Conceptual Data Model

1
Conceptual Data Model

• Principles
• Graphical Languages
• Modeling
• Constraints

2
Principles

• Main approach object-oriented
• Class (entity set, object)
• Association (relationship, relation)
• Data member (attribute, property)
• Instance (entity, occurrence)

3
Languages

• Entity Relationship model (E-R) (ERM)
• Entity set
• Relationship
• Attribute

4
Languages

• MERISE
• Object with occurrences
• Relation
• Propertiy

5
Languages

• Object Role Modeling (ORM)

                           
6
Languages

• Class Diagram
• Class with instances
• Association
• Property

7
Conceptual Model

• Goals
• Starting from the dictionary and the rules this
  model tries to reveal the relations among the
  data and their interaction
• Example School

• Rules
• Every class has a one and only one room.
• Every subject is teaches by only one teacher.
• Every class is taught a subject a fixed number of
  hours.
• Every student can have no more one mark in every
  subject.
• The school manages the timetable and the rating
  of students and teachers..

• Dictionary
• Students Address,
• Subject,
• Number of Hours,
• Class Name,
• Student's Family Name,
• Teachers Name,
• Mark,
• Room Number,
• Students Name

8
Concepts

• Class (Entity class, Entity instance)
• Association
• Relationship between entity instances
• Attribute
• Properties

9
Defining an Entity Class

• Give it a name (a noun)
• Define its attributes
• Define the rules
• What belongs to the class?
• How the instances are identified in the class?
• Identifying an instance (Identifier)

10
Association

• Give a name (a verb)
• Determine the participating classes
• Define the cardinalities

11
Examples
12
Identifier of an Association
13
Cardinalities of an Association

• Cardinalities One to One
• 0..1 0..1 Every student can use one locker
• 0..1 1
• 1 1 Every student uses a locker and ther are
  no free lockers

14
Cardinalities of an Association

• Cardinalities One to Many
• 1 1..N
• 0..1 1..N
• 1 0..N
• 0..1 0..N

15
Cardinalities of an Association

• Cardinalities Many to Many
• 1..N 1..N
• 0..N 1..N
• 0..N 0..N

16
Cardinalities of an Association

• Generalization
• Minimal cardinality
• Mandatory participation of every instance - 1
• Optionally participation of every instance - 0
• Maximal cardinality
• To only one instance of the other class 1
• To multiple instances of the other class - N

17
Dimension of an Association

• Number of different classes participating in it
• Multidimensional

18
Dimension of an Association

• Multidimensional

19
Dimension of an Association

• One-dimensional (Reflexive)

20
Aggregate Associations

• Aggregation
• Composition

21
Weak Entities

• It is identified through the association

22
Recommendations

• Dont use high dimension associations
• Be aware not replace classes by associations

23
Case Study Management Rules

• A patient is characterized by
• Unique Number
• Name
• address
• Phone Number
• General practitioner is characterized by
• Serial Number
• Name
• Phone Number
• Each patient is supervised by a GP
• A policlinic is characterized by
• Name
• Address
• Phone Number
• A specialist is characterized by
• Serial Number
• Name
• Phone Number

• Each specialist has one or more specialties
• Each specialist can give consultations in one or
  more policlinics
• Each policlinic groups several specialists
• A patient can make an appointment for a
  consultation with specialist in a given
  policlinic, The specialist must work in this
  policlinic
• The appointment is for a date that is later than
  the date of appointment
• If the consultation does not take place a new
  appointment must be made no matter what are
  reasons for the failure
• Lists of appointment for every specialist are
  made at the beginning of the day.
• In the end of every day two reports are made
• A log of appointment made
• A log of consultations done

24
Case Study - Policlinic
25
Case Study - Policlinic
26
Subtypes

• Example Hardware components order

27
Subtypes