A new histogram method for sparse attributes: the averaged rectangular attribute cardinality map
Bibliographic record
Abstract
Most current Database Management Systems (DBMS) use histograms in their query optimization, and in approximating query result sizes. This is because they can be utilized in determining efficient query evaluation plans. All the existing methods perform poorly when the attributes of a relation are very sparsely distributed, also called the data These cases are the worst-cases scenarios for attributes with skewed distributions. In this paper, we propose a novel histogram-based algorithm, namely the Averaged Rectangular Attribute Cardinality Map (Averaged R-ACM), and demonstrate its performance in estimating query result sizes for the sparse data cases. Our proposed algorithm combines the advantages of the traditional widely-used histogram-based algorithm, namely the Equi-width histogram, and a relatively new algorithm, namely the R-ACM2 introduced in [Thi99]. The goals of compacting the sparse data distribution and of obtaining accurate estimates of query result sizes are achieved by utilizing this algorithm. The superiority of this algorithm is also validated by an extensive set of experiments. And the entire set of experimental results obtained by integrating the underlying algorithm and other histogram-based algorithms into the ORACLE query optimizer can be found in [Che03].
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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 | 0.001 | 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 | 0.003 | 0.001 |
| 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".