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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.020 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".