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

- 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 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 BookmarkTitle:......Page 38 BookmarkTitle:......Page 39 BookmarkTitle:......Page 40 BookmarkTitle:......Page 41 BookmarkTitle:......Page 42 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