A Multi-Agent-Based Approach for Autonomic Data Exchange Processes.
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we present a prototype for our solution called Data Exchange Autonomic Manager (DEAM) [1] which has as main goal to turn Data Exchange processes into selfmanaged systems. We believe that providing data exchange processes with self-healing autonomic capability is a promising approach toward reliable self-managed and resilient data exchange processes. We describe the high level architecture of DEAM prototype which leverages well established techniques and technologies for Autonomic Computing (Multi-Agent Systems) and Schema Matching (Automatic Schema Matching and Mapping). Keywords-data exchange; autonomic computing; self-managed systems; dependability; fault tolerance; sufficient correctness; schema matching; schema mapping; mapping adaptation; multiagent systems; agent based modelling and simulation
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it