
- •Contents
- •Intended Audience
- •Typographical Conventions
- •Benefits of Data Modeling
- •Data Modeling Sessions
- •Session Roles
- •Sample IDEF1X Modeling Methodology
- •Logical Models
- •The Entity Relationship Diagram
- •The Key-Based Model
- •The Fully-Attributed (FA) Model
- •Physical Models
- •The Transformation Model
- •The DBMS Model
- •Benefits of Data Modeling in ERwin
- •The Entity-Relationship Diagram
- •Defining Entities and Attributes
- •Logical Relationships
- •Many-to-Many Relationships
- •Validating the Design of the Logical Model
- •Data Model Example
- •Identifying Types of Keys
- •Selecting a Primary Key
- •Designating Alternate Key Attributes
- •Designating Inversion Entry Attributes
- •Relationships and Foreign Key Attributes
- •Dependent and Independent Entities
- •Identifying Relationships
- •Non-Identifying Relationships
- •Rolenames
- •Naming Entities and Attributes
- •Synonyms, Homonyms, and Aliases
- •Entity Definitions
- •Definition References and Circularity
- •Constructing a Business Glossary
- •Attribute Definitions
- •Rolenames
- •Definitions and Business Rules
- •Relationship Cardinality
- •Cardinality in Non-Identifying Relationships
- •Referential Integrity
- •Reading Referential Integrity Options
- •RI, Cardinality, and Identifying Relationships
- •RI, Cardinality, and Non-Identifying Relationships
- •Additional Relationship Types
- •Many-to-Many Relationships
- •N-ary Relationships
- •Recursive Relationships
- •Subtype Relationships
- •Complete Versus Incomplete Subtype Structures
- •Inclusive and Exclusive Relationships
- •When to Create a Subtype Relationship
- •Overview of the Normal Forms
- •Functional Dependence (FD)
- •Full Functional Dependence
- •First Normal Form (1NF)
- •Second Normal Form (2NF)
- •Third Normal Form (3NF)
- •Common Design Problems
- •Repeating Data Groups
- •Multiple Use of the Same Attribute
- •Multiple Occurrences of the Same Fact
- •Conflicting Facts
- •Derived Attributes
- •Missing Information
- •Unification
- •How Much Normalization Is Enough?
- •ERwin Support for Normalization
- •First Normal Form Support
- •Second and Third Normal Form Support
- •Creating a Physical Model
- •Summary of Logical and Physical Model Components
- •Denormalization
- •Classification of Dependent Entities
- •Glossary
- •Index

Physical Models
Physical Models
There are also two levels of physical models for an implementation project: the Transformation Model and the DBMS Model. The physical models capture all of the information that systems developers need to understand and implement a logical model as a database system. The Transformation Model is also a “project data model” that describes a portion of an overall data structure intended for support by a single automation effort. ERwin supports individual projects within a business area, allowing the modeler to separate a larger area model into submodels, called subject areas. Subject areas can be developed, reported on, and generated to the database in isolation from the area model and other subject areas in the model.
The Transformation Model
The objectives of the transformation model are to provide the Database Administrator (DBA) with sufficient information to create an efficient physical database, to provide a context for the definition and recording of the data elements and records that form the database in the data dictionary, and to help the application team choose a physical structure for the programs that will access the data.
When deemed appropriate for the development effort, the model can also provide the basis for comparing the physical database design against the original business information requirements to:
■Demonstrate that the physical database design adequately supports those requirements.
■Document physical design choices and their implications, such as what is satisfied, and what is not.
■Identify database extensibility capabilities and constraints.
The DBMS Model
The Transformation Model directly translates into a DBMS model, which captures the physical database object definitions in the RDBMS schema or database catalog. ERwin directly supports this model with its schema generation function. Primary keys become unique indices. Alternate keys and inversion entries also may become indices. Cardinality can be enforced either through the referential integrity capabilities of the DBMS, application logic, or “after the fact” detection and repair of violations.
Information Systems, Databases, and Models 2–7