An information-theoretic approach to normal forms for relational and XML data
Bibliographic record
Abstract
Normalization as a way of producing good relational database designs is a well-understood topic. However, the same problem of distinguishing well-designed databases from poorly designed ones arises in other data models, in particular, XML. While, in the relational world, the criteria for being well designed are usually very intuitive and clear to state, they become more obscure when one moves to more complex data models.Our goal is to provide a set of tools for testing when a condition on a database design, specified by a normal form , corresponds to a good design. We use techniques of information theory, and define a measure of information content of elements in a database with respect to a set of constraints. We first test this measure in the relational context, providing information-theoretic justification for familiar normal forms such as BCNF, 4NF, PJ/NF, 5NFR, DK/NF. We then show that the same measure applies in the XML context, which gives us a characterization of a recently introduced XML normal form called XNF. Finally, we look at information-theoretic criteria for justifying normalization algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.011 | 0.027 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".