wavelet-based histograms for selectivity estimation
Query optimization is an integral part of relational database management systems. One important task in query optimization is selectivity estimation, that is, given a query P , we need to estimate the fraction of records in the database that satisfy P . Many commercial database systems maintain histograms to approximate the frequency distribution of values in the attributes of relations. In this paper, we present a technique based upon a multiresolution wavelet decomposition for building histograms on the underlying data distributions, with applications to databases, statistics, and simulation. Histograms built on the cumulative data distributions give very good approximations with limited space usage. We give fast algorithms for constructing histograms and using them in an on-line fashion for selectivity estimation. Our histograms also provide quick approximate answers to OLAP queries when the exact answers are not required. Our method captures the joint distribution of multiple attributes effectively, even when the attributes are correlated. Experiments confirm that our histograms offer substantial improvements in accuracy over random sampling and other previous approaches.
wavelet histogram select estimates query optimization queries queries optimality integrability relational database data database management systems task records database system maintainability approximation distributed values attributes paper multiresolution decomposition applications statistics simulation space algorithm method experience accuracy random sample query