3.2 Developing ER diagrams
4 min read•august 6, 2024
- (ER) modeling is a crucial part of database design. It helps us visualize and organize data relationships. In this section, we'll learn how to create ER diagrams, which are visual representations of these relationships.
We'll cover the key components of ER models, including entities, attributes, and relationships. We'll also explore different notation styles like Chen and Crow's Foot, and dive into various relationship types. This knowledge will help you design effective database structures.
Entity-Relationship Model Components
Core Components of ER Models
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Top images from around the web for Core Components of ER Models
Class Diagram Example: ER View original
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Class Diagram Example: ER View original
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- Entity Set represents a collection of entities of the same type that share similar properties (Employee, Department, Project)
- Relationship Set defines associations or interactions between two or more entity sets (works_for, manages, assigned_to)
- Attributes describe the properties or characteristics of an entity or relationship set and can be of different types such as simple, composite, single-valued, or multi-valued (Employee: employee_id, name, address; Department: department_id, name, location)
- Key Attributes uniquely identify each entity instance in an entity set and can be a single or a combination of attributes (Employee: employee_id; Department: department_id)
Advanced ER Model Concepts
- Participation Constraint determines whether the existence of an entity depends on its relationship with another entity and can be either total participation or partial participation
- Total Participation means every entity instance in one entity set must be associated with at least one entity instance in the related entity set (every Employee must be assigned to a Department)
- Partial Participation means an entity instance in one entity set may or may not be associated with an entity instance in the related entity set (an Employee may or may not be assigned to a Project)
- Recursive Relationship occurs when an entity set participates in a relationship with itself, representing a hierarchical or network structure (an Employee can manage other Employees)
- Set depends on the existence of another entity set, called the identifying entity set, and cannot be uniquely identified by its own attributes (Dependent entity set relies on the Employee entity set)
- Weak entities are represented by double rectangles in ER diagrams
- Identifying relationship connects a weak entity set to its identifying entity set and is represented by a double
ER Diagram Notations
Chen Notation
- , introduced by Peter Chen in 1976, is a popular way to represent ER diagrams
- Entities are represented by rectangles
- Relationships are represented by diamonds
- Attributes are represented by ovals and are connected to their respective entities or relationships
- Cardinality is represented by placing numbers or symbols near the connection between entities and relationships (1:1, 1:N, M:N)
- Chen notation provides a clear and intuitive way to visualize the structure of a database schema
Crow's Foot Notation
- , also known as Information Engineering (IE) notation, is another widely used ER diagram notation
- Entities are represented by rectangles
- Relationships are represented by lines connecting the entities
- Cardinality is represented by symbols at the ends of the relationship lines
- A single line indicates a (1:1) relationship
- A crow's foot (three lines) indicates a (1:N) relationship
- A crow's foot at both ends indicates a (M:N) relationship
- Attributes are listed inside the entity rectangles
- Crow's Foot notation emphasizes the cardinality and directionality of relationships, making it easier to understand the data flow and dependencies
Relationship Types
Identifying Relationship
- An identifying relationship is a type of relationship where the child entity's primary key includes the primary key of the parent entity
- The child entity is considered a weak entity because its existence depends on the parent entity
- Identifying relationships are represented by solid lines in ER diagrams
- An example of an identifying relationship is the relationship between Order (parent) and OrderItem (child), where the primary key of OrderItem consists of the Order's primary key and an additional attribute (order_id, item_number)
Non-identifying Relationship
- A non-identifying relationship is a type of relationship where the child entity's primary key does not include the primary key of the parent entity
- The child entity is considered a because it can exist independently of the parent entity
- Non-identifying relationships are represented by dashed lines in ER diagrams
- An example of a non-identifying relationship is the relationship between Student and Course, where a Student can enroll in multiple Courses, but the primary key of the Student entity (student_id) is not included in the primary key of the Course entity (course_id)
Key Terms to Review (22)
1NF: First Normal Form (1NF) is a fundamental property of a relational database table that ensures that the table structure is free of duplicate rows and that each column contains atomic, indivisible values. This concept lays the groundwork for organizing data efficiently, ensuring each piece of data is stored in its simplest form, which is crucial for effective database design and management.
2NF: Second Normal Form (2NF) is a level of database normalization that aims to eliminate partial dependencies of attributes on a composite primary key. It builds upon First Normal Form (1NF) by ensuring that all non-key attributes are fully functionally dependent on the entire primary key, which helps to reduce redundancy and improve data integrity. This concept is crucial in creating efficient relational schemas, querying data effectively, and performing accurate aggregations.
3NF: Third Normal Form (3NF) is a database normalization level that aims to eliminate redundant data and ensure that all non-key attributes are fully functionally dependent on the primary key. In simpler terms, it means organizing a database in such a way that no information is repeated unnecessarily and that every piece of data relates directly to the key attribute. This structure not only promotes data integrity but also improves the efficiency of data retrieval and management.
Attribute: An attribute is a property or characteristic of an entity in a database that holds specific information. Attributes serve as the building blocks for data representation, defining the details of the data stored in tables and influencing how relationships among entities are structured.
Chen Notation: Chen Notation is a graphical representation used in entity-relationship (ER) modeling to visually describe data structures and relationships within a database. It utilizes specific symbols to represent entities, attributes, and relationships, making it easier to design and communicate complex database systems. The visual clarity of Chen Notation helps in effectively developing ER diagrams, enhancing the understanding of data requirements and supporting the translation of these diagrams into relational schemas.
Composite attribute: A composite attribute is an attribute in a database that can be divided into smaller sub-attributes, each of which represents a more detailed level of information. This type of attribute is useful for modeling complex data where a single piece of information can be broken down into its components, allowing for more granular representation of entities. In the context of advanced modeling, developing diagrams, translating schemas, and understanding ER components, composite attributes play a vital role in effectively organizing and structuring data.
Crow's Foot Notation: Crow's foot notation is a graphical representation used in Entity-Relationship (ER) modeling to illustrate the relationships between entities in a database. This notation uses symbols that resemble a crow's foot to indicate the cardinality of relationships, such as one-to-one, one-to-many, or many-to-many. Understanding this notation is essential for developing clear ER diagrams, translating them into relational schemas, and identifying keys and constraints within database structures.
Diamond: In the context of entity-relationship (ER) diagrams, a diamond represents a relationship between entities. Relationships are crucial for illustrating how different entities interact with one another within a database, capturing the essence of how data is connected and related in a meaningful way.
Entity: An entity is a distinct, identifiable object or concept that can have data stored about it within a database system. Entities can represent real-world objects like a person, place, or thing, and they play a critical role in modeling the relationships and attributes within a data structure. Understanding entities helps in developing ER diagrams, translating them into relational schemas, and mapping their relationships effectively.
Entity Integrity: Entity integrity is a fundamental principle in relational database design that ensures each entity, or row, in a table is uniquely identifiable and has a primary key that is not null. This principle maintains the uniqueness and validity of each record, establishing a clear framework for data retrieval and management, which is vital for maintaining accurate and consistent databases.
Iterative design: Iterative design is a process that involves continuously refining and improving a product or system through repeated cycles of design, testing, and evaluation. This approach allows designers to incorporate user feedback at each stage, ultimately leading to better usability and functionality. By embracing change and adapting designs based on real-world testing, iterative design promotes a more user-centered outcome.
Many-to-Many: A many-to-many relationship occurs when multiple records in one table can relate to multiple records in another table. This concept is vital for understanding the relational model, as it illustrates how data can be interlinked and queried through various relationships, leading to a more flexible and powerful database design.
Mapping requirements: Mapping requirements refers to the process of translating conceptual data models into a logical structure that can be implemented in a database. This involves detailing how entities, attributes, and relationships identified in an Entity-Relationship (ER) diagram correspond to the tables and columns in a relational database. Properly mapping requirements ensures that the data is accurately represented and efficiently organized for future use.
Multi-valued attribute: A multi-valued attribute is a type of attribute in a database that can hold multiple values for a single entity. This means that an entity can be associated with several values for that attribute, unlike single-valued attributes which can only hold one value. Multi-valued attributes are significant in entity-relationship (ER) diagrams as they help represent complex data relationships by indicating that an entity can have more than one piece of related information.
One-to-many: One-to-many is a type of relationship in database design where a single record in one table can be associated with multiple records in another table. This concept is fundamental in understanding how data is structured and organized, as it allows for efficient data modeling and retrieval. It plays a critical role in defining relationships between entities, ensuring data integrity, and enabling complex queries that can combine information from multiple tables.
One-to-One: One-to-one is a type of relationship in database design where each entity in a set is associated with exactly one entity in another set. This means that for any given instance of an entity, there is a unique corresponding instance in the related entity. Understanding this concept is crucial for correctly developing ER diagrams, translating them into relational schemas, and ensuring proper representation of the relationships within the data model.
Oval: In the context of developing ER diagrams, an oval is a shape used to represent attributes of entities and relationships. It visually distinguishes attributes from other elements, like entities and relationships, by providing a clear graphical representation that enhances the understanding of how data is organized within the system. Ovals help to outline characteristics or properties that are associated with entities or the connections between them, making the diagram more intuitive and easier to interpret.
Rectangle: In the context of ER diagrams, a rectangle represents an entity, which is a fundamental component that encapsulates real-world objects or concepts. Entities can be anything like a person, place, event, or thing that has data stored about it in a database. Each entity typically has attributes associated with it, which describe the properties or characteristics of that entity.
Referential Integrity: Referential integrity is a database concept that ensures relationships between tables remain consistent and valid. It requires that any foreign key value in one table must either match an existing primary key value in another table or be null, thereby preventing orphaned records and maintaining the accuracy of data across related tables.
Relationship: In database design, a relationship refers to the association between two or more entities that defines how they are connected and interact with one another. Relationships are essential for modeling data accurately, as they help to establish how different entities relate, which is crucial when developing Entity-Relationship (ER) diagrams and later translating them into relational schemas.
Strong entity: A strong entity is an object or concept that can exist independently in a database and is characterized by having a primary key that uniquely identifies each instance of the entity. In the context of database design, strong entities are fundamental components that contribute to the structure of data models, making them essential for creating clear and effective Entity-Relationship (ER) diagrams and ensuring accurate representation of real-world scenarios.
Weak entity: A weak entity is a type of entity in a database that cannot be uniquely identified by its own attributes alone and relies on a foreign key relationship with another entity, known as a strong entity, to form a composite key. This relationship typically involves a strong entity that acts as the owner or parent, allowing the weak entity to maintain its existence and be recognized within the database. Weak entities are represented in ER diagrams with a double rectangle and their relationship with strong entities is shown using a double diamond.