A formal analysis of why heuristic functions work
Artificial Intelligence
A formal analysis of why heuristic functions work
Artificial Intelligence
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Histogram techniques are used to efficiently estimate query result sizes in most of the modern-day database systems. In a recent work (Oommen and Thiyagarajah, 1999), we introduced a new histogram-like approximation strategy, called the Rectangular Attribute Cardinality Map (R-ACM), which approximates the density function within a given sector by a rectangular cell. In this paper, we introduce another histogram-like approximation strategy, called the Trapezoidal Attribute Cardinality Map (T-ACM) that approximates the density function within a given sector by a trapezoidal cell, where the slope of the trapezoid is obtained so as to fix the actual probability mass within the cell. We present numerous analytic and experimental results concerning the T-ACM demonstrating its superiority over the traditional equi-width and equi-depth histograms for query result size estimation. We hope that with the R-ACM introduced in (Oommen and Thiyagarajah, 1999), the T-ACM could become an invaluable tool for query optimization in the future database systems.