Predicate Derivation and Monotonicity Detection in DB2 UDB
Why this work is in the frame
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Bibliographic record
Abstract
DB2 universal database allows database schema designers to specify generated columns. These generated columns are useful for maintaining rollup hierarchy variables in warehouses (e.g., date, month, quarter). In order for the generated columns to be useful for query processing, queries must automatically make use of such columns when applicable. In particular, query predicates on the original columns should be rewritten to make use of the generated columns. In this paper, we describe two main aspects of this predicate rewriting technique that allows usage of the generated columns for a variety of query predicate types. The first aspect, monotonicity detection, allows for rewrites in the case of range predicates. The second aspect, predicate derivation, is the technique for using generating expressions for query processing. We show the value of this technique for providing significant performance improvement when combined with indexing or multidimensional clustering in DB2.
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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 | 0.000 | 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 it