K.A.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2000
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Computer programming, programs & data
Subject Keyword Aggregates ♦ Databases ♦ Marketing and sales ♦ Medical treatment
Abstract Datacube queries compute aggregates over database relations at a variety of granularities. Often one wants only datacube output tuples whose aggregate value satisfies a certain condition, such as exceeding a given threshold. We develop algorithms for processing a datacube query using the selection condition internally during the computation. Thus, we can safely prune parts of the computation and end up with a more efficient computation of the answer Our first technique, called "specialization", uses the fact that a tuple in the datacube does not meet the given threshold to infer that all finer level aggregates cannot meet the threshold. Our second technique is called "generalization", and applies in the case where the actual value of the aggregate is not needed in the output, but used just to compare with the threshold. We demonstrate the efficiency of these techniques by implementing them within the sparse datacube algorithm of Ross and Srivastava. We present a performance study using synthetic and real-world data sets. Our results indicate substantial performance improvements for queries with selective conditions.
Description Author affiliation: Columbia Univ., USA (Ross, K.A.)
ISBN 0769506860
ISSN 10993371
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2000-07-28
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 350.66 kB
Page Count 14
Starting Page 139
Ending Page 152


Source: IEEE Xplore Digital Library