Deterministic Wavelet Thresholding for Maximum-Error Metrics

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Deterministic Wavelet Thresholding for Maximum-Error Metrics
Several studies have demonstrated the effectiveness of the wavelet decomposition as a tool for reducing large amounts of data down to compact wavelet synopses that can be used to obtain fast, accurate approximate answers to user queries. While conventional wavelet synopses are based on greedily minimizing the overall root-mean-squared (i.e., L2-norm) error in the data approximation, recent work has demonstrated that such synopses can suffer from important problems, including severe bias and wide variance in the quality of the data reconstruction, and lack of non-trivial guarantees for individual approximate answers. As a result, probabilistic thresholding schemes have been recently proposed as a means of building wavelet synopses that try to probabilistically control other approximation-error metrics, such as the maximum relative error in data-value reconstruction, which is arguably the most important for approximate query answers and meaningful error guarantees. One of the main open ...
Minos N. Garofalakis, Amit Kumar
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2004
Where PODS
Authors Minos N. Garofalakis, Amit Kumar
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