Reverse-engineers a logical model from a database schema
An as-is logical model with declared and inferred structures separated, dead objects and unknowns listed
Can AI do this?
AI does this well Most of the tested AI models master this. 4 of 4 models in the current benchmark master it.
| Model | Tier | Probability of mastery | Status |
|---|---|---|---|
| gemini-4-argon | Flagship | 100% | Mastered |
| gemini-3.5-flash-lite | Low cost | 100% | Mastered |
| gpt-6-luna | Low cost | 100% | Mastered |
| claude-haiku-5-5 | Low cost | 100% | 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: An as-is logical model with declared and inferred structures separated, dead objects and unknowns listed
How it is assessed: From a core-banking account schema recovers entities, keys and relationships, marks each inferred one with its evidence and confidence, leaves dead tables out and asks the data owner about unexplained columns
Related knobits
Builds on
All 7 knobits of Reverse-engineer a model from an existing database.
Source: DAMA-DMBOK2 chapter 5, element(s) DM-28: a performance at CDMP Practitioner level