Normalization

Normalization is the process associated with the relational data model to organize data sets with high dependency and or tight linkage.

Normalization is a technique for organizing data into tables to meet the needs of users in

within an organization.

The purpose of normalization:

- To remove data kerangkapan

- To reduce the complexity

- To facilitate the modification of data

- Determining whether a particular relationship beradadalam "good form"

• In cases where the relation R is not in "good form", then the relation

decomposed into a set of relations (R1, R2, ..., Rn) where:

- Every relationship is in "good form".

Decomposition is lossless-join decomposition qualify.

Normalization Process

The data described in table form, and analyzed

under certain conditions to some degree.

If the tables are tested do not meet certain requirements,

then the table needs to be broken into multiple tables

simpler to meet the optimal shape.



The result of the normalization process are sets of

data in normal form (normal form). There are some normal form, namely:
a. First Normal Form (First Normal Form / 1-NF).

b. Second Normal Form (Second Normal Form / 2-NF).

c. Normal Form III (Third Normal Form / 3-NF).

d. Normal Form IV (Fourth Normal Form / 4-NF).

e. Boyce-Codd Normal Forms (Boyce-Codd Normal Form / BCNF).

f. First Project-Join Normal Form (PJNF).

g. I Domain-Key Normal Form (DKNF).

h. Normal Form V (Fifth Normal Form / 5-NF).



Usefulness of normalization:

a. Minimize repetition of information.

b. Facilitate the identification of entities / objects.



a. First Normal Form (First Normal Form / 1-NF).

A relation meet the 1-NF if and only if every attribute of the relation

have only a single value in a single row or record.

A relation is said to have fulfilled the Normal Form

One if any data is atomic ie, each slice

rows and columns have only one data value





b. Second Normal Form (Second Normal Form / 2-NF).



A relation is said to have fulfilled the Second Normal Form if those relationships are already fulfilling the Normal form of unity, and non-key attribute is fully dependent on keynya.

A relation meet the 2-NF if and only if:

a. Meet the 1-NF.

b. Each non-key attributes are functionally dependent

against all the key attributes and not just some attributes.

If a relation satisfy 1-NF and the relationships it has exactly one

attributes that make up the primary key, then the relationship meets the 2-NF.

Rationalization of 2-NF:

a. Having a more explicit semantics of 1-NF.

b. Prevent some anomalies in the data updating condition.

Functional dependence is to:

a. StudentID => Student's, Birthdate (SC1).

b. CourseID => Course, Credit (SC2).

c. StudentID, CourseID => Grade (SC3, SC3A).

d. Grade => Weight (SC3B).



Finally, all the tables SC1, SC2, SC3A, SC3B is in 3-NF,

so that all databases have the condition of 3-NF.

c. Normal Form III (Third Normal Form / 1-NF).

A relation is said to already meet the third Normal Form

when those relationships are already fulfilling the second normal form

and non-key attributes depend not transitive on

keynya



A relation to meet the form III (3-NF) if and only if:

a. The relation is to meet 2-NF.

b. Each key attribute is not does not depend functionally to

Another key attribute is not in relation.

A relation that meets the 2-Nf and only has one key attribute is not

always meet the 3-NF.



d. Boyce-Codd Normal Form (Boyce-Codd Normal Form / BCNF).



A relation satisfy BCNF if and only if every determinant of the existing

on these relationships is a key candidate (candidate keys).

The determinant is a cluster of attributes dimanaa one or more other attributes

functionally dependent.

e. Relationship model or relationship entities (Entity Realtionship (ER) model).



Entity relationship model is based on the perception of the real world

set of basic objects called entities and relationships among entities.

The entities are objects that can be uniquely identified.

Entities are characterized and presented with a cluster of attributes.

Examples of entity attributes WORKERS cluster is the name, date of birth, NIP,

class / rank.

A group of entities which have a characterization of an entity called the cluster variance

(Entity sets).

Each entity of this cluster is called cluster members (member of the set).

Examples of cluster entities are entities bank employee group, customer group entities

bank. Of some clusters had a relationship may occur, for example relations

between bank groups with customers of the bank group.

Based on the number of groups involved, the relationships among entities are distinguished

becomes:

a. Binary relations (binary), ie the relation between the 2 groups of entities.

b. Relationships trio (ternary), namely the relationship between 3 clusters of entities.

c. N-ary relations, ie relationships among n cluster entities.



Especially for a binary relation then the relation between members of two clusters

involved (the cardinality of binary relationships) can be:

a. Relation 1-1 (one-to-one relationship).

Is a member of the group entities associated with exactly one entity

other group members.



b. Relationship 1-many (one-to-many relationships).

Is a member of the group entities associated with one or more

another group member entities. In contrast one group member entities

others are associated with exactly one entity member of the group

partner.



c. Many-1 relations (many-to-one relationship).

Is a member of the group entities associated with one or more

entities other cluster members and vice versa applies.