Reasoning about uniqueness constraints in object relational databases
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Bibliographic record
Abstract
Uniqueness constraints such as keys and functional dependencies in the relational model are a core concept in information systems technology. We consider uniqueness constraints suitable for object relational data models and identify a boundary between tractable and intractable varieties. The subclass that is tractable is still a strict generalization of both keys and relational functional dependencies. We present an efficient decision procedure for the logical implication problem of this subclass. The problem itself is formulated as an implication problem for a simple dialect of description logic (DL). DLs are a family of languages for knowledge representation that have many applications in information systems technology and for which model building procedures have been developed that can decide implication problems for dialects that are very expressive. Our own procedure complements this approach and can be integrated with these earlier procedures. Finally, to motivate our results, we review some applications of our procedure in query optimization.
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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