Question

What is meant by the attribute preservation condition on a decomposition?

Short answer

The attribute preservation condition on a decomposition ensures all attributes of the original relation are preserved in the decomposed relations, avoiding information loss and allowing for the original relation to be reconstructed if needed.

Step-by-step solution

Definition of Attribute Preservation in Decomposition

Attribute preservation in decomposition refers to the requirement that each attribute of the initial un-normalized relation should be preserved in at least one of the decomposed relations.

Importance of Attribute Preservation

This condition is important because it ensures that no necessary data is lost during the decomposition process. If an attribute were not preserved, it would mean that there could be a loss of information, making it impossible to reconstruct the original relation from the decomposed ones.

Examples of Attribute Preservation

For example, if we have a relation R(A, B, C) and we decompose it into R1(A, B) and R2(B, C), then the attribute preservation condition is met, as all attributes A, B and C of the original relation R appear in at least one of R1 or R2.

Key concepts

Database Normalization

Database normalization is a systematic approach of organizing the fields and tables of a relational database to reduce redundancy and improve data integrity. The ultimate goal of normalization is to isolate data so that additions, deletions, and modifications of a field can be made in just one table and then propagated through the rest of the database via the defined relationships.

Normalization involves dividing large tables into smaller, more manageable pieces according to a set of normalization rules, known as normal forms. These forms, ranging from the first normal form (1NF) through the fifth normal form (5NF), each tackle different types of anomalies and issues to ensure that the data within the databases is clean, well-structured, and efficient.

For students tackling database design, understanding normalization is crucial as it underpins many decisions in structuring data effectively. It ensures that the database supports the needs of the users while minimizing duplication, thus streamlining the data update processes.

Relation Decomposition

Relation decomposition is a strategy used in database normalization to divide a relation with potential problems, like redundancy or update anomalies, into two or more well-structured relations that collectively satisfy a desirable normal form. Decomposition must achieve two main objectives: The resulting relations should have less redundancy, and they should preserve all information as well as the integrity of the database.

Lossless-Join Decomposition

Essentially, for a decomposition to be lossless, you should be able to join the decomposed relations back to retrieve the original relation without losing any information or adding spurious tuples.

To refine database designs through decomposition, practitioners often apply a set of algorithms that automatically split problematic relations into several well-defined ones. It is a balance between enhancing structure without losing sight of inter-relationships between the data items in the process.

Data Integrity

Data integrity refers to the accuracy, consistency, and reliability of data throughout its lifecycle. It is a critical aspect of database management as it ensures that the information stored is correct and can be trusted for various operations, from business decisions to application functions. Data integrity encompasses a range of measures and constraints, including:

• Entity Integrity: Ensuring that each record (or row) in a table is unique and identifiable.
• Referential Integrity: Verifying that relationships between tables remain consistent, meaning that foreign keys match primary keys in related tables.
• Domain Integrity: Enforcing valid entries for a given column by restricting the type, format, or range of possible values.
• User-Defined Integrity: Applying specific business rules to the data as required.

By maintaining high data integrity, databases not only preserve the quality of the data but also defend against unintended data corruption, thereby ensuring that users can depend on the information for their various needs.