Thursday, November 28, 2013

Modeling methodologies


Modeling methodologies

 

A data model is a graphical view of data created for analysis and design purposes. Data modeling includes designing data warehouse databases in detail

Data models represent information areas of interest. While there are many ways to create data models, according to Len Silverston only two modeling methodologies stand out, top-down and bottom-up:

Bottom-up models or View Integration models are often the result of a reengineering effort. They usually start with existing data structures forms, fields on application screens, or reports. These models are usually physical, application-specific, and incomplete from an enterprise perspective. They may not promote data sharing, especially if they are built without reference to other parts of the organization.

Top-down logical data models, on the other hand, are created in an abstract way by getting information from people who know the subject area. A system may not implement all the entities in a logical model, but the model serves as a reference point or template.

Sometimes models are created in a mixture of the two methods: by considering the data needs and structure of an application and by consistently referencing a subject-area model.

There are several notations for data modeling. The actual model is frequently called "Entity relationship model", because it depicts data in terms of the entities and relationships described in the data. An entity-relationship model (ERM) is an abstract conceptual representation of structured data

Generic data models are generalizations of conventional data models. They define standardized general relation types, together with the kinds of things that may be related by such a relation type. The definition of generic data model is similar to the definition of a natural language. For example, a generic data model may define relation types such as a 'classification relation', being a binary relation between an individual thing and a kind of thing (a class) and a 'part-whole relation', being a binary relation between two things, one with the role of part, the other with the role of whole, regardless the kind of things that are related.

Data warehouse modeling includes:

  • Top Down / Requirements Driven Approach
  • Fact Tables and Dimension Tables
  • Multidimensional Model/Star Schema
  • Support Roll Up, Drill Down, and Pivot Analysis
  • Time Phased / Temporal Data
  • Operational Logical and Physical Data Models
  • Normalization and De normalization
  • Model Granularity : Level of Detail

 

 

Examples of metadata include:

  • Data definitions
  • Data models
  • Data mapping specifications

The table below compares the different features:

Feature
Conceptual
Logical
Physical
Entity Names
 
Entity Relationships
 
Attributes
 
 
Primary Keys
 
Foreign Keys
 
Table Names
 
 
Column Names
 
 
Column Data Types
 
 

Below we show the conceptual, logical, and physical versions of a single data model.

Conceptual Model Design

Conceptual Model Design
Logical Model Design

Logical Model Design
Physical Model Design

Physical Model Design

 

 

 

 

 

 

An Enterprise Data Model is an integrated view of the data produced and consumed across an entire organization. It incorporates an appropriate industry perspective. An Enterprise Data Model (EDM) represents a single integrated definition of data, unbiased of any system or application. It is independent of "how" the data is physically sourced, stored, processed or accessed. The model unites, formalizes and represents the things important to an organization, as well as the rules governing them.

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