DBMS Concepts

DBMS Tutorial Components of DBMS. Applications of DBMS The difference between file system and DBMS. Types of DBMS DBMS Architecture DBMS Schema Three Schema Architecture. DBMS Languages.

DBMS ER Model

ER model: Entity Relationship Diagram (ERD) Components of ER Model. DBMS Generalization, Specialization and Aggregation.

DBMS Relational Model

Codd’s rule of DBMS Relational DBMS concepts Relational Integrity Constraints DBMS keys Convert ER model into Relational model Difference between DBMS and RDBMS Relational Algebra DBMS Joins

DBMS Normalization

Functional Dependency Inference Rules Multivalued Dependency Normalization in DBMS: 1NF, 2NF, 3NF, BCNF and 4NF

DBMS Transaction

What is Transaction? States of transaction ACID Properties in DBMS Concurrent execution and its problems DBMS schedule DBMS Serializability Conflict Serializability View Serializability Deadlock in DBMS Concurrency control Protocols

Difference

Difference between DFD and ERD

Misc

Advantages of DBMS Disadvantages of DBMS Data Models in DBMS Relational Algebra in DBMS Cardinality in DBMS Entity in DBMS Attributes in DBMS Data Independence in DBMS Primary Key in DBMS Foreign Key in DBMS Candidate Key in DBMS Super Key in DBMS Aggregation in DBMS Hashing in DBMS Generalization in DBMS Specialization in DBMS View in DBMS File Organization in DBMS What Is A Cloud Database What Is A Database Levels Of Locking In DBMS What is RDBMS Fragmentation in Distributed DBMS What is Advanced Database Management System Data Abstraction in DBMS Checkpoint In DBMS B Tree in DBMS BCNF in DBMS Advantages of Threaded Binary Tree in DBMS Advantages of Database Management System in DBMS Enforcing Integrity Constraints in DBMS B-Tree Insertion in DBMS B+ Tree in DBMS Advantages of B-Tree in DBMS Types of Data Abstraction in DBMS Levels of Abstraction in DBMS 3- Tier Architecture in DBMS Anomalies in Database Management System Atomicity in Database Management System Characteristics of DBMS DBMS Examples Difference between Relational and Non-Relational Databases Domain Constraints in DBMS Entity and Entity set in DBMS ER Diagram for Banking System in DBMS ER Diagram for Company Database in DBMS ER Diagram for School Management System in DBMS ER Diagram for Student Management System in DBMS ER Diagram for University Database in DBMS ER Diagram of Company Database in DBMS Er Diagram Symbols and Notations in DBMS How to draw ER-Diagram in DBMS Integrity Constraints in DBMS Red-Black Tree Deletion in DBMS Red-Black Tree Properties in DBMS Red-Black Tree Visualization in DBMS Redundancy in Database Management System Secondary Key in DBMS Structure of DBMS 2-Tier Architecture in DBMS Advantages and Disadvantages of Binary Search Tree Closure of Functional Dependency in DBMS Consistency in Database Management System Durability in Database Management System ER Diagram for Bank Management System in DBMS ER Diagram for College Management System in DBMS ER Diagram for Hotel Management System in DBMS ER Diagram for Online Shopping ER Diagram for Railway Reservation System ER Diagram for Student Management System in DBMS Isolation in DBMS Lossless Join and Dependency Preserving Decomposition in DBMS Non-Key Attributes in DBMS Data Security Requirements in DBMS DBMS functions and Components What is Homogeneous Database? DBMS Functions and Components Advantages and Disadvantages of Distributed Database Relational Database Schema in DBMS Relational Schema Transaction Processing in DBMS Discriminator in DBMS

Inference Rules

Armstrong’s axioms are the complete set of basic inference rules used to infer all the functional dependencies on the relational database. An inference rule is a type of assertion that a user can apply to a set of functional dependencies to derive other FD (functional dependencies). These axioms in database management system were developed by the William w. Armstrong in 1974.

Following are the six most important rules for functional dependency:

1. Reflexive Rule

In the reflexive rule, if X is a set of attributes and Y is the subset of X, then X functionally determines Y.

If Y ? X, then X ? Y.

Example:

Lastname ?  Firstname, Lastname
then, Firstname, Lastname ? Lastname

2. Augmentation Rule

In the augmentation rule, if X determines Y and Z is any attribute set, then XZ determines YZ. It is also called as a partial dependency.

If X ? Y then XZ ? YZ for any Z.

Example:

Regno ? Firstname, Lastname
then, Regno, address ? Firstname, Lastname, address

3. Transitive Rule

In the transitive rule, if X determines Y and Y determines Z, then X also determines Z.

If X ? Y and Y ? Z then X ? Z.

Example:

Rollno ? addressand address ? Pincode
then Rollno ? Pincode

4. Union Rule

This rule is also known as additive rule. In the union rule, if X determines Y and X determines Z, then X also determines both Y and Z.

If X ? Y and X ? Z, then X ? YZ.

Proof of this Rule:

X ? Y (given) …. (1)
X ? Z (given) …. (2)
X ? XY (using Rule 2 on (1) by augmentation with X)     …. (3)
XY ? YZ (using Rule 2 on (2) by augmentation with Y)    …. (4)
X ? YZ (using Rule 3 on (3) and (4))

Example:

Rollno ? name and Rollno ? address
then Rollno ? name, address

5. Decomposition Rule

This rule is the reverse of Union rule and also known as project rule.In the decomposition rule, if X determines Y and Z together, then X determines Y and Z separately.

If X ? YZ then, X ? Y and X ? Z.

Proof of this Rule:

X ? YZ (given)…. (1)
YZ ? Z    and  YZ ?Y  ( using reflexive method) …. (2)
X ? Y and X? Z (using transitive rule) …. (3) 

Example:

Rollno ? Firstname, Lastname
then, Rollno ? Firstname and Rollno ? Lastname

6. Pseudo transitive Rule

In the pseudo transitive rule, if X determines Y, and YZ determines W, then XZ also determines W.

If X ? Yand YZ ? W then XZ ? W.

Proof of this Rule:

X ? Y (given) …. (1)
YZ ? W (given) …. (2)
XZ ? YZ (using augmentation rule in (1))  …(3)
XZ ? W (using a transitive rule in (2) and (3))

Example:

Rollno ? name and name, marks ?percentage
then, Rollno,marks ? percentage