Selectivity estimation of Window queries for line segment datasets

Despite of the fact that large line segment datasets are becoming more and more popular, most of the analysis for estimating the selectivity of window queries posed on spatial data {the most important parameter for query optimization{ has focused on point or region data only. In this paper we move one signiicant step forward in line segment datasets theoretical analysis. We discovered that real lines closely follow a distribution law, that we named the SLED law (Segment LEngth Distribution). The SLED law can be used for an accurate estimation of the selectivity of window queries. Experiments on a variety of real line segment datasets (hydrographic systems, roadmaps, railroads, utilities networks) show that our law holds and that our formula is extremely accurate, enjoying a maximum relative error of 4% in estimating the selectivity.