Towards service-oriented continuous queries in pervasive systems
Bibliographic record
Abstract
ABSTRACT. Pervasive information systems give an overview of what digital environments should look like in the future. From a data-centric point of view, traditional databases have to be used alongside with non-conventional data sources like data streams, services and events. In this paper, we tackle the definition of continuous queries combining standard relations, data streams and services in a declarative language extending SQL. We first define virtual tables with binding patterns as a way to get a unified view of the pervasive environment. Relations, data streams and services can be homogeneously queried using a SQL-like language, on top of which query optimization can be performed. We also introduce a new clause defining the optimizing criteria to dynamically choose the best way to handle each event. RÉSUMÉ. Les systèmes d’information pervasifs montrent la tendance sur ce que seront les envi-ronnements informatiques de demain. D’un point de vue centré données, les bases de données classiques doivent cohabiter avec des sources de données non-conventionnelles comme les flux de données, les services et les évènements. Dans cet article, nous abordons la définition de requêtes continues combinant les relations classiques, les flux de données et les services dans un langage déclaratif étendant SQL. Nous définissons tout d’abord les tables virtuelles avec
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".