Found 428 Articles for DBMS

Decomposition in DBMS removes redundancy, anomalies and inconsistencies from a database by dividing the table into multiple tables.The following are the types −Lossless DecompositionDecomposition is lossless if it is feasible to reconstruct relation R from decomposed tables using Joins. This is the preferred choice. The information will not lose from the relation when decomposed. The join would result in the same original relation.Let us see an example −Emp_IDEmp_NameEmp_AgeEmp_LocationDept_IDDept_NameE001Jacob29AlabamaDpt1OperationsE002Henry32AlabamaDpt2HRE003Tom22TexasDpt3FinanceDecompose the above table into two tables:Emp_IDEmp_NameEmp_AgeEmp_LocationE001Jacob29AlabamaE002Henry32AlabamaE003Tom22TexasDept_IDEmp_IDDept_NameDpt1E001OperationsDpt2E002HRDpt3E003FinanceNow, Natural Join is applied on the above two tables −The result will be −Emp_IDEmp_NameEmp_AgeEmp_LocationDept_IDDept_NameE001Jacob29AlabamaDpt1OperationsE002Henry32AlabamaDpt2HRE003Tom22TexasDpt3FinanceTherefore, the above relation had lossless decomposition i.e. no loss of information.Lossy DecompositionAs ... Read More

A relationship can be One-to-Many or Many-to-One in DBMS. Let us see what that means with examples −One-to-Many RelationshipOne-to-Many relationship in DBMS is a relationship between instances of an entity with more than one instance of another entity.The relation can be shown as −Let us see an example −A student can work on more than one project. Student and Project are entities here. An individual student working on 2 projects at a time would be considered as One-to-Many relationship in DBMS as shown below:Many to One RelationshipMany-to-One relationship in DBMS is a relationship between more than one instances of an ... Read More

Dependencies in DBMS is a relation between two or more attributes. It has the following types in DBMS −Functional DependencyFully-Functional DependencyTransitive DependencyMultivalued DependencyPartial DependencyLet us start with Functional Dependency −Functional DependencyIf the information stored in a table can uniquely determine another information in the same table, then it is called Functional Dependency. Consider it as an association between two attributes of the same relation.If P functionally determines Q, thenP -> QLet us see an example −EmpIDEmpNameEmpAgeE01Amit28E02Rohit31In the above table, EmpName is functionally dependent on EmpID because EmpName can take only one value for the given value of EmpID:EmpID -> EmpNameThe same is displayed ... Read More

Identifying entity relationships in DBMS is used to identify a relationship between strong and weak entity.Before beginning, let us take an example of Professor entity, which is our Strong Entity, with Professor_ID as a Primary Key −Professor_IDProfessor_NameProfessor_CityProfessor_SalaryThe weak entity is Professor_Dependents entity:NameDOBRelationNow, let us continue, Weak EntityWeak Entity is dependent on Strong Entity and does not have a primary key. Weak Entity has a partial key. It is represented as double rectangle −Strong EntityOther entities are dependent on Strong Entity and it has a key attribute i.e. a primary key and represented as a single rectangle.Identifying RelationshipsIt links the strong and weak entity ... Read More

Database Normalization removes redundancy of data and anomalies in a poorly designed table. The process of normalization converts an unnormalized table in a normalized form.Let us see what flaws are in an Unnormalized table i.e. why Normalization of Database is so crucial −Redundancy of DataStoring same data item multiple times is known as Data Redundancy.Let us see an example −We have an table that has a field to store current address of Employees. Some of these employees are given a place to live by the company; therefore, they have the same address. The address will be repeated in the ... Read More

The history of data models had three generations of DBMS −Hierarchical System was the first generation of DBMS. The first generation also came with the CODASYL system. Both of them introduced in 1960s.The second generation includes the Relational Model. Dr. E.F.Codd introduced it in 1970.The third generation includes Object-Relational DBMS and Object-Oriented DBMS.The history timeline of databases is shown below −File based systemsFile based systems came in 1960s and was widely used. It stores information and organize it into storage devices like a hard disk, a CD-ROM, USB, SSD, floppy disk, etc.Relational ModelRelational Model introduced by E.F.Codd in 1969. The ... Read More

Entity in DBMS can be a real-world object with an existence, For example, in a Company database, the entities can be Employees, Department, Project, etc. In a College database, the entities are Profession, Students, Result, Activities, etc.An entity is represented as a single rectangle, shown belowThe following are the entity relationships −One-to-One RelationshipUnder One-to-One (1:1) relationship, an instance of entity P is related to instance of entity Q and an instance of entity Q is related to instance of entity P.Let us see an example −A person can have only one passport, and a passport is assigned to a single person.One-to-Many RelationshipUnder ... Read More

Entity in DBMS can be a real-world object with an existence, For example, in a College database, the entities can be Professor, Students, Courses, etc. Entities has attributes, which can be considered as properties describing it, for example, for Professor entity, the attributes are  Professor_Name, Professor_Address, Professor_Salary, etc. The attribute value gets stored in the database. Example of Entity in DBMS Let us see an example − Professor_ID Professor_Name Professor_City ... Read More