Bibliographic record
Abstract
We provide a principled extension of SQL, called SchemaSQL , that offers the capability of uniform manipulation of data and schema in relational multidatabase systems. We develop a precise syntax and semantics of SchemaSQL in a manner that extends traditional SQL syntax and semantics, and demonstrate the following. (1) SchemaSQL retains the flavor of SQL while supporting querying of both data and schema. (2) It can be used to transform data in a database in a structure substantially different from original database, in which data and schema may be interchanged. (3) It also permits the creation of views whose schema is dynamically dependent on the contents of the input instance. (4) While aggregation in SQL is restricted to values occurring in one column at a time, SchemaSQL permits "horizontal" aggregation and even aggregation over more general "blocks" of information. (5) SchemaSQL provides a useful facility for interoperability and data/schema manipulation in relational multidatabase systems. We provide many examples to illustrate our claims. We clearly spell out the formal semantics of SchemaSQL that accounts for all these features. We describe an architecture for the implementation of SchemaSQL and develop implementation algorithms based on available database technology that allows for powerful integration of SQL based relational DBMS. We also discuss the applicability of SchemaSQL for handling semantic heterogeneity arising in a multidatabase system.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 0.034 |
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".