1. Web-Enabled Decision Support Systems
Relational Data Modeling and Normalization
Prof. Name
Position (123)
University Name

2. Overview 4.1 Introduction 4.2 The Relational Data Model
4.3 Relational Keys
4.4 Relational Data Integrity Constraints
4.5 Transforming E-R Diagrams into Relational Schemas
4.6 Case Study: Logical Design for a University Database
4.7 Introduction to Normalization
4.8 Data Redundancy
4.9 Database Anomalies
4.10 Functional Dependencies
4.11 Forms of Normalization
4.12 In-Class Assignment
4.13 Summary

3. Introduction
In the previous chapter, we described conceptual database design using object-based entity-relationship (E-R) data modeling
The objective of a logical database design is to transform the conceptual data model into a set of relations used for physical database design
We describe logical database design using record-based relational data modeling
Widely used in contemporary database applications
General modeling approach

4. Overview 4.1 Introduction 4.2 The Relational Data Model
4.3 Relational Keys
4.4 Relational Data Integrity Constraints
4.5 Transforming E-R Diagrams into Relational Schemas
4.6 Case Study: Logical Design for a University Database
4.7 Introduction to Normalization
4.8 Data Redundancy
4.9 Database Anomalies
4.10 Functional Dependencies
4.11 Forms of Normalization
4.12 In-Class Assignment
4.13 Summary

5. Relational Data Model First introduced by IBM’s E.F. Codd in the 1970s
Based on mathematical concept of a relation
Physically represented as a relation or a table that stores data
Consists of three components:
Relational data structure
Where data is organized
Data manipulation
Operations used to manipulate data stored in the data structure
Relational data integrity
Rules that maintain the integrity of data when manipulated

6. Relational Data Structure
A relation is the main data structure that stores and organizes data in the relational data model
Two-dimensional grid that holds data about the object in the database
E-R Diagram and
Relational Schema
for the Student Relation

7. Relational Data Structure (cont.)
A column of a relation is referred to as an attribute
The number of attributes in a relation is called the degree of the relation
A row is referred to as a record or a tuple
The number of records in a relation is defined as the cardinality of the relation
Relational schema example:
STUDENT (SSN, Name, , DeptName)

8. Properties of a Relation
Each relation is uniquely identified by its name
Each cell of a relation contains exactly one (atomic) value
Each record of a relation is unique
Each attribute in a relation has a distinct name
The values of an attribute are from the same domain
The order of attributes is irrelevant
The order of records is also irrelevant

9. Data Manipulation
A way to access and manipulate the data in the relations
Done using a data manipulation language:
Structured Query Language (SQL)
SQL example:

10. Overview 4.1 Introduction 4.2 The Relational Data Model
4.3 Relational Keys
4.4 Relational Data Integrity Constraints
4.5 Transforming E-R Diagrams into Relational Schemas
4.6 Case Study: Logical Design for a University Database
4.7 Introduction to Normalization
4.8 Data Redundancy
4.9 Database Anomalies
4.10 Functional Dependencies
4.11 Forms of Normalization
4.12 In-Class Assignment
4.13 Summary

11. Relational Keys
We need to specify one or more attributes (relational keys) that uniquely identify each record in a relation
A super key is a set of one or more attributes that uniquely identifies each record in a relation
A candidate key is a minimal super key (one that has a minimum number of attributes)

12. Primary and Composite Keys
A primary key is a candidate key that has been selected to uniquely identify records in a relation
Selection of primary key:
It must be unique within its domain at all times
The candidate key can never change
It cannot hold a NULL value
A composite key is a key that has more than one attribute

13. Foreign Key
A foreign key is an attribute or a set of attributes in a relation that serves as a primary key of the same or some other relation
Foreign Key
Primary Key
Student and Department Relations Illustrating Foreign Keys (DeptName)

14. About NULL
Situations where attribute cannot be assigned with a data value
There is no applicable data value
When the value is unknown
Assign NULL value in such situations
NULL means that the value is either unknown or is not applicable
NULL is not same as zero or white space

15. Overview 4.1 Introduction 4.2 The Relational Data Model
4.3 Relational Keys
4.4 Relational Data Integrity Constraints
4.5 Transforming E-R Diagrams into Relational Schemas
4.6 Case Study: Logical Design for a University Database
4.7 Introduction to Normalization
4.8 Data Redundancy
4.9 Database Anomalies
4.10 Functional Dependencies
4.11 Forms of Normalization
4.12 In-Class Assignment
4.13 Summary

16. Integrity Constraints
There are three types of data integrity constraints:
Domain constraints
Entity constraints
Referential constraints

17. Domain Constraints
A domain is the set of values that can be assigned to an attribute
The domain constraint states that all the values of an attribute must be from the same domain
The Domain Definition

18. Entity Constraints
Entity constraints ensure that every relation of a relational data model has a primary key and that the value of the primary key cannot be NULL
Proof:
Primary key:
Minimal set of attributes that uniquely identify tuples
Say primary key can have nulls:
We don’t need all attributes of a primary key to identify the tuples uniquely
CONTRADICTION!

19. Referential Constraints
A referential integrity constraint ensures that the foreign key values of a relation must come from the primary key values of the related relation; otherwise, the value of a foreign key must be NULL
Violation of a
Referential Integrity
Constraint

20. Referential Constraints (cont.)
Representation:
Add an arrow starting from foreign key attribute(s) pointing to the associated primary key attribute(s)
Graphical Representation of a Relational Schema

21. Overview 4.1 Introduction 4.2 The Relational Data Model
4.3 Relational Keys
4.4 Relational Data Integrity Constraints
4.5 Transforming E-R Diagrams into Relational Schemas
4.6 Case Study: Logical Design for a University Database
4.7 Introduction to Normalization
4.8 Data Redundancy
4.9 Database Anomalies
4.10 Functional Dependencies
4.11 Forms of Normalization
4.12 In-Class Assignment
4.13 Summary

22. Task 1: Transforming Regular Entities
Transform a regular entity type in an E-R diagram into a relation.
Assign the entity name in an E-R diagram as a relation name.
Make each simple attribute of a regular entity an attribute of a relation.
Make the identifier of the regular entity type the primary key of a relation.
Transformation of
a Regular Entity

23. Task 2: Transforming Composite Attributes
Include simple attributes of a composite attribute in the relation.
Transformation of a Composite Attribute

24. Task 3: Transforming Multi-Valued Attributes
Transform a regular entity as described; however, do not add any multi-valued attributes to the relation.
Create a new relation (one for each multi-valued attribute).
The new relation should have two attributes:
Identifier of a regular entity
Multi-valued attribute
The name of the new relation should be a logical name that reflects the meaning of a multi-valued attribute.
The primary key of a new relation is a composite key.
The two attributes of a new relation together serve as its primary key.

25. Task 3: Transforming Multi-Valued Attributes
Transformation of Multi-Valued Attribute

26. Task 4: One-to-Many Unary Relationships
Transform an entity of a unary relationship as a regular entity as described previously.
Add a new attribute, primary key of the same relation as a foreign key.
Draw an arrow that originates from the foreign key and points towards the primary key.
Student
Relation
After
Transformation

27. Task 4: One-to-Many Unary Relationships
Transformation of a Unary One-to-Many Relationship

28. Task 4: Many-to-Many Unary Relationships
Transform the entity of a unary relationship as a regular entity.
Create and name a new relation to represent the many-to-many relationship.
The new relation gets the primary key of the entity and a second attribute representing the many-to-many relationship.
Primary key of the new relation is a composite key of the two foreign keys.
Draw referential integrity arrows for the foreign keys.
Add any attributes of the many-to-many relationship in the new relation.

29. Task 4: Many-to-Many Unary Relationships
Transformation of a Unary Many-to-Many Relationship

30. Task 4: Many-to-Many Unary Relationships
Relation Item and
the New Relation
Component After
Transformation

31. Task 5: One-to-One Binary Relationships
Create two relations, one for each entity.
Transform each entity into a relation as a regular entity.
Include the primary key of one relation as a foreign key to the other relation.
Mandatory side migrates towards the optional side.
Show the referential integrity constraint.
Any attributes on the relationship along with the foreign key migrate toward the optional side of the relationship.

32. Task 5: One-to-One Binary Relationships
Transformation of a Binary One-to-One Relationship

33. Task 5: One-to-One Binary Relationships
Employee and Workstation Relations After Transformation

34. Task 5: One-to-Many Binary Relationships
Create two relations, one for each entity.
Transform each entity into a relation as a regular entity.
The primary key of a relation on the “one” side of the relationship migrates towards the relation on the “many” side of the relationship.
Show referential integrity constraints.
Any attributes on the relationship along with the foreign key migrate toward the relation on the “many” side of the relationship.

35. Task 5: One-to-Many Binary Relationships
Transformation of a Binary One-to-Many Relationship

36. Task 5: One-to-Many Binary Relationships
Student and Department Relations After Transformation

37. Task 5: Many-to-Many Binary Relationships
Create two relations, one for each entity.
Transform each entity into a relation as a regular entity.
Create a third new relation to represent the many-to-many relationship.
Primary key from both the relations migrates to the new relation.
Show referential integrity constraints.
Primary key of the new relation is a composite key with foreign keys of relations.
Any attributes of the relationship migrate toward the intermediate relation.

38. Task 5: Many-to-Many Binary Relationships
Transformation of Binary Many-to-Many Telationship

39. Task 5: Many-to-Many Binary Relationships
Transformed
Binary
Many-to-Many
Relationship

40. Task 6: Transforming Ternary Relationships
Create three relations, one for each entity in a ternary relationship.
Transform each entity into a relation as a regular relation.
Create a fourth relation that represents the ternary relationship.
Primary key from the first three relations migrates to the new relations.
Show referential integrity constraints.
The three foreign keys of the new relation together serve as a composite primary key.
Any attributes of the relationship migrate toward the intermediate relation.

41. Task 6: Transforming Ternary Relationships
Transformation
of a Ternary
Relationship

42. Task 6: Transforming Ternary Relationships
Transformed Ternary Relationship

43. Task 7: Transforming Superclass/Subclass Relationships
Create a separate relation for the superclass and each subclass entity.
Transform each entity into a relation as a regular entity.
All common attributes of superclass entity are assigned to the relation for the subclass entity.
Identifier of the superclass serves as the primary key for the superclass relation.
Assign attributes unique to each subclass to each subclass relation.
Primary key of superclass migrates as a foreign key to each subclass relation.
Transform the subclass discriminator as a multi-valued attribute in case of the overlap rule of EER.

44. Task 7: Transforming Superclass/Subclass Relationships
Transformation of a Superclass/Subclass Relationship

45. Task 7: Transforming Superclass/Subclass Relationships
Transformed
Superclass/
Subclass
Relationship

46. Task 8: Transforming Weak Entities
Create a relation for the owner entity type.
Create a new relation for a weak entity type and transform it as a regular entity.
Include the primary key of the owner relation as a foreign key.
Show referential integrity constraints.
Primary key of the new relation is the composite key formed by partial identifier attributes of the weak entity and a foreign key.

47. Task 8: Transforming Weak Entities
Transformation of a Weak Entity

48. Task 9: Transforming Associative Entities
Create two relations, one for each entity types.
Transform them as regular entities.
Create another relation for the associative entity and transform it as a regular entity.
Add the primary key of relations for participating entities as a foreign key for the new relation.
Associative entity with its own identifier becomes primary key for that relation.
If associative entity does not have its own identifier, the keys of the participating entities serve as the composite primary key.

49. Task 9: Transforming Associative Entities
Transformation of an Associative Entity With an Identifier

50. Overview 4.1 Introduction 4.2 The Relational Data Model
4.3 Relational Keys
4.4 Relational Data Integrity Constraints
4.5 Transforming E-R Diagrams into Relational Schemas
4.6 Case Study: Logical Design for a University Database
4.7 Introduction to Normalization
4.8 Data Redundancy
4.9 Database Anomalies
4.10 Functional Dependencies
4.11 Forms of Normalization
4.12 In-Class Assignment
4.13 Summary

51. Case Study: Logical Design for a University DB
The Relational Schema for the University Database

52. Overview 4.1 Introduction 4.2 The Relational Data Model
4.3 Relational Keys
4.4 Relational Data Integrity Constraints
4.5 Transforming E-R Diagrams into Relational Schemas
4.6 Case Study: Logical Design for a University Database
4.7 Introduction to Normalization
4.8 Data Redundancy
4.9 Database Anomalies
4.10 Functional Dependencies
4.11 Forms of Normalization
4.12 In-Class Assignment
4.13 Summary

53. Normalization
Normalization refers to a series of tests performed on relations to determine whether they satisfy or violate the requirements of a normal form.
Technique of decomposing given relations to produce smaller, well-structured sets of relations with desirable properties.

54. Overview 4.1 Introduction 4.2 The Relational Data Model
4.3 Relational Keys
4.4 Relational Data Integrity Constraints
4.5 Transforming E-R Diagrams into Relational Schemas
4.6 Case Study: Logical Design for a University Database
4.7 Introduction to Normalization
4.8 Data Redundancy
4.9 Database Anomalies
4.10 Functional Dependencies
4.11 Forms of Normalization
4.12 In-Class Assignment
4.13 Summary

55. Data Redundancy
Data redundancy is having duplicate data in the database
Increases use of disk storage
Reduces efficiency of data updates
Data Redundancies

56. Overview 4.1 Introduction 4.2 The Relational Data Model
4.3 Relational Keys
4.4 Relational Data Integrity Constraints
4.5 Transforming E-R Diagrams into Relational Schemas
4.6 Case Study: Logical Design for a University Database
4.7 Introduction to Normalization
4.8 Data Redundancy
4.9 Database Anomalies
4.10 Functional Dependencies
4.11 Forms of Normalization
4.12 In-Class Assignment
4.13 Summary

57. Database Anomalies
Relations with redundant data may cause additional inconsistency problems known as anomalies
Example:
VETOFFICE (ClientID, Client Name, PetID, PetName, PetWt, VetID, VetName)
The VetOffice
Relation

58. Database Anomalies (cont.)
Insertion Anomalies:
Inserting a new record for veterinarian will leave the values of VetID and VetName to be NULL in the Client relation.
This is prohibited.
Deletion Anomalies:
If we delete all records for a client, the deletion does not reflect in the records for all vets who work for that client.
Critical data is deleted.
Update Anomalies:
If the weight for a certain pet updated, all records that feature this pet’s weight need to be updated.

59. Overview 4.1 Introduction 4.2 The Relational Data Model
4.3 Relational Keys
4.4 Relational Data Integrity Constraints
4.5 Transforming E-R Diagrams into Relational Schemas
4.6 Case Study: Logical Design for a University Database
4.7 Introduction to Normalization
4.8 Data Redundancy
4.9 Database Anomalies
4.10 Functional Dependencies
4.11 Forms of Normalization
4.12 In-Class Assignment
4.13 Summary

60. Functional Dependency
Functional dependency is a relationship among attributes
An attribute B is functionally dependent of attribute A, if given a value of A, value of B is uniquely defined
Denoted as A  B
Each valid value of A maps exactly to one value of B
Normalization is based on the analysis of functional dependencies within a relation

61. Functional Dependency (cont.)
Examples for the VetOffice relation:
ClientID  ClientName, VetID, VetName
PetID  PetName, PetWeight
VetID  VetName
ClientID, PetID  ClientName, PetName, PetWeight, VetID, VetName
ClientID, PetID, VetID  ClientName, PetName, PetWeight, VetName

62. Determinants
Determinants are the attribute(s) on the left-hand side of the arrow in a functional dependency representation
Examples:
ClientID, PetID, and ClientID-PetID from the previous slide
If all attributes appear in the functional dependency representation then the determinant is the super key
This helps in finding out the primary key for the relation
(ClientID, PetID) is the minimal super key and hence is selected as the primary key

63. Dependency Diagram
A dependency diagram is a pictorial representation of a functional dependency
Dependency Diagram for the VetOffice Relation

64. Types of Functional Dependencies
A partial dependency is a functional dependency in which a non-primary key’s attributes functionally depend on a part of (but not all) the primary key attributes
Example: ClientID  ClientName, VetID, VetName
A transitive dependency is a functional dependency in which none of the attributes involves attributes of a primary key
Example: VetID  VetName
Partial and Transitive Dependencies

65. Overview 4.1 Introduction 4.2 The Relational Data Model
4.3 Relational Keys
4.4 Relational Data Integrity Constraints
4.5 Transforming E-R Diagrams into Relational Schemas
4.6 Case Study: Logical Design for a University Database
4.7 Introduction to Normalization
4.8 Data Redundancy
4.9 Database Anomalies
4.10 Functional Dependencies
4.11 Forms of Normalization
4.12 In-Class Assignment
4.13 Summary

66. Forms of Normalization
The Normal Forms

67. First Normal Form
A relation is said to be in the first normal form if each cell in the relation contains exactly one value
The VetOffice Relation in Non-1NF Form

68. First Normal Form (cont.)
The VetOffice Relation in 1NF Form

69. Second Normal Form
A relation is said to be in the second normal form if:
It is already in the first normal form
It has no partial functional dependencies
Example:
The relation from previous slide is in first normal form, but the following partial dependencies do exist
ClientID  ClientName, VetID, VetName
PetID  PetName, PetWeight
Decompose client relation into smaller relations

70. Second Normal Form (cont.)
The Second Normal Form: Client and Pet Relations

71. Third Normal Form
A relation is said to be in the third normal form if:
It is already in the second normal form
It has no transitive dependencies
Consider the previous slide Client and Pet relations:
Pet is already in third normal form since there are no dependencies
Client relation has a dependency:
VetID  VetName
Decompose Client relation further smaller relations

72. Third Normal Form (cont.)
Client, Vet, and Pet Relations in Third Normal Form

73. Boyce-Codd Normal Form
Functional dependencies related to candidate keys may also cause data redundancy.
A relation is said to be in Boyce-Codd Normal (BCNF) form if:
It is already in third normal form
Every determinant is a candidate key

74. Boyce-Codd Normal Form (cont.)
Example:
STUDENT (StudentID, Major, Advisor, Major-GPA)
Relation is already in third normal form but there are anomalies:
Update anomaly
Change in advisor
Insertion anomaly
A new advisor is added
Deletion Anomaly
Student is removed and if an advisor has only one advisee
Advisor  Major has its determinant as a non-candidate key

75. Boyce-Codd Normal Form (cont.)
We change primary key from (StudentID, Major) to (StudentID, Advisor)
This doesn’t help much!
The dependency now becomes a partial dependency
Apply the second normal form and third normal tests again

76. Boyce-Codd Normal Form (cont.)
Student Relation
Before BCNF
Student and
Advisor Relations
in BCNF

77. Principles for Good Database Design
Relations in a well designed database meet the following criteria:
No redundancy
No partial dependencies
No transitive dependencies
Guidelines:
Identify entities involved and their relevant attributes and identifiers
Define relationships between entities
Draw an E-R / EER diagram to model the problem
Transform the E-R / EER model to a relational schema
Normalize the relations up to BCNF

78. Overview 4.1 Introduction 4.2 The Relational Data Model
4.3 Relational Keys
4.4 Relational Data Integrity Constraints
4.5 Transforming E-R Diagrams into Relational Schemas
4.6 Case Study: Logical Design for a University Database
4.7 Introduction to Normalization
4.8 Data Redundancy
4.9 Database Anomalies
4.10 Functional Dependencies
4.11 Forms of Normalization
4.12 In-Class Assignment
4.13 Summary

79. In-Class Assignment
Create a relational schema for a book store that wishes to keep information about the following entities:
BOOK: book name, author name, publication date, and price
PUBLISHER: publisher’s name, address, and telephone number
CUSTOMER: customer’s name, address, and reading preferences
Develop an appropriate E-R diagram for the above entities and transform it to a relational schema showing all integrity constraints.
Consider Address as a composite attribute
Consider ReadingPreferences as a multi-valued attribute

80. Overview 4.1 Introduction 4.2 The Relational Data Model
4.3 Relational Keys
4.4 Relational Data Integrity Constraints
4.5 Transforming E-R Diagrams into Relational Schemas
4.6 Case Study: Logical Design for a University Database
4.7 Introduction to Normalization
4.8 Data Redundancy
4.9 Database Anomalies
4.10 Functional Dependencies
4.11 Forms of Normalization
4.12 In-Class Assignment
4.13 Summary

81. Summary The relational data model consists of three components:
Relational data structure:
Where data is organized.
Data manipulation:
Operations used to manipulate data stored in the data structure.
Relational data integrity:
Rules that maintain the integrity of data when they are manipulated.
The main data structure that stores and organizes data in the relational data model is a relation, a two-dimensional grid that holds data about the object represented in the database.
A column of a relation is referred to as an attribute.
The number of attributes in a relation is defined as a degree.
A row is referred to as a record, or a tuple.
The number of records in a relation is defined as cardinality.

82. Summary - Keys
A super key is a set of one or more attributes that uniquely identifies each record in a relation.
A candidate key is a super key with a minimum number of attributes.
The primary key is a candidate key that has been selected to identify unique records in a relation.
The foreign key is an attribute or a set of attributes of a relation that serves as a primary key of the same or other relation.

83. Summary - Constraints
The domain constraint states that the values of an attribute must be from the same domain.
Entity constraints ensure that every relation of a relational data model has a primary key and primary key cannot be NULL.
A referential integrity constraint ensures that the foreign key values of a relation must come from the primary key values in the related relation; otherwise, the value of a foreign key must be NULL.

84. Summary - Anomalies
Relations with redundant data may cause inconsistency problems known as anomalies.
Deletion anomalies occur when data has been removed from the database unintentionally.
Insertion anomalies occur when we want to add a new record to the relation and not all of the information is available.
Update anomalies occur when the DBMS must make multiple changes to reflect a single attribute change.

85. Summary - Dependency
A functional dependency is a relationship among attributes.
A dependency diagram is a pictorial representation of a functional dependency.
A partial dependency is a functional dependency in which the non-primary key attributes functionally depend on a part of (but not all) the primary key.
A transitive dependency is a functional dependency in which none of the attributes involves attributes of a primary key.

86. Summary - Normalization
The term normalization, as defined by Codd, refers to a series of tests performed on relations that determines whether a given relation satisfies or violates the requirements of a normal form.
A relation is said to be in the first normal form if each cell in the relation contains exactly one value.
A relation is said to be in the second normal form if it is already in the first normal form and there are no partial functional dependencies.
A relation is said to be in the third normal form if it is already in the second normal form and if it has no transitive dependencies.
A relation is said to be in the BCNF if it is already in the third normal form and if every determinant is a candidate key.

87. Additional Links
Add links here.