Probabilistic diagnosis of hot spots

The authors present several techniques to identify, or diagnose, hot spots in a database. All of them are probabilistic in the sense that they will classify the items as hot or cold and exhibit a non-zero probability of false diagnoses. Each technique is analysed to identify the tradeoffs of time and space involved in maintaining a low probability of false diagnosis. Each of the techniques is presented. The analyses of the techniques is considered to determine how likely they are to diagnose without error. The techniques are compared. A numerical comparison based on the analyses is included