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SIGMOD
1999
ACM

Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets

8 years 11 months ago
Approximate Computation of Multidimensional Aggregates of Sparse Data Using Wavelets
Computing multidimensional aggregates in high dimensions is a performance bottleneck for many OLAP applications. Obtaining the exact answer to an aggregation query can be prohibitively expensive in terms of time and or storage space in a data warehouse environment. It is advantageous to have fast, approximate answers to OLAP aggregation queries. In this paper, we present a novel method that provides approximate answers to high-dimensional OLAP aggregation queries in massive sparse data sets in a time-e cient and space-e cient manner. We construct a compact data cube, which is an approximate and space-e cient representation of the underlying multidimensional array, based upon a multiresolution wavelet decomposition. In the on-line phase, each aggregation query can generally be answered using the compact data cube in one I O or a small number of I Os, depending upon the desired accuracy. We present two I O-e cient algorithms to construct the compact data cube for the important case of s...
Jeffrey Scott Vitter, Min Wang
Added 03 Aug 2010
Updated 03 Aug 2010
Type Conference
Year 1999
Where SIGMOD
Authors Jeffrey Scott Vitter, Min Wang
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