Applying the golden rule of sampling for query estimation

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.