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SSAS 2008 Data Mining ,[object Object],[object Object]
Session Prerequisites ,[object Object],[object Object],[object Object],[object Object]
Session Objectives and Agenda ,[object Object],[object Object],[object Object]
What and Why Data Mining? Presentation Exploration Discovery Passive Interactive Proactive Role of Software Business Insight Predictive Analytics Canned reporting Ad-hoc reporting OLAP Data mining
Cubes vs. Data Mining
DM - From Scenarios to Tasks
From Tasks to Techniques
BI for Everyone Enterprise – Performance Point Individual – Excel  Project – Share Point
Microsoft’s Predictive Analytics Data Mining SQL extensions (DMX) Application Developer Data Mining Specialist Microsoft Dynamics CRM Analytics Foundation SQL Server 2005  Business Intelligence Development Studio Microsoft SQL Server 2008 Analysis Services Information  Worker Data Mining Add-ins for  the 2007 Microsoft Office system Microsoft SQL Server 2008 Data Mining BI Analyst Custom Algorithms
Data Mining Add-ins for Office 2007 Table Analysis Tools for Excel 2007 Data Mining Template for Visio 2007 Data Mining Client for Excel 2007 Information  Worker BI Analyst Data Mining Specialist
Microsoft Data Mining Lifecycle  CRISP-DM SSAS (Data Mining) Excel SSAS (DSV) Query Excel SSIS SSAS SSRS Excel Your Apps SSIS SSAS Excel Data www.crisp-dm.org Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment
Understand & Prepare specifics
Demo 1 – Explore / Clean / Partition Data
Modeling Specifics
Demo 3 – Modeling
Evaluation Specifics
Demo 4 – Evaluation
Data Mining – Logical Model algorithm Mining Model Mining Model Training Data DB data Client data Application data Data Mining Engine To Predict Predicted Data Mining Model DB data Client data Application data “ Just one row ” Data Mining Engine
Data Mining - Physical Model Analysis Services Server Mining Model Data Mining Algorithm Data Source Your Application OLE DB/ ADOMD/ XMLA Deploy BI Dev Studio  (Visual Studio) App Data
Data Mining Interfaces – APIs XMLA Over TCP/IP XMLA Over HTTP Analysis Server (msmdsrv.exe) OLAP Data Mining Server ADOMD.NET .Net Stored Procedures Microsoft Algorithms Third Party Algorithms OLEDB for OLAP/DM ADO/DSO Any Platform, Any Device C++ App VB App .Net App AMO Any App ADOMD.NET WAN DM Interfaces
Configuration ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Deployment ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Excel Functions* ,[object Object],[object Object],[object Object]
Data Mining Extensions (DMX)  CREATE MINING MODEL  CreditRisk (CustID   LONG KEY, Gender  TEXT DISCRETE, Income    LONG CONTINUOUS, Profession  TEXT DISCRETE, Risk   TEXT DISCRETE PREDICT) USING  Microsoft_Decision_Trees INSERT INTO   CreditRisk  (CustId, Gender, Income, Profession, Risk) Select  CustomerID, Gender, Income, Profession,Risk From Customers Select  NewCustomers.CustomerID, CreditRisk.Risk,  PredictProbability(CreditRisk.Risk) FROM  CreditRisk  PREDICTION JOIN  NewCustomers ON   CreditRisk.Gender=NewCustomer.Gender   AND  CreditRisk.Income=NewCustomer.Income AND  CreditRisk.Profession=NewCustomer.Profession
DMX Column Expressions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Mining Interfaces – XMLA ++ XMLA Over TCP/IP XMLA Over HTTP Analysis Server (msmdsrv.exe) OLAP Data Mining Server ADOMD.NET .Net Stored Procedures Microsoft Algorithms Third Party Algorithms OLEDB for OLAP/DM ADO/DSO Any Platform, Any Device C++ App VB App .Net App AMO Any App ADOMD.NET WAN DM Interfaces
New to SQL Server 2008 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
DM Webcasts Fri, 02 Nov 2007 MSDN Webcast: Build Smart Web Applications with SQL Server Data Mining (Level 200) Thu, 08 Nov 2007 MSDN Webcast: Building Adaptive Applications with SQL Server Data Mining (Level 300) Mon, 19 Nov 2007 MSDN Webcast: Extending and Customizing SQL Server Data Mining (Level 300) Fri, 30 Nov 2007 MSDN Webcast: Creating Visualizations for SQL Server Data Mining (Level 300) Thu, 01 Nov 2007 TechNet Webcast: Deliver Actionable Insight Throughout Your Organization with Data Mining (Part 1 of 3): Your First Project with SQL Server Data Mining (Level 200) Thu, 15 Nov 2007 TechNet Webcast: Deliver Actionable Insight Throughout Your Organization with Data Mining (Part 2 of 3): Understand SQL Server Data Mining Add-ins for the 2007 Office System (Level 200) Thu, 29 Nov 2007 TechNet Webcast: Deliver Actionable Insight Throughout Your Organization with Data Mining (Part 3 of 3): Use Predictive Intelligence to Create Smarter KPIs (Level 200)
BI Resources from Lynn Langit 1. “Foundations of SQL Server 2005 Business Intelligence” (published by APress in April 2007) 2.   http://blogs.msdn.com/SoCalDevGal 3.   “Building Business Intelligence Solutions with SQL Server 2008”  (MSPress Fall 2008)
DM Resources Technical Communities, Webcasts, Blogs, Chats & User Groups http://www.microsoft.com/communities/default.mspx   Microsoft Developer Network (MSDN) & TechNet  http://microsoft.com/msdn   http://microsoft.com/technet   Trial Software and Virtual Labs http://www.microsoft.com/technet/downloads/trials/default.mspx   Microsoft Learning and Certification http://www.microsoft.com/learning/default.mspx   SQL Server Data Mining http://www.sqlserverdatamining.com http://www.microsoft.com/bi/bicapabilities/data-mining.aspx

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Data Mining 2008

  • 1.
  • 2.
  • 3.
  • 4. What and Why Data Mining? Presentation Exploration Discovery Passive Interactive Proactive Role of Software Business Insight Predictive Analytics Canned reporting Ad-hoc reporting OLAP Data mining
  • 5. Cubes vs. Data Mining
  • 6. DM - From Scenarios to Tasks
  • 7. From Tasks to Techniques
  • 8. BI for Everyone Enterprise – Performance Point Individual – Excel Project – Share Point
  • 9. Microsoft’s Predictive Analytics Data Mining SQL extensions (DMX) Application Developer Data Mining Specialist Microsoft Dynamics CRM Analytics Foundation SQL Server 2005 Business Intelligence Development Studio Microsoft SQL Server 2008 Analysis Services Information Worker Data Mining Add-ins for the 2007 Microsoft Office system Microsoft SQL Server 2008 Data Mining BI Analyst Custom Algorithms
  • 10. Data Mining Add-ins for Office 2007 Table Analysis Tools for Excel 2007 Data Mining Template for Visio 2007 Data Mining Client for Excel 2007 Information Worker BI Analyst Data Mining Specialist
  • 11. Microsoft Data Mining Lifecycle CRISP-DM SSAS (Data Mining) Excel SSAS (DSV) Query Excel SSIS SSAS SSRS Excel Your Apps SSIS SSAS Excel Data www.crisp-dm.org Business Understanding Data Understanding Data Preparation Modeling Evaluation Deployment
  • 12. Understand & Prepare specifics
  • 13. Demo 1 – Explore / Clean / Partition Data
  • 15. Demo 3 – Modeling
  • 17. Demo 4 – Evaluation
  • 18. Data Mining – Logical Model algorithm Mining Model Mining Model Training Data DB data Client data Application data Data Mining Engine To Predict Predicted Data Mining Model DB data Client data Application data “ Just one row ” Data Mining Engine
  • 19. Data Mining - Physical Model Analysis Services Server Mining Model Data Mining Algorithm Data Source Your Application OLE DB/ ADOMD/ XMLA Deploy BI Dev Studio (Visual Studio) App Data
  • 20. Data Mining Interfaces – APIs XMLA Over TCP/IP XMLA Over HTTP Analysis Server (msmdsrv.exe) OLAP Data Mining Server ADOMD.NET .Net Stored Procedures Microsoft Algorithms Third Party Algorithms OLEDB for OLAP/DM ADO/DSO Any Platform, Any Device C++ App VB App .Net App AMO Any App ADOMD.NET WAN DM Interfaces
  • 21.
  • 22.
  • 23.
  • 24. Data Mining Extensions (DMX) CREATE MINING MODEL CreditRisk (CustID LONG KEY, Gender TEXT DISCRETE, Income LONG CONTINUOUS, Profession TEXT DISCRETE, Risk TEXT DISCRETE PREDICT) USING Microsoft_Decision_Trees INSERT INTO CreditRisk (CustId, Gender, Income, Profession, Risk) Select CustomerID, Gender, Income, Profession,Risk From Customers Select NewCustomers.CustomerID, CreditRisk.Risk, PredictProbability(CreditRisk.Risk) FROM CreditRisk PREDICTION JOIN NewCustomers ON CreditRisk.Gender=NewCustomer.Gender AND CreditRisk.Income=NewCustomer.Income AND CreditRisk.Profession=NewCustomer.Profession
  • 25.
  • 26. Data Mining Interfaces – XMLA ++ XMLA Over TCP/IP XMLA Over HTTP Analysis Server (msmdsrv.exe) OLAP Data Mining Server ADOMD.NET .Net Stored Procedures Microsoft Algorithms Third Party Algorithms OLEDB for OLAP/DM ADO/DSO Any Platform, Any Device C++ App VB App .Net App AMO Any App ADOMD.NET WAN DM Interfaces
  • 27.
  • 28.
  • 29. DM Webcasts Fri, 02 Nov 2007 MSDN Webcast: Build Smart Web Applications with SQL Server Data Mining (Level 200) Thu, 08 Nov 2007 MSDN Webcast: Building Adaptive Applications with SQL Server Data Mining (Level 300) Mon, 19 Nov 2007 MSDN Webcast: Extending and Customizing SQL Server Data Mining (Level 300) Fri, 30 Nov 2007 MSDN Webcast: Creating Visualizations for SQL Server Data Mining (Level 300) Thu, 01 Nov 2007 TechNet Webcast: Deliver Actionable Insight Throughout Your Organization with Data Mining (Part 1 of 3): Your First Project with SQL Server Data Mining (Level 200) Thu, 15 Nov 2007 TechNet Webcast: Deliver Actionable Insight Throughout Your Organization with Data Mining (Part 2 of 3): Understand SQL Server Data Mining Add-ins for the 2007 Office System (Level 200) Thu, 29 Nov 2007 TechNet Webcast: Deliver Actionable Insight Throughout Your Organization with Data Mining (Part 3 of 3): Use Predictive Intelligence to Create Smarter KPIs (Level 200)
  • 30. BI Resources from Lynn Langit 1. “Foundations of SQL Server 2005 Business Intelligence” (published by APress in April 2007) 2. http://blogs.msdn.com/SoCalDevGal 3. “Building Business Intelligence Solutions with SQL Server 2008” (MSPress Fall 2008)
  • 31. DM Resources Technical Communities, Webcasts, Blogs, Chats & User Groups http://www.microsoft.com/communities/default.mspx Microsoft Developer Network (MSDN) & TechNet http://microsoft.com/msdn http://microsoft.com/technet Trial Software and Virtual Labs http://www.microsoft.com/technet/downloads/trials/default.mspx Microsoft Learning and Certification http://www.microsoft.com/learning/default.mspx SQL Server Data Mining http://www.sqlserverdatamining.com http://www.microsoft.com/bi/bicapabilities/data-mining.aspx