Location-based matching in publish/subscribe revisited
Bibliographic record
Abstract
Event processing is gaining rising interest in industry and in academia. The common application pattern is that event processing agents publish events while other agents subscribe to events of interest. Extensive research has been devoted to developing efficient and scalable algorithms to match events with subscribers' interests. The predominant abstraction used in this context is the content-based publish/subscribe (pub/sub) paradigm for modeling an event processing application. Applications that have been referenced in this space include emerging applications in co-spaces that rely on location-based information [1, 7], algorithmic trading and (financial) data dissemination [13], and intrusion detection system [4]. In this work, we focus primarily on the role of state-of-the-art matching algorithms in location-based pub/sub applications.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".