Un profil UML pour les entrepôts de données intégrant les réseaux spatiaux
Bibliographic record
Abstract
Spatial Data Warehouses (SDW) and Spatial OLAP (SOLAP) systems allow the storage and multidimensional analysis of huge volumes of geographic data. Existing SOLAP models do not take into account connectivity of spatial objects and consequently they do not natively integrate networks. In this paper, we propose a UML (Unified Modeling Language) profile for SDW integrating spatial networks. We implemented our UML profile using UML meta-model elements and OCL (Object Constraint Language) constraints with a Computer-Aided Software Engineering tool (MagicDraw). The implementation of the conceptual model in a database is presented using Oracle. / Les entrepôts de données spatiales (EDS) et les outils OLAP spatial (SOLAP) permettent le stockage et l'analyse multidimensionnelle d'une grande quantité de données géographiques. Les modèles existants SOLAP ne tiennent pas compte de la connectivité des objets spatiaux et par conséquent ils n'intègrent pas les données de type réseaux, comme les réseaux routiers et leurs méthodes d'analyse. Dans cet article, nous proposons un profil UML (Unified Modeling Language) pour les entrepôts de données intégrant des réseaux spatiaux. Nous mettons en ½oeuvre notre profil UML en se basant sur le méta-modèle d'UML et des contraintes OCL (Object Constraint Language) avec un atelier de génie logiciel (MagicDraw). Une implémentation du modèle conceptuel dans une base de données est présentée en utilisant Oracle.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".