Query result size estimation using the Trapezoidal Attribute Cardinality Map
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".