Lesson 11

This module discussed how business objects and their characteristics are converted into entities and attributes. This is in preparation for creating an ER diagram. You also learned that multi-valued attributes violate the single-value rule for relational databases, and then learned two approaches to resolving multi-valued attributes. Finally, you learned about constraints that can be placed on attributes and entity relationships to help ensure data integrity.

In relational databases, the single-value rule is a fundamental principle that enforces data consistency, accuracy, and normalization. This rule stipulates that each column in a table must contain only a single, atomic value, thereby preventing the storage of complex or multi-valued data in a single cell. By adhering to the single-value rule, databases maintain a clear, structured organization of information, which simplifies data manipulation, querying, and reporting. This article will provide an overview of the single-value rule and its significance in relational database management.

1. The Basis of the Single-Value Rule: The single-value rule is rooted in the concept of first normal form (1NF), which is a crucial step in the normalization process for relational databases. Normalization is a technique used to minimize data redundancy, eliminate anomalies, and ensure the efficient organization of data. First normal form dictates that each column in a table must contain only atomic values – that is, values that cannot be further decomposed. Consequently, the single-value rule prohibits the use of arrays, lists, or nested structures within individual cells.
2. Benefits of the Single-Value Rule: Adherence to the single-value rule offers several advantages in the context of relational databases:
   
   1. Data Consistency: By ensuring that each column contains only atomic values, the single-value rule promotes data consistency and facilitates accurate querying and reporting.
   2. Data Integrity: Atomic values within columns make it easier to enforce data integrity constraints, such as primary key, foreign key, and unique constraints, which maintain the accuracy and reliability of the database.
   3. Simplified Data Manipulation: The single-value rule enables more straightforward data manipulation, as SQL and other query languages are designed to work efficiently with atomic values.
   4. Improved Performance: A database structured according to the single-value rule typically exhibits better performance, as normalized tables reduce redundancy and require fewer resources for storage and processing.
3. Implementing the Single-Value Rule: To comply with the single-value rule, database designers should take the following steps:
   
   1. dentify Multi-Valued Attributes: Analyze the data model to pinpoint any columns that may contain complex or multi-valued data.
   2. Normalize the Data: Decompose multi-valued attributes into separate, related tables with atomic values, thereby adhering to first normal form.
   3. Establish Relationships: Define primary key and foreign key relationships between the newly created tables, ensuring referential integrity and facilitating data retrieval.

In conclusion, the single-value rule is an essential principle in relational databases that enforces data consistency, integrity, and normalization by ensuring that each column contains only atomic values. By adhering to this rule, database designers can create efficient, organized, and high-performing databases that simplify data manipulation and querying while maintaining the accuracy and reliability of the stored information.

Having completed this module, you should be able to:

1. Define entities and entity attributes
2. Explain the purpose of entity identifiers
3. List rules for creating entity identifiers
4. Describe instances of entities
5. Describe attribute domains and domain types
6. Explain the problem with multi-valued attributes
7. Describe how to resolve multi-valued attributes by adding more attributes
8. Describe how to resolve multi-valued attributes by creating a new entity
9. List entity and attribute constraints

This module introduced you to the following terms:

1. BLOB domain type: Short for “binary large object” ; a domain type used to store binary objects such as graphics in a relational database.
2. Boolean expression: An expression that results in a value of either TRUE or FALSE. Boolean expressions are also called coparison expressions, conditional expressions, and relational expressions.
3. conceptual model: A description of the structure of a database.
4. data value: Data entered at the intersection of a row (record) and column (field); the data describes or identifies the subject of the record.
5. Data integrity: A term used to describe the quality (in terms of accuracy, consistency, and validity) of data in a database, in the sense that values required to enforce data relationships actually exist. Problems with data integrity occur when a value in one table that’s supposed to relate to a value in another can’t, because the second value either has been deleted or was never entered.
6. domain:Determines the type of data values that are permitted for that attribute.
7. domain constraints: Rules that require values of attributes to come from specific domains (e.g., text, numbers, date, etc.).
8. entity: A single stand-alone unit or a business object about which data are stored in a database; usually synonymous with a database table.
9. instance of an entity:The equivalent of a row (record) in a database with values entered for the entity's attributes.
10. key attribute: An attribute that identifies an entity; an entity identifier.
11. multi-valued attribute: An attribute with more than one data value; must be resolved into a single-valued attribute.
12. non-key attribute: An attribute that describes an entity.
13. requirements analysis: The stage in the database design cycle when designers find out everything they can about the data the client needs to store in the database and the conditions under which that data needs to be accessed.
14. single-valued attribute: An attribute with more than one data value; must be resolved into a single-valued attribute.

The next module discusses entity relationships.

Before moving on to the next module, click the Quiz link below to check your understanding of entities and attributes.
Entities Attributes - Quiz