Recognises attributes inapplicable to some instances

K-516 · Skill knobit · Activity: Derive the logical data model · Competence: Data modelling

Optional attributes that are optional because they do not apply to some instances, not because the value is unknown. A null then means two things, and the attribute cannot be made mandatory where it should be.

Can AI do this?

AI does this well Most of the tested AI models master this. 9 of 10 models master it.

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 attributes, the instances they do not apply to, and the missing subtype

How it is assessed: Flags the deficient case and passes the acceptable one. Deficient: Account with interest rate, maturity date and arrears day count, all required: false because none applies to a current account Acceptable: Loan Account and Current Account as subtypes with a discriminator; Loan Interest Rate required on Loan Account, with a value-set code for "not yet fixed" (LOG-013)

Also known as: Inapplicable attributes (missing subtype) (logical model)

Related knobits

Builds on

• K-566 Explains supertype and subtype constraints

Needed for

• K-606 Recognises rolled-up subtypes losing conditional constraints

Usually followed by

• K-536 Introduces subtypes for attributes applying to only some instances

All 56 knobits of Derive the logical data model.

Source: Review checklist 05-logical, row 16; Codd 1979; E&N 14.1.3