Wavelet synopses with error guarantees

Abstract

Recent work has demonstrated the effectiveness of the decomposition in reducing large amounts of data to compact sets of coefficients (termed wavelet synopses) that can be used to provide fast and reasonably accurate approximate answers to queries. A major criticism of such techniques is that unlike, for example, random sampling, conventional synopses do not provide informative error guarantees on the accuracy of individual approximate answers. In fact, as this paper demonstrates, errors can vary widely (without bound) and unpredictably, even for identical queries on nearly-identical values in distinct parts of the data. This lack of error guarantees severely limits the practicality of traditional wavelets as an approximate query-processing tool, because users have no idea of the quality of any particular approximate answer. In this paper, we introduce Probabilistic Wavelet Synopses, the first wavelet-based data reduction technique with guarantees on the accuracy of individual approximate answers. Whereas earlier approaches rely on deterministic thresholding for selecting a set of good coefficients, our technique is based on a novel, probabilistic thresholding scheme that assigns each coefficient a probability of being retained based on its importance to the reconstruction of individual data values, and then flips coins to select the synopsis. We show how our scheme avoids the above pitfalls of deterministic thresholding, providing highly-accurate answers for individual data values in a data vector. We propose several novel optimization algorithms for tuning our probabilistic thresholding scheme to minimize desired error metrics. Experimental results on real-world and synthetic data sets evaluate these algorithms, and demonstrate the effectiveness of our probabilistic synopses in providing fast, highly-accurate answers with error guarantees.