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Record W138003856

Object-relational event middleware for web applications

2011· article· en· W138003856 on OpenAlexaff
Peng Li, Eric Wohlstadter

Bibliographic record

VenueConference of the Centre for Advanced Studies on Collaborative Research · 2011
Typearticle
Languageen
FieldComputer Science
TopicPeer-to-Peer Network Technologies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceAjaxWeb applicationWorld Wide WebMiddleware (distributed applications)JavaConsistency (knowledge bases)Software engineeringDatabaseOperating system
DOInot available

Abstract

fetched live from OpenAlex

Web based applications are increasingly being used for highly reactive systems where clients expect to be notified of broadcast style information with relatively low latency. Software development of these applications has partially been addressed by technologies associated with the Ajax and Comet architecture for Web programming. While such applications are beneficial to end users, they create additional burdens for software developers. In particular, this push-style development is not integrated with the object-oriented model of data used by application-tier developers. In this paper, we investigate an event-driven style of programming to allow event-based subscription and notification of changes to application object state. This requires a new framework to maintain consistency for developers between two key elements. First, consistency must be maintained between application-tier objects and data-tier state. Second, consistency must be maintained between subscriptions across multiple hosts in a server cluster, so that notifications of changes to object state are disseminated to all appropriate browser clients. We use a running example from the Java-based LightPortal open-source social network Web application to describe the approach. We also evaluate performance implications on the RUBiS Web auction application benchmark.

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.

How this classification was reachedexpand

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.214
GPT teacher head0.405
Teacher spread0.191 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations1
Published2011
Admission routes1
Has abstractyes

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