The main types of scientific model are visual, mathematical, and computer models.

Procedure: Scale Model of Relative Diameters of Planets

Scientists use models to investigate objects or processes that happen too slowly, too quickly, or on too small of a scale to observe directly. includes two diagrams that model the relative scale and emptiness of the Solar System.

Lesson Summary A physical model is a constructed copy of an object that is designed to represent that object. This can be a scale model, which is different in size to the real thing but has all the same proportions, or life-size, which is exactly the same size to the real thing.

There are two types of middleware models: logical and physical. The logical middleware model depicts how information moves throughout the enterprise conceptually. In contrast, the physical middleware model depicts both the actual method of information movement and the technology employed.

The logical model enables you to represent business information and define business rules. The physical model enables you to focus on the physical implementation of the model in a database. Both the logical and physical models represent design layers in the application development process.

Entity Relationship Model (ER Modeling) is a graphical approach to database design. It is a high-level data model that defines data elements and their relationship for a specified software system. An ER model is used to represent real-world objects. For example, each employee of an organization is a separate entity.

There is a plethora of commercial tools for conceptual schema design based on the EER model or UML class diagrams. These support the drawing, documenting and layout of conceptual schemas, and even the automatic generation of standard logical (relational) schemas to support database design.

A Conceptual Data Model is an organized view of database concepts and their relationships. The purpose of creating a conceptual data model is to establish entities, their attributes, and relationships. In this data modeling level, there is hardly any detail available on the actual database structure.

The following tasks are performed in an iterative manner:

1. Identify entity types.
2. Identify attributes.
3. Apply naming conventions.
4. Identify relationships.
5. Apply data model patterns.
6. Assign keys.
7. Normalize to reduce data redundancy.
8. Denormalize to improve performance.