Practical selectivity estimation through adaptive sampling
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SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Wavelet-based histograms for selectivity estimation
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Data cube approximation and histograms via wavelets
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Dynamic multidimensional histograms
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Compressed histograms with arbitrary bucket layouts for selectivity estimation
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Effective change detection using sampling
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Robust estimation with sampling and approximate pre-aggregation
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A new approach to building histogram for selectivity estimation in query processing optimization
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The VC-dimension of SQL queries and selectivity estimation through sampling
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Estimating aggregate join queries over data streams using discrete cosine transform
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Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Query size estimation is crucial for many database system components. In particular, query optimizers need efficient and accurate query size estimation when deciding among alternative query plans. In this paper we propose a novel sampling technique based on the golden rule of sampling, introduced by von Neumann in 1947, for estimating range queries. The proposed technique randomly samples the frequency domain using the cumulative frequency distribution and yields good estimates without any a priori knowledge of the actual underlying distribution of spatial objects. We show experimentally that the proposed sampling technique gives smaller approximation error than the Min-Skew histogram based and wavelet based approaches for both synthetic and real datasets. Moreover, the proposed technique can be easily extended for higher dimensional datasets.