Logical Models

There are three levels of logical models that are used to capture business information requirements: the Entity Relationship Diagram (ERD), the KeyBased (KB) Model, and the Fully Attributed(FA) model. The ERD and KB models are also called “area data models” because they often cover a broad business area that is larger than the business chooses to address with a single automation project. In contrast, the FA model is a “project data model” because it typically describes a portion of an overall data structure intended for support by a single automation effort.

The Entity Relationship Diagram

The Entity Relationship Diagram is a high level data model that shows the major entities and relationships which support a wide business area.

The objective of the entity relationship diagram is to provide a view of business information requirements sufficient to satisfy the need for broad planning for development of its information system. These models are not very detailed (only major entities are included,) and there is not much detail, if any, on attributes. Many-to-many (non-specific) relationships are allowed, and keys are generally not included. This is primarily a presentation or discussion model.

The Key-Based Model

A Key-Based Model describes the major data structures which support a wide business area. All entities and primary keys are included, along with sample attributes.

The objective of the key-based model is to provide a wide business view of data structures and keys needed to support the area. This model provides a context in which detailed implementation level models can be constructed. The model covers the same scope as the Area ERD, but exposes more of the detail.

The Fully-Attributed (FA) Model

A Fully Attributed Model is a third normal form data model that includes all entities, attributes and relationships needed by a single project. The model includes entity instance volumes, access paths and rates, and expected transaction access patterns against the data structure.

16 ∙ Information Systems, Databases, and Models