Selectivity Estimation of Window Queries

Despite of the fact that large line segment datasets are appearing more and more frequently in numerous applications involving spatial data, such as GIS 8, 9] multimedia 6] and even traditional databases, 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 signi cant 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. On leave from Dipartimento di Matemati...

• Type: Conference
• Year: 1998
• Where: CIKM
• Authors: Guido Proietti, Christos Faloutsos
• Keywords: CIKM 1998, Information Management, Line Segment Datasets, SLED Law, Window Queries