An Entity-relationship model is a conceptual model of the data that matter for a system or for an application domain. It is often abbreviated as ER Model. By using the ER Model, the types of things that must be represented become clear, along with their attributes and the links between them. Therefore, the model creates a clear picture of the data needed to satisfy requirements.
I model entity types as the main objects. For example, I model “Customer” and “Order” as entity types. I describe each entity type with attributes. For instance, I list attributes such as name, ID, and date. I also mark a primary key to identify each entity instance uniquely. Moreover, I include weak entities when they depend on other entities.
Next, I model relationships between entity types. I show how entities relate. For example, I show that a Customer places an Order. I state cardinalities explicitly. Therefore, I record whether a relationship is one-to-one, one-to-many, or many-to-many. In addition, I add attributes to relationships when they carry data, such as the date a relationship began.
To represent the model visually, ER diagrams are drawn. Boxes show entity types, while lines indicate relationships. Attributes sit near their corresponding entity types. In addition, multiplicity markers appear on the lines. Thus, colleagues can read the model quickly. Furthermore, diagrams can be supplemented with textual notes. In practice, combining visuals and text improves clarity.
I apply the ER Model during requirements engineering. First, I analyze the domain to find important entities. Then, I refine attributes and relationships. As a result, I document data requirements early. Because I work at a conceptual level, I avoid implementation details. Consequently, I can use the same model for database design and for discussions with stakeholders.
I map the ER Model to a logical schema when I move toward implementation. For instance, I convert entity types to tables and attributes to columns in a relational schema. Similarly, I transform many-to-many relationships into join tables. Therefore, the ER Model serves as a bridge between requirements and design.
However, I note limitations. The ER Model focuses on data. Thus, it does not model behavior, user interactions, or business rules fully. For example, I model a transaction as data, but I do not describe the steps to process that transaction. Therefore, I often combine ER models with other modeling languages, such as UML activity or use-case diagrams.
In addition, I keep models readable by choosing the right level of abstraction. First, I create a high-level conceptual model for stakeholders. Next, I create a detailed logical model for developers. Likewise, I create views to limit complexity. For example, I show only customer-related entities on one diagram and only product-related entities on another.
Finally, I follow modeling language rules for syntax and meaning. Thus, I avoid ambiguous constructs. I validate the ER Model with stakeholders. Consequently, I increase the model’s usefulness. In short, I use the ER Model to make data requirements explicit, traceable, and ready for design.