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Record W1723741712

Subject space: a state-persistent model for publish/subscribe systems

2002· article· en· W1723741712 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueConference of the Centre for Advanced Studies on Collaborative Research · 2002
Typearticle
Languageen
FieldComputer Science
TopicPeer-to-Peer Network Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer sciencePublicationSubject (documents)Search engine indexingSemantics (computer science)Flexibility (engineering)State (computer science)Kernel (algebra)Space (punctuation)Information retrievalTheoretical computer scienceWorld Wide WebProgramming language
DOInot available

Abstract

fetched live from OpenAlex

Many conventional models of publish/subscribe systems have limitations. Some models lack the flexibility for expressive subscription semantics, and others exhibit undesirable behaviors under certain situations. This paper introduces the subject space model, a new model for publish/subscribe systems. The major difference between this model and existing ones is that it persists the states of both publications and subscriptions in the system and notifies subscribers only when the states of their subscriptions change from false to true. A novel indexing technique is described for indexing the positions of objects and subscriptions in subject spaces, so that their state information can be retrieved and evaluated very quickly. The performance of the indexing technique is demonstrated in a prototype of a subject space publish/subscribe kernel.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.891
Threshold uncertainty score0.929

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.202
GPT teacher head0.378
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it