1. Normalization

2. Normalization
Database normalization is the process of removing redundant data from the database to improve storage efficiency, data integrity, and scalability.
In the relational model, methods exist for quantifying how efficient a database is. These classifications are called normal forms (or NF), and there are algorithms for converting a given database between them.
Normalization generally involves splitting existing tables into multiple ones, which must be re-joined or linked each time a query is issued.

3. Why Normalization? (Drawbacks of Redundant Information)
Wastage of Storage
Causes problems with update anomalies
Insertion anomalies
Deletion anomalies
Modification anomalies

4. Benefits of Normalization
Less storage space
Quicker updates
Less data inconsistency
Clearer data relationships
Easier to add data
Flexible Structure

5. Example

6. New relat ion

7. EXAMPLE OF AN UPDATE ANOMALY
Consider the relation:
EMP_PROJ(Emp#, Proj#, Ename, Pname, No_hours)
Changing the name of project number P1 from “Billing” to “Customer Accounting” may cause this update to be made for all employees working on project P1.
EXAMPLE OF AN INSERT ANOMALY
Cannot insert a project unless an employee is assigned to it.
Conversely cannot insert an employee unless an he/she is assigned to a project.
EXAMPLE OF AN DELETE ANOMALY
When a project is deleted, it will result in deleting all the employees who work on that project.
Alternately, if an employee is the sole employee on a project, deleting that employee would result in deleting the corresponding project.

8. Guidelines
Each tuple in a relation should represent one entity or relationship instance.
Design a schema that does not suffer from the insertion, deletion and update anomalies.
If there are any anomalies present, then note them so that applications can be made to take them into account.
Relations should be designed such that their tuples will have as few NULL values as possible
Attributes that are NULL frequently could be placed in separate relations

9. Types of Normalization
First Normal Form
Second Normal Form
Third Normal Form
Boyce Codd Normal Form
Fourth Normal Form (Multi Valued Dependencies)
Fifth Normal Form (Join Dependencies)

10. Functional Dependencies
An attribute Y is said to have a functional dependency on a set of attributes X (written X →Y) if and only if each X value is associated with precisely one Y value.
Functional Dependencies-Example

11. Functional Dependencies-Example
{Ssn,Pnumber} -> {Hours}
{Ssn} -> {Ename }
{Pnumber} -> {Pname, Plocation}

12. Types of Functional Dependencies
Trivial functional dependency
A trivial functional dependency is a functional dependency of an attribute on a superset of itself.
{Ssn,Pnumber} -> {Hours} Trivial
{Ssn} -> {Ename } Non trivial
Full functional dependency
An attribute is fully functionally dependent on a set of attributes X if it is
functionally dependent on X, and
not functionally dependent on any proper subset of X.
{Ssn,Pnumber} -> {Hours}
Transitive dependency
A transitive dependency is an indirect functional dependency, one in which X→Z only by virtue of X→Y and Y→Z.
Multivalued dependency
A multivaluesd dependency is a constraint according to which the presence of certain rows in a table implies the presence of certain other rows.
Join dependency
A table T is subject to a join dependency if T can always be recreated by joining multiple tables each having a subset of the attributes of T.

13. Inference Rules for FDs
(Reflexive) If Y subset-of X, then X -> Y
(Augmentation) If X -> Y, then XZ -> YZ
(Transitive) If X -> Y and Y -> Z, then X -> Z
Decomposition: If X -> YZ, then X -> Y and X -> Z
Union: If X -> Y and X -> Z, then X -> YZ
Pseudo transitivity: If X -> Y and WY -> Z, then WX -> Z

14. First Normal Form (1NF)
A relation is said to be in First Normal Form (1NF) if and only if each attribute of the relation is atomic.
It does not allows the composite and multi valued attributes.
First Normal Form (1NF) sets the very basic rules for an organized database:
Eliminate duplicative columns from the same table.
Create separate tables for each group of related data and identify each row with a unique column (the primary key).

15. First Normal Form (1NF)-Example
Apply First Normal Form ->

16. First Normal Form (1NF)-Example
Decompose the relation into
{Ename, Ssn, Bdate, Address, Dnumber}
{Dnumber, Dname, Dmgr_Ssn}

17. First Normal Form (1NF)-Result

18. Second Normal Form (2NF)
A relation schema R is in second normal form (2NF) if a relation in 1NF and every non key attribute A in R is fully functionally dependent on the primary key

19. Second Normal Form (2NF)-Example

20. FD in 2NF

21. FD in 2NF

22. Results

23. Third Normal Form
A relation schema R is in third normal form (3NF) if a table is in second normal form (2NF) and there are no transitive dependencies.
(OR)
Meet all the requirements of the 1NF
Meet all the requirements of the 2NF
Remove columns that are not dependent upon the primary key.

24. Third Normal Form-Example
FD ?

25. FD in 3NF

26. FD in 3NF

27. Results

28. Comparison of 1NF, 2NF & 3NF 1NF Remove repeating groups
2NF Remove partial key dependencies
3NF Remove transitive dependencies

29. BCNF (Boyce Codd Normal Form)
A relation schema R is in BCNF if for every nontrivial FD X-> Y in R, X is a candidate key.
(OR) A relation is in BCNF, if and only if, every determinant is a candidate key.
Each normal form is strictly stronger than the previous one
Every 2NF relation is in 1NF
Every 3NF relation is in 2NF
Every BCNF relation is in 3NF
There exist relations that are in 3NF but not in BCNF
The goal is to have each relation in BCNF (or 3NF)

30. 3NF VS BCNF
3NF
BCNF
A relation schema R is in 3NF if for every nontrivial FD X-> Y in R, X is not a candidate key
A relation schema R is in Boyce- Codd Normal Form (BCNF) if for every nontrivial FD X-> Y in R, X is a candidate key
3NF has some redundancy
BCNF removes all redundancies caused by FD’s
Performance is Lesser than BCNF
Better Performance than 3NF

31. Example- 1NF,2NF,3NF & BCNF
FD ?

32. Example- 1NF,2NF,3NF & BCNF FD ?
Patno -> PatName Patno,appNo -> Time,doctor

33. Apply Normalization Apply 1NF Eliminating Repeating Groups Apply 2NF
2NF Eliminate partial key dependencies
R1(Patno,appNo,time,doctor)
R2(Patno,PatName) 
Apply 3NF
3NF Eliminate transitive dependencies
None: so just as 2NF
Apply BCNF
Every determinant is a candidate key
It is in BCNF Form.