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Onsite Seminar

Advanced Data Modeling


This workshop addresses all of the issues that trouble the experienced practicing data modeler:

  • How to apply the most advanced and contemporary techniques in entity relationship modeling
  • How to abstract in data modeling
  • How to create generalized data models
  • How to deal with time and history in data models
  • How to deal with complex business rules
  • How to represent entity life histories
  • How to validate a data model
  • How to interrelate data and process models
  • How to reconcile data models and object models
  • The difference between an operational and an analytical data model
  • How to transition a logical data model into a physical data model

Objective
On workshop completion, participants will be able to spot various advanced situations as they arise in their own modeling assignments, and deal with them efficiently and effectively.

This is a workshop and relies on many realistic exercises. You will learn how to apply leading edge data modeling principles to practical and difficult modeling situations. All the exercises have been derived from real modeling situations.
 
The workshop emphasizes three things:

  1. Analysis of business rules to develop detailed understanding of the business
  2. Expression of these in the data model
  3. The use of different objects to verify the data model

Prerequisite
Some experience with logical data modeling or formal training in logical data modeling.

Course Outline

Review of Data Modeling

  • Definition of data modeling
  • Levels of development- Conceptual, Logical, Physical
  • Placement of data modeling in the SDLC
  • Value of data modeling
  • Approaches to data modeling -Forward engineering, Reverse engineering
  • Types of projects-New development, Maintenance, Decision Support Systems)
  • Modeling constructs –Entity, Attribute, Relationship, Primary key, Constraint, Domain
  • Dealing with nulls

Advanced Data Modeling Constructs

  • Types of entities
  • Keys
    • Primary key characteristics:
    • Foreign keys
    • Natural keys
    • Surrogate keys
    • Pros and cons of each
    • Relationship of identifiers across development approaches

Normalization

  • Definition of
  • Advantages of
  • What normalization is and isn’t
  • Steps of normalization
    • First normal form, Second normal form, Third normal form
    • Boyce-Codd normal form
    • Fourth normal form
    • Fifth normal form

Abstraction in Data Modeling

  • Relevance of abstraction to development
  • Types of abstraction –Aggregation, Generalization. Subtyping
  • Rules
  • Inheritance
  • Single inheritance, Multiple inheritance
  • Other characteristics
  • Type of” Relationship

Inheritance Rules

  • Subtyping
  • Exclusivity rules
  • Precedence rules
  • One to one relationships
  • “Member of” relationships
  • Practical examples
  • Other characteristics

Flexibility in Models

  • Different meanings Of flexibility
  • Limitations of subtyping
  • Using abstraction to generalize models
  • Using type coding
  • Creating extensible models
  • Generalizing business rules, entities, attributes, relationships

Different Kinds of Relationships

  • Complex relationships
  • Relationship constraints
  • Mutually exclusive and inclusive relationships

Business Rules

  • Definition of a business rule
  • Types of business rules
  • Static (association) rules vs. dynamic rules
  • Sources of Business rules
  • Methods of recording business rules
  • Assigning the business rules to the appropriate object

Modeling Time and History

  • Short term and long term view
  • Five methods for dealing with time
  • The effect of time on determination of derived data

Advanced Data Modeling

  • Capturing business changes
  • Importance of representing the business time dimension
  • Application of time and history to DSS systems
  • Restatement
    • Revising past data perspectives
    • Recreating the past in terms of the present and the present in terms of the past

Validating Data Models

  • Use of views, use cases and scenarios
  • Use cases
    • Types of use cases: formal, basic, informal
    • Levels of use cases: abstract, concrete
  • Data model walkthroughs
  • Using formal data views
    • Listing the data used
    • Attribute characteristics:
    • Create, read, update, delete
    • Mandatory, option, prohibited, postponed
    • Assignment of rules to the data
    • Static rule assignment
    • Dynamic rule assignment

Model Reconciliation and Access Path Analysis

  • Importance of concurrent model developments
  • Definition of access path analysis
  • Kinds of access path analysis
  • Various methods of access path analysis
    • CRUD Matrix
    • Data views
    • Entity life histories
    • Usage maps
    • Prototyping
    • Data responsibility matrices
    • Affinity analysis
    • Other methods

Entity Life Histories (ELH)

  • Definition of ELH
  • Rules for
  • Value of
  • Syntax rules
  • UML vs. fence diagrams
  • Examples

Process Discovery Methods

  • Event Analysis
    • External events
    • Temporal events
    • Data triggers
  • Stakeholder analysis
    • Types of stakeholders
    • Activities pertinent to stakeholders
    • Object analysis
    • Entity Life histories
    • CRUD matrices and others

Relationship between Data Models and Object Models

  • Definition of an object
  • Characteristics of an object
    • Classification
    • Encapsulation
    • Inheritance
    • Message passing
    • Polymorphism
  • Relationship of OO to data modeling
  • Unified Modeling Language (UML)
  • Comparison of UML and ER modeling
  • Use of existing modeling methods in OO

Dimensional Modeling

  • Definition of dimensional modeling
  • Relevance of dimensional modeling
  • The business value of data warehousing
  • Operational data vs. informational data
  • Characteristics of a data warehouse
  • Facts and dimensions
  • Star schema vs. snowflake
  • Which is better?
  • Relationship of DSS modeling to ER modeling
  • Data warehouse vs. data mart

Data Design Compromises

  • Transition into design
  • Design factors
  • Creation of the first cut physical
  • Characteristics of the first cut physical
  • Safe compromises for optimization
  • Creation of the final cut physical
  • Characteristics of the final cut physical
  • Aggressive compromises for optimization
  • Effect of aggressive compromises on integrity

Summary and Conclusion

Duration
2-4 days

Course Format
Lecture, group discussion and exercises

Instructor
Tom Haughey

To request a quote for this in-house seminar
Please call (561) 218-4752 or email info@debtechint.com

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