TECHNIQUES FOR QUERY PROCESSING USING HIGH DIMENSION HISTOGRAMS

摘要

Relational data for a database table having multiple columns (dimensions) is represented as a histogram. The buckets of the histogram are clustered into clusters, the clusters are fewer in number than the buckets of the histogram. Each cluster is represented in hyper-space as a line segment (vector) with a thickness value. Average frequencies for the line segments (vectors) and their thickness values are computed. The average frequencies are processed by a query optimizer when processing a query predicate of a query to use as an estimation of selectivity or an estimation of a cardinality of a join operation.