Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Chapter 6 The Data Warehouse Jason C. H. Chen, Ph.D. Professor of MIS School of Business Administration Gonzaga University Spokane, WA

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining 6.1 Operational Databases

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Data Modeling and Normalization One-to-One Relationships One-to-Many Relationships Many-to-Many Relationships

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Data Modeling and Normalization First Normal Form Second Normal Form Third Normal Form

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.1 A simple entity- relationship diagram

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining The Relational Model

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining

6.2 Data Warehouse Design

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.2 A data warehouse process model

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Entering Data into the Warehouse Independent Data Mart ETL (Extract, Transform, Load Routine) Metadata

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Structuring the Data Warehouse: The Star Schema Fact Table Dimension Tables Slowly Changing Dimensions

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.3 A star schema for credit cared purchases

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining The Multidimensionality of the Star Schema

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.4 Dimensions of the fact table shown in Figure 6.3

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Additional Relational Schemas Snowflake Schema Constellation Schema

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.5 A constellation schema for credit card purchases and promotions

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Decision Support: Analyzing the Warehouse Data Reporting Data Analyzing Data Knowledge Discovery

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining 6.3 On-line Analytical Processing

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining OLAP Operations Slice – A single dimension operation Dice – A multidimensional operation Roll-up – Aggregation, a higher level of generalization Drill-down – A greater level of detail the reverse of a roll-up Rotation – View data from a new perspective

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.6 A multidemensional cube for credit card purchases

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Concept Hierarchy A mapping that allows attributes to be viewed from varying levels of detail.

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.7 A concept hierarchy for location

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.8 Rolling up from months to quarters

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining 6.4 Excel Pivot Tables for Data Analysis

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Creating a Simple Pivot Table

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.9 A pivot table template

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Steps 1,2 (p.198)

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Steps 2, 3

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 3

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 4

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 5

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 6

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 7

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Result of Step 7 (p.198)

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.10 A summary report for income range

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining

Figure 6.10 A summary report for income range

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.9 A pivot table template

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 1, 2(bottom of p.198)

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 3 (top) and steps 1,2 3 (p.199)

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 4 (p.199)

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 4 (p.199)

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining

Steps 1,2

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 2

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining

Step 3 (p.200)

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 3 - continued (p.200)

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 3 - continued (p.200)

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 3 - continued (p.200)

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 3 - result (p.200)

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.11 A pie chart for income range

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Pivot Tables for Hypothesis Testing Younger cardholders purchase credit card insurance whereas more senior cardholders do not.

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.12 A pivot table showing age and credit card insurance choice

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Method 1

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.13 Grouping the credit card promotionn data by age

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.14 PivotTable Layout Wizard Method 2- Steps 1,2,3

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Method 2- Step 4

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Steps 4,5

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 6

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 7

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 8

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Result of Method 2 The average age for credit card insurance = no is approximately 41.42, whereas the average age for credit card insurance = yes is approximately 32.33

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Creating a Multidimensional Pivot Table Investigate relationships between the magazine, watch, and life insurance promotions relative to customer gender and income range.

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.15 A credit card promotion cube

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Steps 1,2,3 (p. 206)

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Steps 3 (after dragging life insurance promotion to DropData Items Here. ) Continue dragging watch promotion and magazine promotion to DropData Items Here.

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 3 (result)

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Step 4

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Decision Making – steps 1-3, p.207 A total of two customers took advantage of the life insurance and magazine promotions but did not purchase the watch promotion.

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining Figure 6.16 A pivot table with page variables for credit card promotions

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining

Result of p.207

Dr. Chen, Data Mining  A/W & Dr. Chen, Data Mining