knobit.org

Recognises an update anomaly in a logical data model

edu:Knobit

https://knobit.org/id/knobit/K-502

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Context functionsResult evaluation
Knobit creation methodsAI generated, AI edited
Knobit creation methodsAI generated, human edited
Knobit typesSkill
LCG purposesAssessing the learner's knowledge or skill state
LCG purposesSelecting an assessment task or learning activity
action verbRecognises en
applies knowledgeExplains the insertion, update and deletion anomalies
assessment criterionFlags the deficient case and passes the acceptable one. Deficient: Account holder address held on every Account; the customer moves and three of their five accounts are updated Acceptable: Address held once on Party; Account reaches it through Account Holder en
assessment methodskill-tests task: Loan application logical model (normalisation anomaly skills) en
conforms to profilehttps://oppekava.edu.ee/a/Knobitite_kirjeldamise_ja_seostamise_reeglid#LCG-KNOBIT-0.1
dcterms:identifierK-502
dcterms:identifierQ-387
dcterms:licensehttps://creativecommons.org/licenses/by/4.0/
dcterms:sourceReview checklist 03-logical, row 2; Codd 1971; E&N 14.1.2; C&B en
dcterms:sourceShared-URI copy of data-modelling K-502 (Review checklist 03-logical (data-modelling), row 2; Codd 1971; E&N 14.1.2; C&B) en
expected resultThe fact stored more than once and the rows a change would have to reach en
owl:sameAshttps://bitbucket.org/reffa/ontology/id/knobit/data-modelling/05-logical/02
rdfs:labelRecognises an update anomaly in a logical data model en
schema:creativeWorkStatusaccepted
schema:isPartOfData modelling (DAMA-DMBOK2)
schema:isPartOfData quality management (DAMA-DMBOK2)
skos:altLabelUpdate anomaly (logical model) en
skos:definitionOne fact is stored in several rows, so a change must be repeated and a partial change leaves the data contradicting itself. en
skos:prefLabelRecognises an update anomaly in a logical data model en
target objectupdate anomaly in a logical data model en

Used by

depends on knobitRecognises an unmanaged duplicate column
includes knobitDerives a normalised logical data model and traces anomalies to their causes
includes knobitDerives data quality rules from the data rules and the physical model
requires knobitDerive the logical data model
requires knobitDerive data quality rules