Database Design

Database Design

Informal Design Guidelines for Relation Schemas; Functional Dependencies; Normal Forms Based on Primary Keys; General Definitions of Second and Third Normal Forms; Boyce-Codd Normal Form.

Fourth Normal Form; and Fifth Normal Form;

INFORMAL DESIGHN GUIDELINES FOR RELATIONAL SCHEMA

1. Semantics of the Attributes

2. Reducing the Redundant Value in Tuples.

3.Reducing Null values in Tuples.

4.Dissallowing spurious Tuples.

1. Semantics of the Attributes

Whenever we are going to form relational schema there should be some meaning among the attributes.This meaning is called semantics.This semantics relates one attribute to another with some relation.

2. Reducing the Redundant Value in Tuples

Mixing attributes of multiple entities may cause problems Information is stored redundantly wasting storage Problems with update anomalies

Insertion anomalies

Deletion anomalies

Modification anomalies

The main goal of the schema diagram is to minimize the storage space that the base memory occupies.Grouping attributes information relations has asignificant effect on storage space.

Here whenever if we insert the tuples there may be ‘N’ stunents in one department,so Dept No,Dept Name values are repeated ‘N’ times which leads to data redundancy.

Another problem is updata anamolies ie if we insert new dept that has no students.

If we delet the last student of a dept,then whole information about that department will be deleted

If we change the value of one of the attributes of aparticaular table the we must update the tuples of all the students belonging to thet depy else Database will become inconsistent.

Note: Design in such a way that no insertion ,deletion,modification anamolies will occur

3. Reducing Null values in Tuples.

Note: 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 (with the primary key)

Reasons for nulls:

attribute not applicable or invalid

attribute value unknown (may exist)

value known to exist, but unavailable

4. Disallowing spurious Tuples

Bad designs for a relational database may result in erroneous results for certain JOIN operations

The "lossless join" property is used to guarantee meaningful results for join operations

Note: The relations should be designed to satisfy the lossless join condition. No spurious tuples should be generated by doing a natural-join of any relations.

Functional dependency

1. Functional dependencies (FDs) are used to specify formal measures of the "goodness" of relational designs

2. FDs and keys are used to define normal forms for relations

3. FDs are constraints that are derived from the meaning and interrelationships of the data attributes

4. X->Y : A set of attributes X functionally determines a set of attributes Y if the value of X determines a unique value for Y

5. X -> Y holds if whenever two tuples have the same value for X, they must have

the same value for Y

6. For any two tuples t1 and t2 in any relation instance r(R): If t1[X]=t2[X], then

t1[Y]=t2[Y]

7. X -> Y in R specifies a constraint on all relation instances r(R)

8. Written as X -> Y; can be displayed graphically on a relation schema as in Figures. ( denoted by the arrow: ).

9. FDs are derived from the real-world constraints on the attributes

10. social security number determines employee name SSN -> ENAME

11. project number determines project name and location PNUMBER -> {PNAME, PLOCATION}

11. employee ssn and project number determines the hours per week that the employee works on the project

{SSN, PNUMBER} -> HOURS

Inference rules for FDs:

Inference rules also known as Armstrong's Axioms are published by Armstrong. These properties are as given below:

1. Reflexivity property: X ->Y is true if Y is subset of X.

2. Augmentation property: If X->Y is true, then

XZ ->YZ is also true.

3. Transitivity property: If X->Y and Y->Z then

X ->Z is implied.

4. Union property: If X ->Y and X ->Z are true, then

X ->YZ is also true. This property indicates that if right hand side of FD contains many attributes then FD exists for each of them.

5. Decomposition property: If X ->Y is implied and Z is subset of Y, then X ->Z is implied. This property is the reverse of union property.

6. Pseudotransitivity property: If X ->Y and WY ->Z are given, then XW ->Z is true.

Normalization:

Normalizing the database ensures the following things: Dependencies between data are identified.

Redundant data is minimized.

The data model is flexible and easier to maintain

Normalization: The process of decomposing unsatisfactory "bad" relations by breaking up their attributes into smaller relations

Normal form: Condition using keys and FDs of a relation to certify whether a relation schema is in a particular normal form

FIRST NORMAL FORM

The purpose of first normal form (1NF) is to eliminate repeating groups of attributes in an entity.

Disallows composite attributes, multivalued attributes, and nested relations; attributes whose values for an individual tuple are non-amanjuic

Consider the following table:

 

SECOND NORMAL FORM

• The purpose of second normal form (2NF) is to eliminate partial key dependencies.

• Each attribute in an entity must depend on the whole key, not just a part of it.

THIRD NORMAL FORM

Third Normal form also helps to eliminate redundant information by eliminating interdependencies between non-key attributes.

It is already in 2NF

There are no non-key attributes that depend on another non-key attribute

General Normal Form Definitions (For Multiple Keys)

The above definitions consider the primary key only

The following more general definitions take into account relations with multiple candidate keys

A relation schema R is in second normal form (2NF) if every non-prime attribute A in R is fully functionally dependent on every key of R

Definition:

Superkey of relation schema R - a set of attributes S of R that contains a key of R A relation schema R is in third normal form (3NF) if whenever a FD X -> A holds in R, then either:

X is a superkey of R, or

A is a prime attribute of R

Example 1

CUSTOMER

This is not in strict 3NF as the City could be obtained from the Post code attribute. If you create a table containing postcodes then city could be derived.

The Description of what the certificate means could be obtained frome the certifcate attribute - it does not need to refer to the primary key VideoID. So split it out and use the primary key / secondary key approach.

Example 3

CLIENT

In this case the database is almost in 3NF - for some reason the Cinema Address is being repeated in the Client table, even though it can be obtained from the Cinemas table. So simply remove the column from the client table

BOYCE-CODD NORMAL FORM (BCNF)

A relation schema R is in Boyce-Codd Normal Form (BCNF) if whenever an FD X

-> A holds in R, then X is a superkey of R

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

Definition:

A multivalued dependency (MVD) X —>> Y specified on relation

schema R, where X and Y are both subsets of R, specifies the following constraint on any relation state r of R: If two tuples t1 and t2 exist in r such that t1[X] = t2[X], then two tuples t3 and t4 should also

exist in r with the following properties, where we use Z to denote (R - (X υ Y)):

Fourth Normal Form (4NF)

• Fourth normal form eliminates independent many-to-one relationships between columns.

• To be in Fourth Normal Form,

– a relation must first be in Boyce-Codd Normal Form.

– a given relation may not contain more than one multi-valued attribute.

• Defined as a relation that is in Boyce-Codd Normal Form and contains no nontrivial multi-valued dependencies.

Example

Fifth Normal Form (5NF)

A relation decompose into two relations must have the lossless-join property, which ensures that no spurious tuples are generated when relations are reunited through a natural join operation.

However, there are requirements to decompose a relation into more than two relations. Although rare, these cases are managed by join dependency and fifth normal form (5NF).

General constraints: constraints that do not fit in

the basic SQL categories

Mechanism: CREAT ASSERTION

Components include: a constraint name, followed by CHECK,

followed by a condition

Assertions: An Example

“The salary of an employee must not be greater

than the salary of the manager of the department that the employee works for’’

CREAT ASSERTION SALARY_CONSTRAINT CHECK (NOT EXISTS (SELECT *

FROM EMPLOYEE E, EMPLOYEE M, DEPARTMENT D

WHERE E.SALARY > M.SALARY AND E.DNO=D.NUMBER AND D.MGRSSN=M.SSN))

SQL Triggers

Objective: to monitor a database and take initiate action when a condition occurs

Triggers are expressed in a syntax similar to assertions and include the following:Event

Such as an insert, deleted, or update operation Condition

Action

To be taken when the condition is satisfied

SQL Triggers: An Example

A trigger to compare an employee’s salary to his/her supervisor during insert or update operations: