Activity #15: Research Cardinality in Entity-Relationship Diagrams

Research Cardinality in ERDs:

Understanding Cardinality

Cardinality in ERDs defines the relationship between two entities, indicating how many instances of one entity can or must be associated with instances of another entity. It plays a crucial role in database design, ensuring accurate modeling of real-world relationships between data tables.

Types of Cardinality

1. One-to-One (1:1)

   • Definition: One instance of an entity is related to exactly one instance of another entity.

   • Example: A person has one passport, and each passport belongs to one person.

   • ER Diagram Representation: A straight line between the entities with a "1" near both ends.

2. One-to-Many (1

   )

   • Definition: One instance of an entity is related to many instances of another entity, but those many instances are related to only one instance of the first entity.

   • Example: A department can have multiple employees, but each employee belongs to only one department.

   • ER Diagram Representation: A line connecting the entities, with "1" near the one side and "N" (or "many") near the other side.

3. Many-to-Many (M

   )

   • Definition: Many instances of one entity can be related to many instances of another entity.

   • Example: Students can be enrolled in many courses, and each course can have multiple students.

   • ER Diagram Representation: Lines connecting the two entities with "M" and "N" or "many" indicators. Often, a junction table (or associative entity) is used to represent this relationship in a database.

Cardinality Representation in ERDs

In the diagrams you uploaded, cardinality relationships can typically be identified by analyzing the links between entities, the use of foreign keys, and labels on the relationship lines. Here are some points related to how cardinality might be represented in the given ERD images:

1. Foreign Keys: Foreign keys in a table imply relationships between entities. For instance, a foreign key in a student table linking to a department table indicates a one-to-many relationship, where multiple students can belong to a single department.

2. One-to-Many Relationships: In the provided ERD, relationships like department_id in the Students table imply a one-to-many relationship (i.e., one department has many students).

3. Many-to-Many Relationships: These are often handled using a join table, as seen in the Courses_Students table. This table will have foreign keys referring to both students and courses, enabling the representation of many-to-many relationships between these two entities.

• Example: The relationship between "Scholarship" and "Student" is mandatory. Each student must be associated with a scholarship.

ERD Diagram Example

The ERD in the provided image demonstrates various cardinality relationships

• One-to-One: "Student" and "Address"

• One-to-Many: "Course" and "Student", "Department" and "Student", "Project" and "Student"

• Many-to-Many: "Project" and "Student" (implemented using the "Project_Student" junction table)

• Optional: "Department" and "Student"

• Mandatory: "Scholarship" and "Student"

By understanding cardinality, you can effectively model real-world relationships in your database, ensuring data integrity and efficiency.

Denormalization in Database Design

The image illustrates the concept of denormalization in database design. Denormalization involves combining data from multiple tables into a single table to improve performance and simplify queries. It's often used when read operations are more frequent than write operations.

Comparison of Tables:

• stu_info: This table appears to be normalized, containing attributes related to student information. Each row represents a unique student, and each column represents a specific attribute.

• STUDENT: This table appears to be denormalized. It combines attributes from multiple tables, likely including "stu_info" and other tables related to student data. This approach might be chosen for faster data retrieval, as all student information is readily available in a single table.

Advantages of Denormalization:

• Improved Performance: By reducing the number of joins required for queries, denormalization can speed up data retrieval.

• Simplified Queries: Denormalized tables make queries easier to write and understand, as all the necessary data is in one place.

Disadvantages of Denormalization:

• Data Redundancy: Denormalization can lead to redundant data, potentially causing inconsistencies if data is not updated consistently across all tables.

• Data Integrity Issues: Maintaining data integrity can be more challenging in denormalized tables, as changes in one table might require updates in multiple places.

Example:

The "STUDENT" table likely combines attributes from "stu_info" and other tables. For instance, it might include the student's name, address, phone number, and other details from "stu_info" along with additional information like their major, GPA, or courses taken from other tables.

Denormalization is a trade-off between performance and data integrity. It can improve performance by reducing joins and simplifying queries but can also lead to data redundancy and integrity issues. The decision of whether to denormalize depends on the specific needs of the application and the trade-offs involved.