Multivalued Dependency in DBMS: A Complete Guide

Designing databases effectively isn’t just about reducing redundancy—it’s about ensuring the data is accurate and easy to maintain as the system grows. When databases become more complex, managing how data relates to other data becomes a bigger challenge. A key factor in tackling this complexity is Multivalued Dependency (MVD). It helps improve database design at higher levels of normalization, especially when trying to avoid redundant data entries. In this article, we’ll explore what MVD is, why it matters for your database design, and how addressing it can improve overall data handling and performance.

Functional Dependency

Functional dependencies are usually resolved in the earlier stages of normalization, often in the Third Normal Form (3NF) or the Boyce-Codd Normal Form (BCNF). However, when it comes to more complex scenarios, like handling independent sets of values within the same table, multivalued dependencies come into play. Let’s dive deeper into how Multivalued Dependency works in a database management system (DBMS). For instance, knowing an EmployeeID lets us determine the EmployeeName, as shown here:

EmployeeID —>EmployeeName

A multivalued dependency exists in a relation when, for every value of one attribute (say A), there is a set of values for another attribute (B) that remains independent of the other attributes in the table. To put it simply, knowing the value of A gives us a set of B values, and these values are consistent regardless of any other information in the same row. This dependency can be formally expressed as:

A —> B

Also Read:  Data Redundancy in DBMS

Example of Multivalued Dependency

Consider a relation Employee Projects Certifications that tracks which projects an employee is working on and the certifications they possess:

In this table, the employee E101 is working on two projects: Project X and Project Y, and the employee has two certifications including Java Certified and AWS Certified. All the projects E101 is working on are completely independent of the certifications he holds. This means that the certification is not determined by the project that you are assigned to. This leads to the following conclusion:

EmployeeID —> Certification

The data of certification is being duplicated for each of the projects that E101 processes, resulting in data redundancy. This justifies the dependency as the certifications are dissociated from the project information, but linked with the same employee.

Also Read: DBMS Tutorial

Challenges of Multivalued Dependency in Database Design

This analysis shows that multivalued dependencies can be very problematic when working with large databases. Some of the key problems caused by unresolved MVDs include:

●      Data Redundancy

As shown in the Differentiating Fields through the case of Employee Projects Certifications example, the use of Multivalued Dependencies results in the replication of data. This is especially true where identification is repeated for each project, with the certifications of an employee duplicated.

●      Update Anomalies

This is a problem of dB and if redundant data exists, change in one instance will be required in every other instance. For instance, new qualifications earned by E101 would require that new information be included for every project. Failure to update all such instances may cause the system to develop some level of ‘inconsistencies’.

●      Insert Anomalies

If there is a new project addition to E101 then completion of certification details to that particular project is necessitated. It also makes the data susceptible to experiencing certain difficulties at the time of insertion.

●      Delete Anomalies

While deleting a record may occur there is a high probability that some essential details may be lost inadvertently. For example, if we exclude E101 out of Project X then we may not get details of the certification of E101 also.

To avoid such issues, multivalued dependencies must be dealt with during the initial database design process which usually involves partitioning the relation into other less complex ones.

Also Read: Normal Forms in DBMS

How to Resolve Multivalued Dependencies with Fourth Normal Form (4NF)

To deal with multivalued dependencies databases need to be normalized to Fourth normal form or 4 NF. A relation is in 4NF if it is 3NF and has no multivalued dependencies for which a proper subset of the determinant is also a proper subset of the set of attributes determined by the dependency. In 4NF, multivalued dependencies are removed by breaking the relation into lesser relations where these dependent sets of values are separated.

Changing the Relation to 4NF

We can decompose the relation into two separate tables to eliminate the multivalued dependency:

EmployeeProjects:

EmployeeCertifications:

This gets rid of the multivalued dependency problem that arises when the first table is split into two tables each having fewer fields. Today, there is no project or certification information in the EmployeeProjects table, as it only holds information on employee projects; the EmployeeCertifications table also holds only certification data. One set of data is distinct from the other, thus, it is not necessary to display certification information for each project.

Multivalued Dependency vs. Functional Dependency

Although both multivalued and functional dependencies define relationships between attributes in a relation, they differ significantly in terms of complexity and the kinds of issues they address:

• Functional Dependency (FD) is defined as one attribute providing information about another attribute in a unique way. For instance, if you know an employee’s ID it’s possible to look up their name.
• Multivalued Dependency (MVD) occurs when one attribute determines a set of values for another attribute without influence from other attributes in the relation. It is more complex because it involves sets of values and concerns with other concepts of database management that have to do with data redundancy and cannot be handled solely by eradicating functional dependencies.

MVDs usually appear in situations where more than one relationship type has to be defined, for example, employee–project, and employee–certification.

Advantages of Resolving Multivalued Dependencies

• Reduction of Redundancy: Through table decomposition and MVD resolution, we eliminate redundancy which makes our storage system more efficient in its data management.
• Improved Data Integrity: Closing an MVD means that changes are made once which decreases the chance of having unreliable information.
• Easier Maintenance: Whereas, with MVDs resolved, DBA can simply work with data and do not have to worry about unnecessarily duplicated or conflicting data placed in different records.
• Better Query Performance: This results in enhancing query performance since the database frees up more space from data that may not be commonly used during search, insert, and update.

Also Read: Functional Dependency in DBMS

Practical Applications of Multivalued Dependencies

Multivalued dependencies have some significance wherever there is more than one independent association that an entity may engross with other entities. Some examples include:

• Employee Skills and Projects: Employees may work on several projects; each employee has skills, but the skills are not related to the projects.
• Student Courses and Extracurricular Activities: A student may take several courses and engage in different activities if activities are separate from courses.
• Product Categories and Suppliers: More than one category of a product is possible with the same product being supplied by more than one vendor and with no relation between the categories and suppliers.

Also Read: DBMS Interview Questions With Answers

Conclusion

In short, resolving multivalued dependencies leads to better data organization, reduced redundancy, and improved database performance. As databases continue to scale, handling MVDs becomes crucial for maintaining data integrity and efficiency. By applying normalization techniques like 4NF, database designers can ensure that their systems are optimized for both storage and performance. When applied correctly, dependency theory and normalization techniques provide a path for database designers to build databases that facilitate storage of data but also maintain, have accuracy, and good performance. Want to explore DBMS in-depth? Try Hero Vired’s Accelerator Program in Business Analytics and Data Science.