Wavelet-based histograms for selectivity estimation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Wavelet synopses with error guarantees
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Approximate query processing using wavelets
The VLDB Journal — The International Journal on Very Large Data Bases
A survey on wavelet applications in data mining
ACM SIGKDD Explorations Newsletter
Probabilistic wavelet synopses
ACM Transactions on Database Systems (TODS)
One-pass wavelet synopses for maximum-error metrics
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Wavelet synopses for general error metrics
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2004
Subquadratic algorithms for workload-aware haar wavelet synopses
FSTTCS '05 Proceedings of the 25th international conference on Foundations of Software Technology and Theoretical Computer Science
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Histogram and Wavelet synopses provide useful tools in query optimization and approximate query answering. Traditional wavelet synopsis construction algorithms treat the construction algorithms as the wavelet coefficients selection problem which is called Coefficient Thresholding. However, all these algorithms just focus on the selection of best wavelet coefficients but deal with the unselected ones naively (just setting them to zero). A key problem is whether it can achieve the optimum of error when the unselected ones are set to one single value: zero. In this paper, we consider a novel Wavelet-based Synopsis construction for the known L2 error measure which can handle the unselected wavelet coefficients effectively. We provide a comprehensive theoretical analysis and demonstrate the effectiveness of these algorithms in providing more optimal error significantly through synthetic data sets.