Gpu-accelerated query selectivity estimation based on data clustering and monte carlo integration method developed in cuda environment

Abstract

Query selectivity is a parameter that allows to estimate the size of data satisfying a query condition. For complex range query condition it may be defined as multi integral over a multivariate probability density function (PDF). It describes a multidimensional attribute value distribution and may be estimated using the known approach based on a superposition of Gaussian clusters. But there is the problem of an efficient integration of the multivariate PDF. This may be solved by applying Monte Carlo (MC) method which exposes its advantages for high dimensions. To satisfy the time constraint of selectivity calculation, the parallelized MC integration method was proposed in the paper. The implementation of the method is based on CUDA technology. The paper also describes the application designated for obtaining the time-optimal parameter values of the method.