Predicate matching and subscription matching in 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
An important class of publish/subscribe matching algorithms work in two stages. First, predicates are matched and then matching subscriptions are derived. We observe that in practice, the domain types over which predicates are defined are often of fixed enumerable cardinality. Based on this observation we propose a table-based look-up scheme for fast predicate evaluation that finds all matching predicates for each type with one table lookup. We compare this scheme to alternative general-purpose implementations. This observation may also suggests that matching in publish/subscribe systems could equally well be implemented with standard database technology. We propose two DBMS-based matching algorithms and compare the better one with a special purpose publish/subscribe matching algorithm implementation. We provide first evidence that for application scenarios that require large subscription workloads and process many events a DBMS-based solution is not a feasible alternative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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