1. Chapter 9 Normalizing Database Designs
Database Principles: Fundamentals of Design, Implementation, and Management Tenth Edition
Chapter 9
Normalizing Database Designs

2. NORMALIZATION What is normalization?
Normalization is a procedure that is used to eliminate data redundancy while ensuring data integrity.
Data integrity : ensures the quality of the data in the database
(satisfies data constraint rules. i.e. Primary Key,
Foreign Key, etc.)
Data redundancy: if data can be found in two places in a single database (direct redundancy) or calculated using data from different parts of the database (indirect redundancy) then redundancy exists.
The goal is to remove redundancy and other data modification (insertion, update and deletion) problems

3. What is Data redundancy?
When a group of attributes has multiple values then we say there is
a repeating group of attributes in the relation
Example:
Movie
Title
Year
Length
FilmType
StudioName
StarName
Harry Potter
2003
180
Color
WarnerBros
JK Rowling
Mark Hamill
Harrison Ford
Flight Plan
2005
110
Fox
Judie Foster
Sean Penn
190
Mike Mayers
REPEATING GROUP

4. Problems of redundancy
If redundancy exists then this can cause problems during normal database operations:
Updates - when some data needs altering, we find it's in several places.
Insertion - When we want to insert something, we can't because some other data is also required which we don't have.
Deletion - When something is deleted, other data is lost as well.

5. Integrity Constraints
An integrity constraint is a rule that restricts the values that may be present in the database.
Entity Integrity: The rows (or tuples) in a relation represent entities, and each one must be uniquely identified.
Hence we have the primary key that must have a unique not-null value for each row.
Referential Integrity: This constraint involves the foreign keys.
Foreign keys tie the relations together, so it is vitally important that the links are correct. Every foreign key must either be null or its value must be the actual value of a key in another relation.

6. Entity Integrity: Selecting Primary Keys
Primary key is the most important characteristic of an entity
Single attribute or some combination of attributes
Primary key’s function is to guarantee entity integrity
Primary keys and foreign keys work together to implement relationships
Properly selecting primary key has direct bearing on efficiency and effectiveness

7. Natural Keys and Primary Keys
Natural key is a real-world identifier used to uniquely identify real-world objects
Familiar to end users and forms part of their day-to-day business vocabulary
Generally, data modeler uses natural identifier as primary key of entity being modeled
May instead use composite primary key or surrogate key

8. Primary Key Guidelines
Attribute that uniquely identifies entity instances in an entity set
Could also be combination of attributes
Main function is to uniquely identify an entity instance or row within a table
Guarantee entity integrity, not to “describe” the entity
Primary keys and foreign keys implement relationships among entities
Behind the scenes, hidden from user

10. When to Use Composite Primary Keys
Composite primary keys useful in two cases:
As identifiers of composite entities
In which each primary key combination is allowed once in M:N relationship
As identifiers of weak entities
In which weak entity has a strong identifying relationship with the parent entity
Automatically provides benefit of ensuring that there cannot be duplicate values

12. When to Use Composite Primary Keys (cont’d.)
When used as identifiers of weak entities normally used to represent:
Real-world object that is existent-dependent on another real-world object
Real-world object that is represented in data model as two separate entities in strong identifying relationship
Dependent entity exists only when it is related to parent entity

13. When To Use Surrogate Primary Keys
Especially helpful when there is:
No natural key
Selected candidate key has embedded semantic contents
Selected candidate key is too long or cumbersome

14. When To Use Surrogate Primary Keys (cont’d.)
If you use surrogate key:
Ensure that candidate key of entity in question performs properly
Use “unique index” and “not null” constraints

16. KEY Key: We can say that one or more attributes of a Relation can be a
Key for that Relation.
Those attributes functionally determine all other attributes of the relation. (There can’t be more than one let say two distinct tuple that is key for a Relation)
No proper subset of the Relation functionally determines all other attributes of Relation.
A key must be minimal.
Super Key: Is an attribute or set of attributes whose closure contains all attributes of the table. (A table may contain many S.K.)
Candidate Key: Is a minimal S.K. (Contains no attributes that are not needed to form a S.K.) (A table may have many C.K.)
Primary Key: The C.K. chosen for implementation.
Each table has only 1 P.K.

17. Example
StaffBranch
staffNo
sName
position
salary
branchNo
bAddress
SL21
John White
Manager
30000
B005
22 Deer Rd, London
SG37
Ann Beech
Assistant
12000
B003
163 Main St, Glasgow
SG14
David Ford
Supervisor
18000
SA9
Mary Howe
9000
B007
16 Argyll St, Aberdeen
SG5
Susan Brand
24000
SL41
Julie Lee
Functional Dependency? Describes the relationship between the attributes in a relation
Reasonable Functional Dependencies (FDs):
staffNo  sName, position, salary branchNo  bAddress
So, what is the Key for StaffBranch Relation?

18. Closure Closure is denoted by: { }+
Closure of an attribute or set of attributes is the set that contains all attributes that can be determined directly or indirectly by the given attribute or set of attributes.
Procedure for Computing Closure of {A1,A2,…..An}
START ans={A1,A2,…..An}
REPEAT until ans does not change for each FD
if the Left Hand Side(LHS) is in ans
ADD the Right Hand Side(RHS) to ans
STOP
Closure is denoted by: { }+

19. Computing Closure Consider the following Relation and FDs:
StaffBranch(staffNo,sName,position,salary,branchNo,bAddress)
FDs:
staffNo  sName, position, salary
branchNo  bAddress
Compute the Closures of given attributes:
{staffNo}+= {staffNo,sName,position,salary}
{branchNo}+= {branchNo,bAddress}
{staffNo,branchNo}+={StaffNo,sName,position,salary, branchNo, bAddress}

20. Boyce Codd Normal Form (BCNF)
A table is in BCNF if the determinant of all Functional Dependencies are Super Key.
Procedure for BCNF:
Step1: Check if the Functional Dependencies violate BCNF. Compute closure of all determinants. Find the Functional Dependencies (F.D.s) that violate BCNF.
Step2: Choose any violating F.D. and form a new table from its closure. List all F.D.s that valid on the new table. Find the Primary Key.
Step3: Eliminate all the non key attributes of the table formed in Step2 from the table that you normalize. Form a new table from the remainders. List all F.D.s that are valid on the new table.

21. EXAMPLES will be solved in the lecture session.

22. Database Tables and Normalization
Process for evaluating and correcting table structures to minimize data redundancies
Reduces data anomalies
Series of stages called normal forms:
First normal form (1NF)
Second normal form (2NF)
Third normal form (3NF)

23. Database Tables and Normalization (cont’d.)
Normalization (continued)
2NF is better than 1NF; 3NF is better than 2NF
For most business database design purposes, 3NF is as high as needed in normalization
Highest level of normalization is not always most desirable
Denormalization produces a lower normal form
Increased performance but greater data redundancy

24. The Need for Normalization
Example: company that manages building projects
Charges its clients by billing hours spent on each contract
Hourly billing rate is dependent on employee’s position
Periodically, report is generated that contains information such as displayed in Table 6.1

26. The Need for Normalization (cont’d.)
Structure of data set in Figure 9.1 does not handle data very well
Table structure appears to work; report is generated with ease
Report may yield different results depending on what data anomaly has occurred
Relational database environment is suited to help designer avoid data integrity problems

27. The Normalization Process
Each table represents a single subject
No data item will be unnecessarily stored in more than one table
All nonprime attributes in a table are dependent on the primary key
Each table is void of insertion, update, and deletion anomalies

29. The Normalization Process (cont’d.)
Objective of normalization is to ensure that all tables are in at least 3NF
Higher forms are not likely to be encountered in business environment
Normalization works one relation at a time
Progressively breaks table into new set of relations based on identified dependencies

31. The Normalization Process (cont’d.)
Partial dependency
Exists when there is a functional dependence in which the determinant is only part of the primary key
Transitive dependency
Exists when there are functional dependencies such that X → Y, Y → Z, and X is the primary key

32. Conversion to First Normal Form
Repeating group
Group of multiple entries of same type can exist for any single key attribute occurrence
Relational table must not contain repeating groups
Normalizing table structure will reduce data redundancies
Normalization is three-step procedure

33. Conversion to First Normal Form (cont’d.)
Step 1: Eliminate the Repeating Groups
Eliminate nulls: each repeating group attribute contains an appropriate data value
Step 2: Identify the Primary Key
Must uniquely identify attribute value
New key must be composed
Step 3: Identify All Dependencies
Dependencies are depicted with a diagram

35. Conversion to First Normal Form (cont’d.)
Dependency diagram:
Depicts all dependencies found within given table structure
Helpful in getting bird’s-eye view of all relationships among table’s attributes
Makes it less likely that you will overlook an important dependency

37. Conversion to First Normal Form (cont’d.)
First normal form describes tabular format:
All key attributes are defined
No repeating groups in the table
All attributes are dependent on primary key
All relational tables satisfy 1NF requirements
Some tables contain partial dependencies
Dependencies are based on part of the primary key
Should be used with caution

38. Conversion to Second Normal Form
Step 1: Make New Tables to Eliminate Partial Dependencies
Write each key component on separate line, then write original (composite) key on last line
Each component will become key in new table
Step 2: Reassign Corresponding Dependent Attributes
Determine attributes that are dependent on other attributes
At this point, most anomalies have been eliminated

40. Conversion to Second Normal Form (cont’d.)
Table is in second normal form (2NF) when:
It is in 1NF and
It includes no partial dependencies:
No attribute is dependent on only portion of primary key

41. Conversion to Third Normal Form
Step 1: Make New Tables to Eliminate Transitive Dependencies
For every transitive dependency, write its determinant as PK for new table
Determinant: any attribute whose value determines other values within a row

42. Conversion to Third Normal Form (cont’d.)
Step 2: Reassign Corresponding Dependent Attributes
Identify attributes dependent on each determinant identified in Step 1
Identify dependency
Name table to reflect its contents and function

44. Conversion to Third Normal Form (cont’d.)
A table is in third normal form (3NF) when both of the following are true:
It is in 2NF
It contains no transitive dependencies

45. Improving the Design
Table structures should be cleaned up to eliminate initial partial and transitive dependencies
Normalization cannot, by itself, be relied on to make good designs
Valuable because it helps eliminate data redundancies

46. Improving the Design (cont’d.)
Issues to address, in order, to produce a good normalized set of tables:
Evaluate PK Assignments
Evaluate Naming Conventions
Refine Attribute Atomicity
Identify New Attributes

47. Improving the Design (cont’d.)
Identify New Relationships
Refine Primary Keys as Required for Data Granularity
Maintain Historical Accuracy
Evaluate Using Derived Attributes

50. Surrogate Key Considerations
When primary key is considered to be unsuitable, designers use surrogate keys
Data entries in Table 6.4 are inappropriate because they duplicate existing records
No violation of entity or referential integrity

51. Higher-Level Normal Forms
Tables in 3NF perform suitably in business transactional databases
Higher-order normal forms are useful on occasion
Two special cases of 3NF:
Boyce-Codd normal form (BCNF)
Fourth normal form (4NF)

52. The Boyce-Codd Normal Form
Every determinant in table is a candidate key
Has same characteristics as primary key, but for some reason, not chosen to be primary key
When table contains only one candidate key, the 3NF and the BCNF are equivalent
BCNF can be violated only when table contains more than one candidate key

53. The Boyce-Codd Normal Form (cont’d.)
Most designers consider the BCNF as a special case of 3NF
Table is in 3NF when it is in 2NF and there are no transitive dependencies
Table can be in 3NF and fail to meet BCNF
No partial dependencies, nor does it contain transitive dependencies
A nonkey attribute is the determinant of a key attribute

57. Fourth Normal Form (4NF)
Table is in fourth normal form (4NF) when both of the following are true:
It is in 3NF
No multiple sets of multivalued dependencies
4NF is largely academic if tables conform to following two rules:
All attributes dependent on primary key, independent of each other
No row contains two or more multivalued facts about an entity

60. Normalization and Database Design
Normalization should be part of the design process
Make sure that proposed entities meet required normal form before table structures are created
Many real-world databases have been improperly designed or burdened with anomalies
You may be asked to redesign and modify existing databases

61. Normalization and Database Design (cont’d.)
ER diagram
Identify relevant entities, their attributes, and their relationships
Identify additional entities and attributes
Normalization procedures
Focus on characteristics of specific entities
Micro view of entities within ER diagram
Difficult to separate normalization process from ER modeling process

67. Denormalization
Creation of normalized relations is important database design goal
Processing requirements should also be a goal
If tables are decomposed to conform to normalization requirements:
Number of database tables expands

68. Denormalization (cont’d.)
Joining the larger number of tables reduces system speed
Conflicts are often resolved through compromises that may include denormalization
Defects of unnormalized tables:
Data updates are less efficient because tables are larger
Indexing is more cumbersome
No simple strategies for creating virtual tables known as views

69. Data-Modeling Checklist
Data modeling translates specific real-world environment into data model
Represents real-world data, users, processes, interactions
Data-modeling checklist helps ensure that data-modeling tasks are successfully performed
Based on concepts and tools learned in Part II