MétaCan
Menu
Back to cohort
Record W3210155192 · doi:10.1145/3459637.3482087

Discovery of Temporal Graph Functional Dependencies

2021· article· en· W3210155192 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
FieldDecision Sciences
TopicData Quality and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFunctional dependencyComputer scienceDependency graphDependency (UML)USableSatisfiabilityPGraphDependency theory (database theory)Theoretical computer scienceBounded functionBoolean satisfiability problemData miningTime complexityAlgorithmArtificial intelligenceMathematicsRelational database

Abstract

fetched live from OpenAlex

Temporal Graph Functional Dependencies (TGFDs) are a class of data quality rules imposing topological, attribute dependency constraints over a period of time. To make TGFDs usable in practice, we study the TGFD discovery problem, and show the satisfiability, implication, and validation problems for k-bounded TGFDs are in PTIME. We introduce the TGFDMiner algorithm, which discovers minimal, frequent TGFDs. Our evaluation shows the efficiency and effectiveness of TGFDMiner, and the utility of TGFDs.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.705
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0020.000

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 head0.207
GPT teacher head0.381
Teacher spread0.174 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicData Quality and ManagementFrench-language works237,207