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Record W2115422660 · doi:10.1109/aiccsa.2001.934017

CORBA views: distributing objects with views

2002· article· en· W2115422660 on OpenAlexafffund
Hafedh Mili, Hamid Mcheick, Jamal Ahmad Dargham, Salah Sadou

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicLogic, programming, and type systems
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceCommon Object Request Broker ArchitectureDomain (mathematical analysis)ViewpointsObject (grammar)Embodied cognitionProgramming languageClass (philosophy)Context (archaeology)Human–computer interactionTheoretical computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

We propose a model for building object oriented applications based on the composition of application slices or fragments that provide their own overlapping definitions or expectations of the same domain objects. Different slices may implement different functional or implementation concerns, or embody different access rights and privileges to the same domain objects. We call such slices views and we recognize that the behavior embodied in views may be abstracted into generic class-like algebraic structures called viewpoints, from which views for specific domain classes may be generated. We are interested in the problem of distributing view based applications when different sites access different slices of the same domain objects. Specifically, we are interested in the problem of offering different views of the same domain objects to different client programs in a CORBA-like environment. We first discuss the principles behind view programming, and then explore ways in which objects with views may be distributed in a way that support's different sets of functionalities to different client programs. An interesting application of view programming in a distributed context is the selective duplication of object slices.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.054
GPT teacher head0.250
Teacher spread0.196 · 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 designNot applicable
Domainnot available
GenreEmpirical

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
Published2002
Admission routes2
Has abstractyes

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