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ADVANCED DATA MODELING:

Objects, Business Rules, and Other Important Things

Why this Course?

The emergence of data warehouse, object technology, multi-media, document, rule-based and other unstructured data type technology has created a challenge to traditional data modeling methods. Furthermore, the World Wide Web has altered shape of both business work and information management.

Basic entity-relationship modeling and normalization techniques capture only the structural and static relationships of structured data. This seminar addresses semantic modeling of complex and unstructured data types along with the behavioral aspects of data. This provides robust, high-integrity, resource-centric application development and database design and exploitation of today’s emerging technologies.

You learn how to apply object-oriented concepts to data modeling including inheritance and life cycle state, as well as how to model business rules and integrity policies. How to model time-dependent data and distributed data are addressed.

Data models must support not just application development, but business process re-engineering, TQM processes and rapid application development (RAD) projects. In this seminar you learn how to model generic data types to support flexible design.

Informational applications such as data warehouse, decision support and executive information systems require models that are inherently different from operational data models. You learn rules and guidelines for effective data warehouse modeling and design.

 

Learning Outcomes:

Upon completion of this seminar, you will be able to:
  • Identify and model special cases of data, such as:
    • Types and subtypes with inheritance
    • Object/entity life cycles and state transition diagrams
    • Time-variant data
    • Generic data types and metadata types
  • Define and model business rules and data integrity policies
  • Develop data warehouse data models including star schema models
  • Describe and model hypermedia data types
  • Describe how emerging trends will influence data modeling
Audience:

Data administrators, data resource management staff, DBAs, systems analysts and application developers who participate in data requirements definition, data analysis and data modeling, data warehouse staff, Web-page designers
Format: Lecture with exercises and discussion to reinforce the concepts
Duration:
 3 Days
Pre-requisites: Basic data modeling training with a minimum of six months data modeling experience

 

 

Course Outline:

 

1. Advances in Data Modeling Methods
  • Paradigms for effective data modeling and management
    • Business model
    • Mind map and object abstraction
    • Architecture and the Zachman Architecture Framework
  • Data and the resource life cycle
  • Semantic network influences
  • Classification theory and taxonomy
  • Object-oriented analysis and role/responsibility modeling

 

2. Modeling Generic Entity Types
  • Metadata as data
  • Relationship types
  • Data model patterns
  • Generic data models and flexibility design

 

3. Object Concepts Applied to Data Modeling
  • Object class as an extension of entity type
  • Abstraction and encapsulation
  • Object typing and inheritance
  • Entity types and subtypes
    • Subtype inheritance
    • Exclusive versus inclusive subtypes
    • Single versus multiple inheritance
    • Validation rules
  • Dynamic object models and event models
  • Object state and state transition
  • Entity life cycles
    • Entity life-cycle diagrams and state transition diagrams
    • Entity life cycle types
    • Life-cycle validation rules
    • Events and state transition
  • Business rules as pre-conditions and post-conditions

 

 

4. Business rules: Modeling Integrity Constraints and Policies

  • Meta model for business rules
  • Business rule definition
  • A taxonomy of business rules
  • Business rule format
  • Characteristics of effective rules

5. Data Warehouse Modeling

  • Data warehouse and data marts
  • Supporting strategic and tactical business processes
  • Identifying data subjects
  • Data analysis for data warehouse
  • Data warehouse attribute inclusion rules
  • Classifying time-variant data
  • Star schema modeling
  • Determining dimensions
  • Summary and derived data modeling
  • Rules for de-normalization

6. Modeling Hypermedia Data types for Internet and Multi-Media Databases

  • Impact of Internet and Intranet on data management
  • Non-traditional data types
  • Hypermedia modeling: nodes and links
  • Semantic data modeling

7. Future Trends in Information Modeling

  • Template models
  • Model-driven engineering
  • The future of software packages
  • Intelligent database: convergence of information technologies

 

 

 

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