Automatic Index Selection for Inequalities
Sam Arch has extended the existing automatic index selection technique in Soufflé to support indexed inequalities. An Honours thesis can be found here, and the corresponding slides can be found here.
The work investigates two competing automatic index selection techniques designed to accelerate rules with inequality constraints. We present a new auto-index selection strategy that constructs a minimum cluster of B-Tree indexes that cover all searches with at most one inequality - the B-Tree SPS technique. The new technique ensures that rules with inequalities are evaluated efficiently, speeding up the evaluation time of real-world applications by up to 2.32x. The technique is also lightweight, incurring less than a 1% overhead in maximum memory usage and only a 6% overhead in compilation time. Furthermore, the technique is robust, with no practical degradation in performance for real-world applications when compared to the existing auto-index selection scheme.