Recognises a lossy decomposition
https://knobit.org/id/knobit/K-514
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| Context functions | Result evaluation |
|---|---|
| Knobit creation methods | AI generated, human edited |
| Knobit types | Skill |
| LCG purposes | Assessing the learner's knowledge or skill state |
| LCG purposes | Selecting an assessment task or learning activity |
| action verb | Recognises en |
| applies knowledge | Explains lossless-join decomposition |
| assessment criterion | Flags the deficient case and passes the acceptable one. Deficient: Account Holder split into (Account, Holder Role) and (Party, Holder Role) and joined on Holder Role: two accounts each with one joint holder give four holdings Acceptable: Keep (Account, Party, Holder Role) whole, or split only on attributes that are a key of one side en |
| assessment method | skill-tests task: Loan application logical model (normalisation anomaly skills) en |
| conforms to profile | https://oppekava.edu.ee/a/Knobitite_kirjeldamise_ja_seostamise_reeglid#LCG-KNOBIT-0.1 |
| dcterms:identifier | K-514 |
| dcterms:license | https://creativecommons.org/licenses/by/4.0/ |
| dcterms:source | Review checklist 03-logical, row 14; Heath 1971; E&N 14.1.4, ch. 15 en |
| expected result | The split, the shared attributes that are not a key, and an example spurious row en |
| owl:sameAs | https://bitbucket.org/reffa/ontology/id/knobit/data-modelling/05-logical/14 |
| rdfs:label | Recognises a lossy decomposition en |
| schema:creativeWorkStatus | accepted |
| schema:isPartOf | Data modelling (DAMA-DMBOK2) |
| skos:altLabel | Lossy decomposition (spurious rows) (logical model) en |
| skos:definition | An entity is split so that joining the parts back produces rows that never existed. The split is lossless only if the shared attributes are a key of at least one part. en |
| skos:prefLabel | Recognises a lossy decomposition en |
| target object | lossy decomposition of an entity en |
Used by
| depends on knobit | Recognises a lossy or unconstrained vertical split |
|---|---|
| includes knobit | Derives a normalised logical data model and traces anomalies to their causes |
| requires knobit | Derive the logical data model |