Requêtes arbres régulières pour l'analyse de dépendances entre vues et mises à jour de documents XML
Bibliographic record
Abstract
In this paper we study the classical problem of the impact of an update on a view defined over semi-structured data. We adopt the following working hypotheses: (i) the source document is modeled by an unranked, labeled, ordered tree, (ii) a view V is a tree query whose evaluation on the source document provides a desired partial view of the document, (iii) a class of updates C. is also given by a tree query selecting the nodes to modify. We then study the following problem: given a view query V and a class of updates C., is it possible to detect if the view V is independent of each update q in C.? We show that the problem is in general PSPACE-hard. We propose a sufficient condition evaluable in polynomial time ensuring the independence of a view V with respect to a class of updates C.. We then consider the class of linear view queries for which the problem becomes polynomial. We also show that the tree query model chosen to express V and C., is incomparable with XPath but is able to capture positive queries of CoreXPath.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".