Sensitivity Analysis of Frequency Counting

Many database optimization activities, such as prefetching, data clustering and partitioning, and buuer allocation, depend on the detection of hot spots in access patterns. While a database designer can in some cases use special knowledge about the data and the users to predict hot spots, in general one must use information about past activity to predict future activity. However, algorithms that make use of hot spots pay little attention to the way in which hot spot information is gathered, or to the quality of this information. In this paper, we present a model for analyzing hot spot estimates based on frequency counting. We present a numerical method for estimating the quality of the data, and a rule-of-thumb. We nd that if b of the references are made to the hottest a of the N data items, then one should process N ad(1 ? a)=(b ? a) 2 e references.