Data Modeling: Logical Database Design

This textbook, and its companion volume which follows, provide a solid basis from which one can successfully implement relational database, multidimensional data warehouse and business intelligence (BI) technologies. The principal objective of this initial course volume is to convey a practical and common sense guide to the theory and concepts of data modeling. Using these sophisticated techniques one can create an elegant logical design of a database. Within this course we discuss not only the premier modeling theories from the best industry experts but also present the practical and real-world experience of the past 20-years of Sideris data design practitioners.

 

The methodologies discussed are applicable to any relational database environment, including IBM DB2, the Oracle database, Microsoft SQL Server, the open-source MySQL and PostgreSQL databases as well as other RDBMS platforms. They are also applicable to other database technologies, such as object databases and legacy IMS and IDMS databases. Finally, while we use the free Oracle SQL Developer Data Modeler product as a demonstration modeling tool, one can complete the exercises of this course and apply the techniques learned using any other popular data model diagramming tool, such as IBM InfoSphere Data Architect, CA ErWin Data Modeler, Embarcadero ER/Studio and others.

 

This textbook can be used for advanced self-study, or for instructor-led training in both in-class or virtual-class environments. Textbooks can also be used for ongoing reference long after the course is completed.

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In the workshop exercises you will build an increasingly complex series of data models, and will critique and correct other models. A summary of the detailed objectives of this textbook are:
• A review of model-based design, including process modeling, physical data modeling and other modeling techniques which relate to logical data modeling.
• A comparison of data modeling concepts and theories, including top-down data modeling, bottom-up data modeling, data normalization, object-oriented and semantic modeling.
• Hints, tips and guidelines in identifying entities, attributes and relationships which should appear within a data model.
• Review the popular commercial data modeling tools commonly in use today.
• The benefits of building a conceptual data model in advance of the logical model.
• Learn to find and fix well-known mistakes which can exist in relationship definitions, finding missing attributes and correcting erroneous attribute definitions.
• Review a recommended strategy for unique identifiers.
• Using semantic modeling constructs and techniques such as supertypes, subtypes, generalization, specialization, constraints, lattices and arcs.
• Using object-oriented modeling techniques such as domains, attribute classes, extended types and abstraction of attributes.
• Time-dependency and state-dependency within a data model.
• Explore classic structures and modeling patterns, including many-to-many recursion.
• Steps and available options for engineering a physical data model from a logical model.
• Reverse engineering and forward engineering of a physical data model into an implementation relational database. 
The primary target audiences for this course are:
• Business analysts
• Data modelers, data analysts and data architects
• Senior application designers and developers
• Database administrators
No mandatory prerequisites exist for this course. However a basic knowledge of computer systems, business systems requirements and database technologies is helpful.
If your implementation database platform is the Oracle database, then you may wish to consider one of the following courses next, depending upon your job role and area of interest:
• ORACLE DATABASE 11G R2: SQL FUNDAMENTALS – COMPLETE LIBRARY
• ORACLE DATABASE 11G R2: ADMINISTRATION I
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Data Modeling Theory & Concepts
About Model-Based Design
About Data Modeling
About Data Model Diagrams
Advanced Modeling Methodologies

Building An Initial Data Model
Principles Of Data Modeling
Building The Model
Identifying Entities
Identifying Attributes
Identifying Relationships
A Simple Modeling Scenario

Drawing A Model Using Software Engineering Tools
About Data Modeling Tools
Drawing A Data Model Diagram

Increasing The Accuracy Of The Model
Starting With A Conceptual Model
Supplementing The Requirements
Refining The Relationship Definitions

Finding & Fixing Attribute Mistakes
Capturing Missing Attribute Details
Character
Numeric
Date
Correcting Attribute Definitions
Unique Identifiers
Unit Of Measure Attributes

Semantic & Object Oriented Modeling Of Entities & Relationships
Defining Supertypes & Subtypes
Entity Name Problems
Naming Standards
Specialization & Generalization
Subtype Constraints
Defining Relationship Arcs

Semantic & Object Oriented Modeling Of Domains & Types
Defining Domains
Defining Types
Collection Types

Time-Dependency & State-Dependency
About Time & State
Time-Dependent Sub-Model
Person / Individual Roles Sub-Model

Classic Structures & Patterns
Master-Detail-Detail
M:N Recursion (Bill-Of-Materials)
Organization Unit Hierarchy
Entity Locations
Entity Contacts

Logical / Physical Model Transformation
About Physical Data Models
Physical Relational Transformation
Model Transformation Example
Automatic Transformation
Supertype Transformation

RDBMS Implementation Of The Physical Model
Reverse Engineer A Physical Model
About The Relational Database
Relational Database Objects
Forward Engineer A Physical Model

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