Building Business Intelligence and Data Mining Applications with Microsoft SQL Server 2005

✍ Scribed by Loria J.

• Tongue: English
• Leaves: 65
• Category: Library

✦ Synopsis

• Getting information from enterprise data- Using BI across the enterprise as an integral part of doing business- Capture and model all of your data- Integration with business processes- Relational reporting and OLAP converged through a single dimensional model

✦ Table of Contents

Building Business Intelligence and Data Mining Applications with Microsoft SQL Server 2005......Page 1
Introductions......Page 2
Agenda......Page 3
Agenda......Page 4
Business Intelligence Platform......Page 5
Overview......Page 6
Business Intelligence Challenges......Page 7
What Is a Cube?......Page 8
What Is a Cube?......Page 9
Enterprise BI Today......Page 10
Relational vs. OLAP Reports......Page 11
Agenda......Page 12
The Unified Dimensional Model The Best of Relational and OLAP......Page 13
UDM’s Role......Page 14
Enterprise BI with UDM......Page 15
Scalable, High Performance UDM Server......Page 16
Analysis Server as UDM Server......Page 17
Streamlined BI Infrastructure......Page 18
BI Development Studio......Page 19
Performance......Page 20
MOLAP, ROLAP, and HOLAP......Page 21
MOLAP Caching......Page 22
Agenda......Page 23
UDM and The BI Studio......Page 24
UDM Data Sources......Page 25
Data Source Views......Page 26
Dimensions and Hierarchies......Page 27
Cubes......Page 28
Perspectives......Page 29
Categorization......Page 30
Time......Page 31
Translations......Page 32
Attribute Semantics......Page 33
Key Performance Indicators......Page 34
Closing the Loop......Page 35
ProClarity Business Intelligence Analytics......Page 36
ProClarity Key Differentiators......Page 37
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Agenda......Page 43
CRoss Industry Standard Processfor Data Mining (CRISP)......Page 45
Data Mining Algorithms......Page 46
Microsoft Mining Models......Page 47
When To Use What......Page 48
Decision Trees......Page 50
Decision Trees (cont.)......Page 51
Decision Trees (cont.)......Page 52
Naïve Bayes......Page 53
Naïve Bayes (cont.)......Page 54
Cluster Analysis......Page 55
Cluster Analysis (cont.)......Page 56
Sequence Clustering......Page 57
Sequence Clustering (cont.)......Page 58
Microsoft Mining Models......Page 59
Association Rules......Page 60
Association Rules – Support......Page 61
Association Rules – Confidence......Page 62
Time Series......Page 63
Neural Network......Page 64
Back-Propagation......Page 65