Improving estimation accuracy of aggregate queries on data cubes

In this paper, we investigate the problem of estimation of a target database from summary databases derived from a base data cube. We show that such estimates can be derived by choosing a primary database with the desired target measure but not the desired dimensions, and use a proxy database to estimate the results. This technique is common in statistics, but an important issue we are addressing is the accuracy of these estimates. Specifically, given multiple primary and multiple proxy databases, the problem is how to select the primary and proxy databases that will generate the most accurate target database estimation possible. We propose an algorithmic approach which makes use of the principles of information entropy for determining the steps to select or compute the primary and proxy databases that provide the most accurate target database. We show that the primary database with the largest number of cells in common with the target database and the proxy database provides the more accurate estimates. We prove that this is consistent with maximizing the entropy. We provide some experimental results on the accuracy of the target database estimation in order to verify our results. Furthermore, we investigate the accuracy results in cases where the dimensions are defined over a hierarchy of categories and roll-up and drill-down operations are needed to generate the desired target results.