Efficient matching for state-persistent publish/subscribe systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Content-based publish/subscribe systems allow information dissemination and fine-grained information filtering in loosely coupled distributed systems. Stateless publish/subscribe systems send notifications to all subscribers whose subscriptions match an incoming publication. State-persistent publish/subscribe systems, a recently proposed model that stores the states of both publications and subscriptions, only send notifications upon state transitions. The information filtering process requires an efficient matching algorithm with high throughput and scalability. Although there have been studies on matching algorithms for stateless publish/subscribe systems, the matching problem for state-persistent publish/subscribe systems is still an open research problem. This paper presents a novel content-based matching algorithm and its data structures for state-persistent publish/subscribe systems. We will also present the complexity analysis and results of simulations that validates the analytical predictions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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