Database Management Systems (DBMS)

Dependency preserving decomposition is a concept in database normalization that ensures all functional dependencies remain enforceable even after splitting a relation into multiple sub-relations. By applying dependency preserving decomposition, you can maintain the integrity, constraints, and intended behavior of your database without losing the ability to validate certain data conditions directly on the decomposed tables. […]

Lossless Join Decomposition in DBMS is a technique that ensures when a database table is decomposed into multiple tables, you can still retrieve the original table’s data without losing information. By achieving lossless join decomposition in DBMS, you maintain the integrity and completeness of your data even as you break down large relations into smaller,

Canonical cover in DBMS is an essential concept for organizing and refining the set of functional dependencies within a database schema. By determining the canonical cover in DBMS, you reduce the complexity of functional dependencies, making them simpler, more intuitive, and easier to manage. This refined set of dependencies helps you identify candidate keys, normalize

Minimal cover in DBMS is a concept that helps database designers simplify and optimize the set of functional dependencies in a relational database schema. By determining the minimal cover in DBMS, you reduce complex functional dependencies into their most streamlined form. This ensures efficient normalization, fewer anomalies, and a clearer understanding of how attributes relate

Closure in DBMS is a concept that helps you understand how attributes within a database relate to one another. By finding the closure in DBMS for a given set of attributes, you can determine what other attributes can be functionally derived from them. This knowledge is essential when working with functional dependencies, normalizing databases, and