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Record W3174162781 · doi:10.1145/3461837.3464515

R2GSync and edge views

2021· article· en· W3174162781 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
TopicGraph Theory and Algorithms
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceRelational database management systemJoinssyncGraphRelational databaseSynchronization (alternating current)Enhanced Data Rates for GSM EvolutionTheoretical computer scienceJoin (topology)Set (abstract data type)DatabaseInformation retrievalProgramming languageComputer networkArtificial intelligence

Abstract

fetched live from OpenAlex

Graph databases that are used in enterprises are primarily extracted from a main transactional store that is often an RDBMS. This data infrastructure set up raises the challenge of keeping the extracted graph in a graph database management system (GDBMS) in sync with the source RDBMS. When the extracted graphs contain edge types that are results of join queries, this synchronization requires incrementally maintaining these join queries. In this paper, we investigate an alternative design where we can map the individual relations in these joins to virtual nodes and edges to keep the synchronization very efficient and instead support view-based querying in the GDBMS. We present a system called R2GSync, that synchronizes an RDBMS with a GDBMS and our accompanying edge view design for a GDBMS. We describe our implementation of edge views in GraphflowDB and query optimization techniques for improving the performance of queries that involve edge views.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.728
Threshold uncertainty score0.118

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 science0.0000.000
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 head0.019
GPT teacher head0.237
Teacher spread0.218 · 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

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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