Resource-Centric Application Engineering

This seminar describes the trends in the evolution of application development in the information age. It describes how one can–and must–exploit today's information technology for competitive advantage applications.

You learn the emerging directions in information management and data modeling along with their ramification on application engineering and data resource development.

You learn characteristics of a data-driven application architecture and approach that can reduce the cost of application development while increasing the quality of applications.

• Describe what conceptual data modeling is and why it is required
• Describe the goals and objectives of a shared data environment
• List the functions and responsibilities of DA as it relates to application development
• Describe the required data-driven development tasks in the application development life cycle
• List the components of a strategic automation architecture
• Define subject areas as a mechanism for organizing the data resources of an enterprise
• Describe elements of effective information management standards and procedures
• Describe a business-driven entity-relationship model and how it reflects the enterprise view of its data
• Describe normalization from a business viewpoint
• Describe how to validate the stability of the conceptual data model
• Identify and model entity types and subtypes
• Describe techniques to model entity life cycles and business rules

 

4. Conceptual Data Modeling: An Integrated Approach

• Entity-relationship modeling: essential ingredients
• Normalization as a business tool
• Information views
• Flexibility and stability analysis
• Architectures and their relationships
• Critical success factors for information management

5. Resource-Centric Application Development

• The “information-based” approach versus the “data flow” approach
• Entity types and subtypes as redundancy management
• Event-driven development and entity life cycles
• Events and responding processes
• Resource-centric application architecture
• Point-of-origin as data entry design
• Stewardship and accountability for data integrity
• Implementing data integrity

6. Transitioning a Conceptual Data Model for Physical Database Design

• Model simplification principles
• Transaction analysis and logical access maps
• Index identification and data clustering
• Type/subtype implementation
• Entity life cycle implementation
• The information warehouse
• Principles for compromising the conceptual model