Transcript Logical Data Modelling

Database Systems
Logical Data Modelling
Tutor:
Tel:
E-mail:
Web:
Ian Perry
01723 35 7287
[email protected]
http://itsy.co.uk/units/dbs0204/
Remember the ‘Stack’
Conceptual Overview of things
Logical
Physical
Ian Perry
that are perceived to
be of ‘interest’ in the
‘real’ world.
Data elements & the
relationships between
those elements in a
tabular form.
Actual data held in a
database & the
means to manipulate
that data.
Model of the
Business System.
(ER Model)
Model of Data
Storage Theory
(Db Schema)
Physical
Implementation
(RDBMS)
Slide 2
What is a Logical Data Model?
• A ‘robust’ representation of the initial
decisions made when building our Conceptual
Data Model, which was composed of:
– Entities
– Attributes
– Relationships
• When I say ‘robust’ I mean that this model
MUST ‘perform’ well with respect to a specific
style/type of software.
Ian Perry
Slide 3
Database Theories & Software
• Hardware independent, the match to ‘type’ of
software is our only concern here, e.g.:
– hierarchy
– relations
– objects
Hierarchical DBMS
Relational DBMS
Object-based DBMS
• Each Database Theory addresses:
– Data Structure
– Data Integrity
– Data Manipulation
Ian Perry
Slide 4
Database Theory = Relational Model
• First proposed by Dr. E. F. Codd in June 1970.
– Codd E F, (1970), A Relational Model of Data for Large Shared Data
Banks, Communications of the ACM, Vol. 13, No. 6, Pgs 377 – 387.
• Codd's model is now accepted as the definitive model for
relational database management systems (RDBMS).
• Structured English Query Language ("SEQUEL") was
developed by IBM Corporation, Inc., to use Codd's model.
– SEQUEL later became SQL.
• In 1979, Relational Software, Inc. (now Oracle Corporation)
introduced the first commercial implementation of SQL.
– SQL is the most widely used RDBMS manipulation language.
Ian Perry
Slide 5
Relational Modelling Language
Schema
set of relations
Relation
tablular framework
Tuple
horizontal division of relation (row)
Attribute
vertical division of relation (column)
Data Cell
junction of horizonal & vertical
Cardinality Number of tuples in relation
Degree
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Number of attributes in relation
Slide 6
Relations look very like Entities
• Staff (SCode, Name, Address, DoB, DoE)
SCode Name
9491 Smith
7416 Day
Address
DoB
DoE
6 Shaw St 130265 031098
2 Sale St 140157 031192
• Contract (CCode, Site, Begin, End, Super)
CCode Site
279
Hull
665
York
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Begin
270699
140901
End
030702
020203
Super
9491
7416
Slide 7
Attributes
• Attribute Values are held in Data Cells:
– must be atomic (i.e. simple) values only.
• Set of Attribute Values eligible for entry in a
Data Cell:
– is known as an Attribute’s Domain.
• The Relational Model is weak at explicitly
modelling relationships:
– Relations must share an Attribute Domain if a
relationship is present/possible.
Ian Perry
Slide 8
Data Access
• Data is retrieved by reference to name (of an
Attribute) & value (held in a Data Cell) only.
• Most important Attribute(s) is know as the
‘Key’, of which there are two types.
– Primary Key:
• special Attribute(s) used to control access to Tuples (i.e.
rows) of data.
– Foreign Key:
• used, as logical pointers, instead of lines to represent
associations between Relations.
Ian Perry
Slide 9
Relations are NOT Tables
• Tables are just data items presented in rows &
columns.
– Relations must obey rules (Codd’s 12 Rules).
• For example, we cannot have:
– Duplicates:
• each Tuple (row) must be unique.
– Row or Column dependencies
• there is no hidden meaning from location/sequence of
Tuples (rows) and/or Attributes (columns).
– etc.
Ian Perry
Slide 10
Keys to Data Integrity
• Rule:
– each Tuple (i.e. row) must be unique (for all time).
• Need a way to discriminate between Tuples:
– so, each Relation must have Primary Key:
• There may be many Candidates for the job of
Primary Key, so select on basis of:
– uniqueness AND/OR minimality
• Keys with more than one Attribute:
– are know as composite keys.
Ian Perry
Slide 11
Other things about Attributes
• Attributes that are part of the Primary Key
are known as Prime Attributes.
• Attributes not selected as part of the Primary
Key are known as Secondary Keys.
• Secondary Key Attributes which serve as the
Primary Key in another Relation are known
as Foreign Keys.
Ian Perry
Slide 12
Rules for Integrity
• Entity Integrity:
– No Attribute that is a member of a Primary Key
can assume a ‘null’ value.
• Else, how could we discriminate between Tuples?
• Referential Integrity:
– Foreign Key Attributes must take values that are
either ‘null’, or from same Domain as the Relation
in which Attribute(s) acts as Primary Key.
• Else, we will lose linkages between Relations.
Ian Perry
Slide 13
In Summary
• Logical Data Modelling:
– is all about translating our Conceptual Data Model so that it
might be implemented using software that matches a
specific Database Theory.
• Relational Database Theory, Codd (1970):
– allows us to develop mathematically rigorous abstract data
models, composed of a number of distinct Relations.
• Tables are NOT Relations:
– simply the way we choose to mentally give flesh to our
Logical Data Model,
Ian Perry
Slide 14
And
• Relations?
– defined by a list of Attributes.
– relationships via Primary/Foreign Key mechanism.
• Attributes?
– must be distinctly named.
– ordering is irrelevant.
– data entries:
• should be atomic, of the same type, from the same domain.
• Tuples?
– must be unique (so need a Key).
– ordering is irrelevant.
Ian Perry
Slide 15
So
• Staff (SCode, Name, Address, DoB, DoE)
SCode Name
9491 Smith
7416 Day
Address
DoB
DoE
6 Shaw St 130265 031098
2 Sale St 140157 031192
• Contract (CCode, Site, Begin, End, Super)
CCode Site
279
Hull
665
York
Ian Perry
Begin
270699
140901
End
030702
020203
Super
9491
7416
Slide 16
And Finally!
• I know what you are thinking:
– the Relation Definitions of a Logical Data Model
looks to be exactly the same as the Entity
Definitions of a Conceptual Data Model.
• However:
– it is highly unlikely that your Logical Data Model
will be exactly the same as your Conceptual Data
Model.
• You will have made some logical errors!
Ian Perry
Slide 17