| In Database systems, several important components require accurate estimation of the selectivity of a given query. Query optimization is an important functionality of modern database systems and often based on estimating the selectivity of queries before actually executing them. And the main task of query optimization is to choose an efficient execution plan. In this paper, we develop a nonparametric statistical selectivity estimation approach, which is based upon the empiric distribution estimation by orthogonal series. This novel method is applicable to relations with numerical attributes. It is efficient, accurate, adaptive, and easy to maintain. Real Datasets are used in Experiments and compared with the wavelet-based histogram method. Experimental results show that our approach yields comparable or better estimation accuracy than the Wavelet based Histogram method. |
