Question

Discuss the correspondences between the ER model constructs and the relational model constructs. Show how each ER model construct can be mapped to the rela. tional model, and discuss any alternative mappings.

Short answer

In the ER model, entities map to tables in the relational model, and attributes map to columns. Relationships in the ER model can be translated into separate tables in the relational model by joining primary keys of the associated entities. Complex attributes are usually represented as separate tables. Alternative mappings might include using null values to represent optional relationships or moving relationship attributes to one of the related entities to simplify the model.

Step-by-step solution

Understanding ER and Relational Models

The ER model represents the conceptual design of database and includes entities ('things' about which data is being captured), attributes (properties of entities) and relationships (associations between entities). Conversely, the relational model represents the logical structure of the database, using tables (or relations) to represent data, and these tables are linked through primary and foreign keys.

Mapping Entities to Tables

As the first step of mapping, each entity in the ER model is converted into a table in the relational model. The attributes of the entity in the ER model are transformed into columns of the table in the relational model. An additional column for unique identification, known as the Primary Key, is also added.

Mapping Relationships to Tables

Next, each relationship in the ER model is mapped to a table in the relational model. To facilitate this, the primary keys from each related entity are combined to form a new table in the relational model. Each row in the relationship table represents an association between the two entities. Additionally, any attributes of the relationship are also added as columns in this table.

Mapping Complex Attributes

Complex attributes, or attributes composed of other attributes, are mapped by creating a separate table for the complex attribute. The primary key of the original entity is used as the primary key of the new table.

Discussing Alternative Mappings

There may be cases where alternative mappings are possible. For instance, in case of optional relationships where entities may exist without a relationship, a null value could be used to represent absence of relationship rather than creating a separate table. Also, if an attribute of a relationship can be comfortably moved to one of the related entities, it could simplify the relational model.

Key concepts

Conceptual Design of Database

Creating a database starts with a high-level design known as the conceptual design. It's the phase where the ER (Entity-Relationship) model is built. This model visually represents the database and helps in understanding the scope and boundaries of data. It includes three main components:

• Entities: These are objects or 'things' in the real world with independent existence that are represented in the database. Examples include 'Customer', 'Order', etc.
• Attributes: These are properties or characteristics of entities, such as 'Name' or 'Customer ID'.
• Relationships: These define how two or more entities are connected. For example, a 'Customer' places an 'Order'.

During the conceptual design phase, one focuses on identifying what data needs to be captured and how the data points relate to one another. It's a crucial step before moving to the next phase, which is the logical structure of the database. A well-thought conceptual design ensures efficient database performance and fulfills the data requirements of an application.

Logical Structure of Database

After the conceptual design, the logical structure of the database is defined using the relational model. Here, data is organized in terms of tables, also known as relations.

• Tables: Each table corresponds to an entity or a relationship from the ER model. Each table contains rows, known as records, and columns, which represent attributes.
• Schema: This defines the structure of a table, including the table name, column names, and types.

The transformation from conceptual design to logical structure involves mapping each entity and its attributes to a table and columns, respectively. Relationships between entities are also represented in tables, allowing for data to be easily queried and maintained. Emphasizing a well-structured logical design ensures data integrity and consistency throughout the database.

Primary and Foreign Keys

In the relational model, primary and foreign keys are vital in maintaining data integrity and establishing links between tables.

• Primary Key: This is a unique identifier for each record in a table. It ensures that no two records have the same primary key value. For instance, 'Customer ID' might serve as a primary key in a Customers table.
• Foreign Key: This key is used to link tables together. It is a column or a set of columns in one table that uniquely identifies a row in another table. For example, if the 'Order' table has a 'Customer ID' as a foreign key, it links back to the primary key of the 'Customer' table.

The use of primary and foreign keys is fundamental in structuring a relational database, as they enable the creation of relationships between different tables, allowing for a coherent data model and ensuring that data remains consistent and non-redundant. Proper utilization of these keys minimizes duplication and errors in database management.

Complex Attributes Mapping

Complex attributes, in an ER model, are those that can be divided into smaller sub-attributes. For instance, an 'Address' might be broken down into street, city, state, and postal code. These can be collectively considered a complex attribute.
When converting these into the relational model, it's common to create a new table to handle the complexity. Here's why:-

• Breaking down hierarchical data: This allows for each component of the complex attribute to be stored independently, which aids precise querying and manipulation.
• Avoids redundancy: It avoids duplication of complex data across multiple records by centralizing the complex attribute in its own table.

The primary key of the original entity's table is often used as a foreign key in the new table created for complex attributes. This form of mapping ensures the logical structure remains clear, making complex data relationships easier to maintain and understand in the relational database.