Developing Analytical Database Applications

Overview

A strategic approach to enterprise database development.

Developing Analytical Database Applications delivers a complete "best-practices" methodology for building goal-directed enterprise database applications. Based on the advanced "spiral" approach, it focuses on the key stages of development that can make or break your project.

Learn how to identify your application's strategic vision and scope-and transform this information into a detailed,...

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Overview

A strategic approach to enterprise database development.

Developing Analytical Database Applications delivers a complete "best-practices" methodology for building goal-directed enterprise database applications. Based on the advanced "spiral" approach, it focuses on the key stages of development that can make or break your project.

Learn how to identify your application's strategic vision and scope-and transform this information into a detailed, practical design. Next, walk through constructing the application, and deploying it with maximum effectiveness. Coverage includes:

  • A complete, practical discussion of dimensional concepts.
  • Formal modeling techniques for producing analytical applications-with specific data warehousing examples.
  • Techniques for matching technologies to tasks and employee expertise.
  • RAD approaches for delivering the enterprise's most strategic processes first.
  • Mechanisms for reviewing and modifying applications as business goals change.

Developing Analytical Database Applications is a complete roadmap for ensuring that your enterprise database applications achieve your strategic goals-now, and for years to come.

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Editorial Reviews

Booknews
A detailed guide to developing analytical applications covering introductory materials, defining an analytical methodology, and applying it to various kinds of projects. Also presents an object- oriented approach to developing analytical database applications. An index presents selected material on the tools in the analytical applications marketplace, based on . Annotation c. by Book News, Inc., Portland, Or.
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Product Details

  • ISBN-13: 9780130824301
  • Publisher: Prentice Hall Professional Technical Reference
  • Publication date: 12/22/1998
  • Edition number: 1
  • Pages: 640
  • Product dimensions: 7.04 (w) x 9.25 (h) x 1.78 (d)

Table of Contents

1. Knowledge Workers and Analytical Applications.
What Are Analytical Applications? The Business Need for Analytical Applications. Downsized Organizations. Brittle Systems. Dispersed Decision Making. Faster Access to Information. Knowledge Workers and Analytical Applications. What Knowledge Workers Require. What Knowledge Workers Want. The Structure of Methodologies. Task Domain Analysis. User Domain Analysis. Data Domain Analysis. Summary.

2. A Model For Analytical Applications.
Typical Analytical Applications. Sales Performance. Budgeting and Planning. Manufacturing. Common Elements in Analytical Applications. A Model of Analytical Applications. Deep Structure. The Surface Structure. Life At the Interface. Summary.

3. Decision Support and Analytical Applications.
The components of information. Content. Structure. Delivery. Knowledge Worker Tasks. Monitoring & Reporting. Analysis & Diagnosis. Simulation & Planning. DSS Technology Space. Data Acquisition Quality and Delivery. Query and Report Writers. Managed Query Environment. Data Visualization. Data Mining. Architectures for Analytical Applications. Relational Online Analytical Processing (ROLAP). Multi-dimensional Databases (MDBs). Hybrid OLAP. Which Technology is More Suitable? Technology Requirements in the DSS Space. Summary.

4. Dimensional Structure of Analytical Applications.
Multi-dimensional Views. The Structure of Information. Types of Dimensions. Structural Dimensions. Information Dimensions. PartitioningDimensions. Categorical Dimensions. More about Dimensions. Multicubes vs. Hypercubes. Dimension Depth and Height. Data Sparsity. Dimensional Explosion. Dimensional Databases. The Star Schema. Deciding between MDBs and RDBs. Summary.

5. Architecture for Analytical Applications.
A Sample Database. The Semantics of Information. An Example of a Semantic Ambiguity. Analytical Database Terms and Definitions. Notes on Facts and Dimensions. Implementing dimensional tables. Requirements. Monitoring and reporting. Sharing results. Analysis and diagnosis. Simulation and planning. Summary.

6. A Methodology for Analytical Applications.
System Model and Documentation. What is a Methodology? Different Kinds of Methodologies. Summary of Four Quadrants. Quadrant I: Vision & Scope. Quadrant II: Design. Quadrant III: Build. Quadrant IV: Deployment. Project Planning. Task Management. Developing the Plan. Summary.

7. Vision & Scope.
Application Goals and Constraints. Monthly Close Processing. IS Staffing Levels. System Availability. Data Center Operations. Technology Decisions. End-user Computing. Setting Goal Priorities. Vision & Scope: Purpose. Vision & Scope: Deliverables. Introduction. Purpose. Goals and Constraints. Readiness Assessment. Project Planning. Glossary. Vision & Scope Actions. Schedule of Events for Vision & Scope. The Consensus Meeting. Collaborative Sessions. Readiness Assessment. Commitment Meeting. Summary.

8. Design.
Purpose. Design Qualities. Deliverables: Design Document. Management Documentation. Functional Documentation. Participatory Design. Technical Documentation. Activities: Defining the Business Processes. Collaborative Design Sessions. Source System Analysis. Analytical Process Analysis. Actions. Business Process Design. Assessment. Planning. Commitment Meeting. Summary.

9. Build and Deployment.
Build. Imperative #1: Ship Every Day. Imperative #2: Can we Stop Today? The Project Plan. For Each Build Deliverable. Assessment. Risk management and problem solving. Planning. Deployment. Rollout #n. Summary.

10.
A Little Story. The Whole from the Parts. The Perfect Data Warehouse. Semantics vs. Structure. The Star Architecture. Data Modeling. Introduction. The Steps. The Business Model. Data Model Analysis. Model Normalization. Integrity Constraints. Dimensional Model. Structural Dimensions. Informational Dimensions. Categorical Dimensions. Partitioning Dimensions. Slowly Changing Dimensions. Physical Model. General Objectives. Categorical Dimensions. Connectedness and Complexity. Complexity. Sample Data Warehouses. The Retail Model. Health Care Manufacturing. Stars, Snowflakes and Galaxies. Summary.

11.
Functional vs. Component Decomposition. When to Stop. Object Analysis. Use Case Analysis. Class Relationships. The Process. The Analytical Application. Step 1: The Use Case Scenario. Step 2: Functional Decomposition. Step 3: Definition of Input-Operator-Output. Step 4: Normalization of Candidate Classes. Step 5: Abstraction of the Class Structure. Summary.

12. Dimension Class Model.
Hypercube Class Model. Hypercube Representations. The Library Class Model. External Databases. Result Set. Relational Databases. Presentation Objects. Data Grids. Charts. Reports. Hypercube Orientation. Scripts. Dimensional Calculation. Activity Diagrams. Cube Storage Management. Translating Queries. Summary.

Appendix A: Market Leaders in Analytical Applications.
Glossary.
Index.
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