Query Size Estimation using Systematic Sampling

In this paper, we propose a new approach to the estimation of query size for select and join operations. The technique, which we have called \systematic sam-pling", is a novel variant of the sampling approach, which sorts the relation before sampling, and which maintains a summary relation to improve run-time performance. We compare the method to a number of existing methods to tackle the problem of query size estimation, and demonstrate, with extensive experimental results, that it performs better than existing approaches over a wide range of data sets.