Flashcards in Exam Deck (42):
when data is stored in different places unnecessarily. E.g when you record names & numbers different places.
Making changes to the way file characteristics are stored without affecting the programs ability to access data. e.g Usually lacking in file systems coz most programs change when data characteristics change.
Database Management System
Eliminate data inconsistency, stop data anormaly, Data Dictionary Management, Data transformation, Security Management etc
When you can change the file structure without affecting the data access.
Result of processing data to reveal meaning
A link between the user & the Database.
+s promotes data sharing, Eliminates problems islands of info, enforces data integrity,stops redundancy,promotes security
Data about data
Support self-doccumentation through metadata,enforcement of data types or domains to ensure consistency in columns, define relationships among tables, or constraints to make consistent on related tables.
Data Modelling is important because
Designers use to communicate with programmers and end users of a database. Main function is understanding complexities of real world environment.
A Business Rule
A short,precise and unambiguous description of a policy, procedure or principal that applies to an organization.
Business Rule purpose
To define entities,attributes, relationships and constraints.
Translating business rules
noun= entity verb= relationship between entities
Describes and association between entities
Three types of relationships
1:M 1 to many, M:N Many to many, 1:1 one to one
A Weak Entity must
1. Be existence dependant
2. The entity must have a PK that is partially or totally from the parent entity.
Strong(or identifying) relationship
PK of related entities contains a PK component of parent entity. Shown bold line ERD
Is in a 1:M relationship with the parent entities and is made up of the pk attributes of the parent entity.
Used to represent/solve a M:N relationship between 2 or more entities.
when a relationship can exist between occurances of the same entity set. eg an employee is a manager of a store.
A generic entity type that is related to one or more entity subtypes. Used to: avoid nulls when one supertype might have different characteristics than another.
Example of 1:M Relationship
One cook can make many burgers and each burger can be made by many cooks.
Example of M:N realtionship
A student can take many classes and each class can have many students.
Example of 1:1 relationshiop
Each store is managed by one employee and each manager manages only one store.
when you can change the internal model without affecting the conceptual model
when you can change the physical model without affecting the internal model
Difference between a database and a table
A database is made up of/holds tables and a table resembles a file conceptually.
Each row (entity) in the table has its own unique identity(PK)
When the FK consists of a value that refers to an existing valid row in another relation.
What subtypes store:
Unique attributes e.g if a supertype is employee subtypes could be cook,waitress bar man etc
Shows the arrangment and relationships between entity supertypes and subtypes.
The attribute in the supertype entity that detirmines which subtype the supertype is related to. E.g Employee type
Contain nonunique subsets of the supertype entity each instance appearing in more than one subtype. e.g tech an employee can also be a student.
Not every supertype occurance is a member of a subtype.
Every supertype occurance must be part of at least one subtype.
a "virtual" entity type used to represent multiple entities and relationships in a erd. Simplifies erd and makes more readable.
A Pk created by designer to simplify figuring out entities. Used when there is no natural suitable PK , when it is composite or too long to use.
Most common design trap. Happens when you have 1 entity in 2 1:M relationships and there is an association that is not expressed in the model. Doesn't show relationship properly.
The process of evaluating and correcting table structures to minimize data redundancy and in turn reducing anomalies.
Table format, no repeating groups, Pk identified, attributes dependent to PK still has partial dependencies.
1NF and with no partial dependencies still might have transitive dependencies.