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Record W2894842211 · doi:10.1145/3239372.3239412

Incremental View Model Synchronization Using Partial Models

2018· article· en· W2894842211 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicModel-Driven Software Engineering Techniques
Canadian institutionsMcGill University
FundersMagyar Tudományos Akadémia Számítástechnikai és Automatizálási Kutatóintézet
KeywordsComputer scienceModel transformationTransformation (genetics)ScalabilitySynchronization (alternating current)Graph rewritingSource modelBenchmark (surveying)Context (archaeology)Set (abstract data type)GraphContext modelTheoretical computer scienceDistributed computingProgramming languageArtificial intelligenceDatabaseConsistency (knowledge bases)

Abstract

fetched live from OpenAlex

View models are abstractions of a set of source models derived by unidirectional model transformations. In this paper, we propose a view model transformation approach which provides a fully compositional transformation language built on an existing graph query language to declaratively compose source and target patterns into transformation rules. Moreover, we provide a reactive, incremental, validating and inconsistency-tolerant transformation engine that reacts to changes of the source model and maintains an intermediate partial model by merging the results of composable view transformations followed by incremental updates of the target view. An initial scalability evaluation of an open source prototype tool built on top of an open source model transformation tool is carried out in the context of the open Train Benchmark framework.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.690
Threshold uncertainty score0.491

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 communication0.0000.001
Open science0.0000.000
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 head0.043
GPT teacher head0.274
Teacher spread0.232 · 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