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IT420: Database Management and Organization

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1 IT420: Database Management and Organization
Introduction to ER Model Adina Crăiniceanu

2 Lab Lab 1 due next Tuesday (January 17) before class
You do not have to enforce the rule Landing > Takeoff Send created database to

3 Last Time Why Database Management Systems? Microsoft Access
Transactions High-level abstractions for data access, manipulation, and administration Data integrity and security Performance and scalability Microsoft Access

4 Goals of This Lecture Understand the Entity-Relationship Model

5 Database Design Process
Requirements analysis Conceptual design  data model Logical design Schema refinement: Normalization Physical tuning

6 Conceptual Design Overview
ER Model What are the entities and relationships in the enterprise? What information about these entities and relationships should we store in the database? What are the integrity constraints or business rules that hold?

7 Data Model A data model is a plan, or blueprint, for a database.
General Abstract (no implementation suggested) Easy to change

8 ER Model Entity-Relationship model: set of concepts and graphical symbols Versions Original E-R model — Peter Chen (1976) Extended E-R model — Extensions to the Chen model Information Engineering (IE) — James Martin (1990) “Crow’s foot” notation IDEF1X — A national standard developed by the National Institute of Standards and Technology Unified Modeling Language (UML) — The Object Management Group; it supports object-oriented methodology

9 Entities Something that can be identified and the users want to track
Entity class — a collection of entities of a given type Entity instance — the occurrence of a particular entity There are usually many instances of an entity in an entity class.


11 Attributes Attributes: describe the characteristics of an entity
Entity instances: Same attributes Different values

12 EMPLOYEE: Attributes in Ellipses

13 EMPLOYEE: Attributes in Rectangle

14 Identifiers Identifiers = attributes that identify entity instances
Composite identifiers: Identifiers that consist of two or more attributes Identifiers in data models become keys in database designs: Entities have identifiers. Tables (or relations) have keys.

15 Entity Attributes Display in Data Models

16 Relationships Relationships: associations between entities
Relationship classes: associations among entity classes Relationship instances: associations among entity instances No attributes Relationship degree: Number of entities in the relationship

17 Binary Relationship

18 Ternary Relationship

19 Cardinality Cardinality means “count,” and is expressed as a number
Maximum cardinality: maximum number of entity instances that can participate in a relationship Minimum cardinality: minimum number of entity instances that must participate in a relationship

20 Maximum Cardinality Maximum cardinality: maximum number of entity instances that can participate in a relationship One-to-One [1:1] One-to-Many [1:N] Many-to-Many [N:M]

21 The Three Types of Maximum Cardinality

22 HAS-A Relationships Previous relationships: HAS-A relationships:
Each entity instance has a relationship with another entity instance: An EMPLOYEE has one BADGE A BADGE has an assigned EMPLOYEE.

23 Minimum Cardinality Minimum cardinality: minimum number of entity instances that must participate in a relationship. zero [0]  participation is optional one [1]  participation is mandatory

24 Indicating Minimum Cardinality
Minimum cardinality of zero [0]: oval next to the optional entity. Minimum cardinality of one [1]: vertical hash mark next to the required entity.

25 The Three Types of Minimum Cardinality

26 Data Modeling Notation
(b) Crow’s foot version (ERWin)

27 Data Modeling Notation: ERwin

28 Data Modeling Notation: N:M and O-M
Note that: (1) ERwin cannot indicate true minimum cardinalities on N:M relationships (2) Visio introduces the intersection table instead of using a true N:M model

29 ID-Dependent Entities
ID-dependent entity: entity (child) whose identifier includes the identifier of another entity (parent) Example: BUILDING : APARTMENT PAINTING : PRINT Minimum cardinality from the ID-dependent entity to the parent is always one

30 ID-Dependent Entities
A solid line indicates an identifying relationship

31 Weak Entities A weak entity is an entity whose existence depends upon another entity. All ID-Dependent entities are considered weak. But there are also non-ID-dependent weak entities. The identifier of the parent does not appear in the identifier of the weak child entity.

32 Weak Entities (Continued)
Weak entities must be indicated by an accompanying text box in Erwin – There is no specific notation for a nonidentifying but weak entity relationship A dashed line indicates a nonidentifying relationship

33 ID-Dependent and Weak Entities
ID-Dependent entity: Identifier depends includes another identifier Identifying relationship Ex: BUILDING:APARTMENT Weak entity: existence depends on another entity Ex: MODEL:CAR ID-Dependent  Weak Weak does NOT imply ID-Dependent

34 Subtype Entities Subtype entity: special case of a supertype entity:
STUDENT : UNDERGRADUATE or GRADUATE Supertype: all common attributes [discriminator attribute] Subtypes: specific attributes.

35 Subtypes with a Discriminator

36 Subtypes: Exclusive or Inclusive
If subtypes are exclusive, one supertype relates to at most one subtype. If subtypes are inclusive, one supertype can relate to one or more subtypes.

37 Subtypes: Exclusive or Inclusive

38 Subtypes: IS-A relationships
IS-A relationships: a subtype IS A supertype. Supertype and subtypes identifiers are identical Use subtypes if Have attributes that make sense only for subtypes Want to specify a relationship only for subtype or supertype

39 Class Exercise Draw ER diagram for a database used to manage IT420 class (at least 3 entities) Specify entities, attributes, identifiers Specify relationships Specify cardinalities for relationships

40 ER Modeling: Case Study has offered you a free life-time supply of prescription drugs (no questions asked) if you design its database schema. Given the rising cost of health care, you agree. Here is the information that you gathered: Patients are identified by their SSN, and we also store their names and age Doctors are identified by their SSN, and we also store their names and specialty Each patient has one primary care physician Each doctor has at least one patient

41 For Next Time Read Chapter 5

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