SF-Tree: An Ecient and Flexible Structure for Selectivity Estimation

Estimating the selectivity of a simple path expression (SPE ) is essential for selecting the most ecient evaluation plans for XML queries. To estimate selectivity, we need an ecient and exible structure to store a summary of the path expressions that are present in an XML document collection. In this paper we propose a new structure called SF-Tree to address the selectivity estimation problem. SF-Tree provides a exible way for the users to choose among accuracy, space requirement and selectivity retrieval speed. It makes use of signature les to store the SPEs in a tree form to increase the selectivity retrieval speed and the accuracy of the retrieved selectivity. Our analysis shows that the probability that a selectivity estimation error occurs decreases exponentially with respect to the error size.