Recognises a physical workaround lifted into the logical model
A copy, a report-specific index or a packed flag turned into a logical attribute.
Can AI do this?
People still lead Few or none of the tested AI models showed they master this. 0 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 90% | Not shown |
| gemini-3.5-flash-lite | Low cost | 10% | Not shown |
| gpt-6-luna | Low cost | 9% | Not mastered |
| claude-haiku-5-5 | Low cost | 9% | Not mastered |
Mastered means a probability of at least 90% that the model has this knobit, estimated from its graded answers. “Not shown” means the answers do not settle it either way. How the models are tested.
What it looks like in practice
Expected result: The structure kept physical with its derivation and the business need in the logical model
How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: LA-016 "Payment Handler Workload Index For Reporting" from PE-016 Acceptable: Stays physical with derivation; logical holds the business need
Also known as: Physical workaround lifted to logical
Related knobits
Builds on
- K-914 Explains forward and reverse engineering of data models
- K-920 Explains database design principles
All 7 knobits of Reverse-engineer a model from an existing database.
Source: Review checklist reverse-engineering, row 6 (P&B; DMBOK)