Exploitation du vague spatial dans le SOLAP : vers une approche de conception prenant en compte les risques d'usage
Bibliographic record
Abstract
Spatial OLAP (SOLAP) systems allow multidimensional analysis of huge volume of\nspatial data. Spatial vagueness is a usual spatial data imperfection. Several works propose new models for handling spatial vagueness. However, the implementation of those models in data cubes and their use with SOLAP tools are still in an embryonic state. Thus, we present in this paper a new approach for designing spatial data cubes based on users tolerance to the risks of data cubes misuses. / Les systèmes « Spatial OLAP » (SOLAP) permettent l'analyse multidimensionnelle\nde grands volumes de données spatiales. Le vague spatial est une imperfection courante des données. De nombreux travaux proposent de nouveaux modèles pour gérer le vague spatial. Néanmoins, l'implémentation de ces modèles dans les cubes de données et leur utilisation avec des outils SOLAP sont encore à l'état embryonnaire. Aussi, nous présentons dans cet article une nouvelle approche pour concevoir des cubes de données spatiales prenant en compte la tolérance des utilisateurs aux risques de mauvais usages des cubes.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".