Selectivity Estimation of High Dimensional Window Queries via Clustering

Abstract. Query optimization is an important functionality of modern database systems and often based on estimating the selectivity of queries before actually executing them. Well-known techniques for estimating the result set size of a query are sampling and histogram-based solutions. Sampling-based approaches heavily depend on the size of the drawn sample which causes a trade-off between the quality of the estimation and the time in which the estimation can be executed for large data sets. Histogram-based techniques eliminate this problem but are limited to low-dimensional data sets. They either assume that all attributes are independent which is rarely true for real-world data or else get very inefficient for high-dimensional data. In this paper we present the first multivariate parametric method for estimating the selectivity of window queries for large and high-dimensional data sets. We use clustering to compress the data by generating a precise model of the data using multivari...

• Type: Conference
• Year: 2005
• Where: SSD
• Authors: Christian Böhm, Hans-Peter Kriegel, Peer Kröger, Petra Linhart
• Keywords: Data Sets, Large Data Sets, Low-dimensional Data Sets, SSD 2005