Download presentation
Presentation is loading. Please wait.
1
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 99223 chen@gonzaga.edu
2
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining 6.1 Operational Databases
3
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
4
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Data Modeling and Normalization First Normal Form Second Normal Form Third Normal Form
5
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.1 A simple entity- relationship diagram
6
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining The Relational Model
7
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining
9
6.2 Data Warehouse Design
10
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.2 A data warehouse process model
11
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Entering Data into the Warehouse Independent Data Mart ETL (Extract, Transform, Load Routine) Metadata
12
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Structuring the Data Warehouse: The Star Schema Fact Table Dimension Tables Slowly Changing Dimensions
13
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.3 A star schema for credit cared purchases
14
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining The Multidimensionality of the Star Schema
15
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.4 Dimensions of the fact table shown in Figure 6.3
16
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Additional Relational Schemas Snowflake Schema Constellation Schema
17
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.5 A constellation schema for credit card purchases and promotions
18
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Decision Support: Analyzing the Warehouse Data Reporting Data Analyzing Data Knowledge Discovery
19
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining 6.3 On-line Analytical Processing
20
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
21
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.6 A multidemensional cube for credit card purchases
22
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.
23
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.7 A concept hierarchy for location
24
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.8 Rolling up from months to quarters
25
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining 6.4 Excel Pivot Tables for Data Analysis
26
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Creating a Simple Pivot Table
27
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.9 A pivot table template
28
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Steps 1,2 (p.198)
29
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Steps 2, 3
30
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 3
31
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 4
32
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 5
33
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 6
34
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 7
35
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Result of Step 7 (p.198)
36
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.10 A summary report for income range
37
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining
39
Figure 6.10 A summary report for income range
40
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.9 A pivot table template
41
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 1, 2(bottom of p.198)
42
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 3 (top) and steps 1,2 3 (p.199)
43
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 4 (p.199)
44
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 4 (p.199)
45
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining
46
Steps 1,2
47
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 2
48
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining
49
Step 3 (p.200)
50
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 3 - continued (p.200)
51
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 3 - continued (p.200)
52
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 3 - continued (p.200)
53
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 3 - result (p.200)
54
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.11 A pie chart for income range
55
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.
56
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.12 A pivot table showing age and credit card insurance choice
57
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Method 1
58
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.13 Grouping the credit card promotionn data by age
59
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.14 PivotTable Layout Wizard Method 2- Steps 1,2,3
60
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Method 2- Step 4
61
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Steps 4,5
62
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 6
63
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 7
64
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 8
65
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
66
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.
67
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.15 A credit card promotion cube
68
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Steps 1,2,3 (p. 206)
69
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.
70
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 3 (result)
71
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Step 4
72
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.
73
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining Figure 6.16 A pivot table with page variables for credit card promotions
74
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining
75
Result of p.207
76
Dr. Chen, Data Mining A/W & Dr. Chen, Data Mining
Similar presentations
