SUPPORTING DISTRIBUTED EVENT-CONDITION-ACTION RULES IN A MULTIDATABASE ENVIRONMENT
Why this work is in the frame
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Bibliographic record
Abstract
We describe a mechanism based on distributed Event-Condition-Action (ECA) rules that supports data coordination in a multidatabase setting. The proposed mechanism includes an ECA rule language and a rule execution engine that transforms rules when they are first posted, and then coordinates their execution. Like traditional ECA rules, our ECA rule language has three parts: an event language, a condition language, and an action language. The event language provides a set of operators with a formal semantics for a multidatabase environment, and which allows a wide variety of composite events. The condition language provides Boolean algebra operators that take as operands either composite or simple conditions. The action language provides a conjunction of simple or composite actions. The execution model partitions rules to more easily manageable forms, distributes them to relevant databases, monitors their execution and composes their evaluations. The mechanism has been designed in a manner that minimizes the number of messages that need to be exchanged over the network. We have also conducted an experimental evaluation to compare the implementation with a naïve centralized execution model. The paper also presents a prototype implementation as well as experimental results on its performance. This work is part of an on-going project intended to develop data coordination techniques for data sharing settings.
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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 | 0.001 | 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 | 0.007 |
| Open science | 0.000 | 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