Lecture 2 - Database Management System.pptx
- 1. Lecture # 2 Database Management Systems By: Kainat Azmat Ullah
- 2. Data Independence It refers to the ability to change the schema of a database at one level (e.g., the internal or external level) without having to change the schema at the other levels. There are two main types of data independence: logical data independence and physical data independence.
- 3. Importance of Data Independence: Scalability: Making it easier to scale the database system by abstracting changes in physical storage and logical structure. Maintenance: Reducing the need for reworking applications when database changes are made. Adaptability: Easing the migration to newer database technologies or hardware without affecting user access. Data Redundancy: Storing the same data in multiple places. While data independence aims to reduce redundancy in the schema, redundancy is often a challenge when making changes to the database schema. How proper data independence can reduce redundancy and improve data consistency.
- 4. Data Independence in Distributed Databases Data independence becomes even more important in distributed databases, where data is stored across multiple locations. Ensuring that changes in one part of the database don't affect the global view or application logic.
- 5. Types of Data Independence Physical Data Independence: The ability to change the physical storage of the data without impacting the logical schema. Examples: Changing file formats, storage devices, or indexing mechanisms. Key challenges: Storage and performance optimization.
- 6. Logical Data Independence: The ability to change the logical schema without impacting the external schema or application programs. Examples: Adding new fields, changing data types, or re-organizing relationships between entities. Key challenges: Ensuring the change does not affect user views or applications relying on the data.
- 8. Relational Data Model The Relational Data Model is one of the most widely used and well-known data models for structuring and managing data in database management systems (DBMS). It is based on the concept of representing data in terms of tables (relations), which are related to each other through keys and constraints.
- 9. Key Concepts of the Relational Data Model Relation (Table): A relation is essentially a table with rows and columns. A relation consists of a set of tuples (rows) and a set of attributes (columns). Each table represents an entity or a relationship between entities. Attribute: An attribute is a column in the table, which represents a property or characteristic of the entity that the table models. Example: In a "Student" table, attributes might include Student_ID, First_Name, Last_Name, DOB (Date of Birth), etc.
- 10. Tuple: A tuple is a row in the table, which contains a specific value for each attribute. Example: A tuple in the "Student" table could be a record like: (123, "John", "Doe", "2000-05-15"). Domain: The domain of an attribute defines the set of permissible values for that attribute. Example: The domain of the attribute DOB might be a set of valid date values. Primary Key: A primary key is a set of one or more attributes that uniquely identify a tuple in a relation (table). Example: In a "Student" table, Student_ID could be the primary key, as it uniquely identifies each student.
- 11. Foreign Key: A foreign key is an attribute or set of attributes in one table that refers to the primary key in another table. It establishes a relationship between two tables. Example: In a "Course_Enrollment" table, Student_ID might be a foreign key that references the Student_ID in the "Student" table. Referential Integrity: Referential integrity ensures that a foreign key value in a table must either be null or match a primary key value in the referenced table. This prevents orphaned records and ensures the integrity of relationships between tables.
- 12. Relations and Keys: Superkey: A set of one or more attributes that can uniquely identify a tuple in a relation. Every relation must have at least one superkey. Candidate Key: A minimal superkey, i.e., a superkey where no attribute can be removed without losing the ability to uniquely identify a tuple.
- 13. Primary Key: A candidate key chosen to uniquely identify tuples in the table. Alternate Keys: Other candidate keys that are not selected as the primary key. Composite Key: A primary key that consists of two or more attributes.
- 15. Advantages of the Relational Model Simplicity: The relational model is easy to understand and use, with tables (relations) being a familiar concept. Flexibility: It is flexible in handling data, as it allows easy modifications such as adding new attributes or tables. Data Integrity: The relational model enforces constraints (e.g., primary key, foreign key) that help maintain data integrity. Support for Query Languages: SQL, a powerful and widely adopted query language, is built around the relational model. Normalization: The relational model supports normalization techniques to reduce redundancy and improve data integrity.
- 16. Anomalies in Relational Model Insertion Anomalies: It is the inability to insert data in the database due to the absence of other data. For example: Suppose we are dividing the whole class into groups for a project and the GroupNumber attribute is defined so that null values are not allowed. If a new student is admitted to the class but not immediately assigned to a group then this student can't be inserted into the database.
- 17. Modification/Update Anomalies – It is the data inconsistency that arises from data redundancy and partial updation of data in the database. For example: Suppose, while updating the data into the database duplicate entries were entered. Now, if the user does not realize that the data is stored redundantly after updation, there will be data inconsistency in the database.
- 18. Deletion Anomalies - It is the accidental loss of data in the database upon deletion of any other data element. For example: Suppose, we have an employee relation that contains the details of the employee along with the department they are working in. Now, if a department has one employee working in it and we remove the information of this employee from the table, there will be a loss of data related to the department also. This can lead to data inconsistency.