Graph-Based Publish/Subscribe Research with Dynamic Subscriptions
Bibliographic record
Abstract
The publish/subscribe paradigm is well-regarded for its ability to successfully disseminate data while decoupling data providers from data consumers. Traditionally, pub/sub uses two common forms of filtering: topic-based and content-based, with a matching language that is limited to those filtering forms. However, it can be difficult to use these approaches to express interest in a semantic information domain. The current, past, and future research work shown in this paper explores an alternate publication and filtering model based on graphs, where consumers can express their interest in certain publications by subscribing to parts of a graph. This graph-based approach takes advantage of the properties of nodes and semantics of edges to model an application domain. In our research we have developed a graph-based pub/sub middleware prototype and are currently working on extending it to support dynamic graph updates and dynamic subscriptions.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".