Encoding XQuery using <em>System F</em>
Bibliographic record
Abstract
Since the World Wide Web Consortium (W3C) has recommended XQuery as the standard XML query language, the interest in using existing relational technology to query the XML data has dramatically increased. The most significant challenge of the relational approach is how to fully support XQuery semantics in XQuery-to-SQL translation. To eliminate the implicit semantics of XQuery, an XQuery fragment must be defined with simple syntax and explicit semantics. XQ is proposed as an XQuery fragment to express XML queries. In this thesis, XQ is intensively investigated. It is encoded by System F, a second-order lambda calculus with a considerable expressive power and a strong normalization property. Since XML data is defined as inductive data types, XML tree and XML forest, in System F, all basic XML operators in XQ have been successfully encoded. Also, the semantics of XQ are represented in System F where XQ's semantics environment is encoded by an Environment data type with the corresponding operators. The successful encoding of XQ by System F ensures the termination of XQ query evaluation. Moreover, an extension of XQ by a new tree operator Xtree and a vertical Vfor clause is proposed in this thesis to express some undefinable XQ queries. It is demonstrated that this extension still allows XQ to retain its XQ-to-SQL translation property that ensures the polynomial evaluation time complexity, and its System F encodable property that ensures the termination of query evaluation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".