Efficiently constructing regression models for selectivity estimation

Abstract

A model generator constructs a model for estimating selectivity of database operations by determining a number of training examples necessary for the model to achieve a target accuracy and by generating approximate selectivity labels for the training examples. The model generator may train the model on an initial number of training examples using cross-validation. The model generator may determine whether the model satisfies the target accuracy and iteratively and geometrically increase the number of training examples based on an optimized geometric step size (which may minimize model construction time) until the model achieves the target accuracy based on a defined confidence level. The model generator may generate labels using a subset of tuples from an intermediate query expression. The model generator may iteratively increase a size of the subset of tuples used until a relative error of the generated labels is below a target threshold.