View updates in a semantic data modelling paradigm
Bibliographic record
Abstract
The Sketch Data Model (SkDM) is a new semantic modelling paradigm based on category theory (specifically on categorical universal algebra), which has been used successfully in several consultancies with major Australian companies. This paper describes the sketch data model and investigates the view update problem (VUP) in the sketch data model paradigm. It proposes an approach to the VUP in the SkDM, and presents a range of examples to illustrate the scope of the proposed technique. In common with previously proposed approaches, we define under what circumstances a view update can be propagated to the underlying database. Unlike many previously proposed approaches the definition is succinct and consistent, with no ad hoc exceptions, and the propagatable updates form a broad class. We argue that we avoid ad hoc exceptions by basing the definition of propagatable on the state of the underlying database. The examples demonstrate that under a range of circumstances a view schema can be shown to have propagatable views in all states, and thus state-independence can frequently be recovered. Keywords: View update, category theory, data model, semantic data modelling 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.027 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".