Explain the concept of multivalued dependency in the context of database normalization.

In the context of database normalization, multivalued dependency refers to a situation where a relationship between two sets of attributes exists in a table, and the values of one set of attributes determine the values of another set of attributes, but not vice versa.

To understand multivalued dependency, let's consider an example. Suppose we have a table called "Employees" with the following attributes: EmployeeID, EmployeeName, and Skills. In this table, each employee can have multiple skills, and each skill can be possessed by multiple employees.

Now, let's say we have the following data in the Employees table:

EmployeeID | EmployeeName | Skills
-----------|--------------|-------
1 | John | Java, SQL
2 | Jane | C++, Python
3 | Mark | Java, Python

In this example, we can observe that the Skills attribute is multivalued, as it can have multiple values (skills) for each employee.

Multivalued dependency occurs when there is a functional dependency between two sets of attributes, where the values of one set determine the values of another set, but not vice versa. In our example, the EmployeeID determines the EmployeeName, and the Skills determine the EmployeeID. However, the EmployeeID does not determine the Skills, and the Skills do not determine the EmployeeName.

To normalize the table and eliminate multivalued dependency, we can create a separate table called "EmployeeSkills" with the attributes EmployeeID and Skill. This new table will have a composite primary key consisting of both attributes.

The normalized tables would look like this:

Employees table:
EmployeeID | EmployeeName
-----------|--------------
1 | John
2 | Jane
3 | Mark

EmployeeSkills table:
EmployeeID | Skill
-----------|------
1 | Java
1 | SQL
2 | C++
2 | Python
3 | Java
3 | Python

By splitting the multivalued attribute into a separate table, we ensure that each attribute in a table depends only on the primary key and not on any other non-key attributes. This helps in reducing data redundancy, improving data integrity, and making the database more efficient.