A multivalued dependency (MVD) is a concept in database theory that describes a relationship between attributes in a relational database. It is an extension of the functional dependency (FD) concept and deals with dependencies among sets of attributes rather than individual attributes.
In a relational database, an MVD occurs when a set of attributes (X) determines a set of attributes (Y), but there can be multiple possible values for another set of attributes (Z) that are independent of both X and Y. In other words, for each combination of values in X, there can be multiple combinations of values in Z associated with it, which in turn determines multiple combinations of values in Y.
MVDs are denoted as X ->-> Y, where X, Y, and Z are sets of attributes. This notation indicates that X determines Y, but there can be multiple possible combinations of values in Z associated with each combination of values in X.
MVDs are primarily used to identify certain types of redundancies in a relational database. By detecting and eliminating these redundancies, we can improve the efficiency of storage and query processing.
It’s worth noting that MVDs are less commonly used and studied compared to functional dependencies, and they are not supported by the traditional relational model. However, they have been investigated in the context of database normalization and database design.
Example of Multivalued Dependency:
Sure! Let’s consider a simplified example to demonstrate a multivalued dependency.
Suppose we have a relation/table called “Employee” with the following attributes: Employee_ID, Employee_Name, and Skills. The “Skills” attribute represents the various skills possessed by an employee, and it can contain multiple values.
Employee Table:
| Employee_ID | Employee_Name | Skills |
|---|---|---|
| 1 | John | Java, Python |
| 2 | Alice | C++, Python |
| 3 | Mike | Java, SQL |
In this scenario, we can observe that there is a multivalued dependency between the attributes Employee_Name and Skills. Let’s break it down:
If we consider the attribute set X = {Employee_Name}, and attribute set Y = {Skills}, we can see that for each employee name (X), there can be multiple combinations of skills (Y). For example, John can have skills in Java and Python, while Alice can have skills in C++ and Python.
However, there might exist another set of attributes Z = {Employee_ID} that is independent of both X and Y. The employee IDs do not have any influence on the possible combinations of skills.
So, in this case, the multivalued dependency can be represented as Employee_Name ->-> Skills, indicating that the employee name determines the set of skills, but there can be multiple possible combinations of skills associated with each employee name.
Identifying such dependencies can be useful in database design and normalization, as it helps to eliminate redundancies and ensure data consistency.