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BIN06-IS Understanding the Data Mining Add-Ins for Excel 2007 Lynn Langit MSDN Developer Evangelist – Southern California http://blogs.msdn.com/SoCalDevGal
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? Predictive Analytics Presentation Exploration Discovery Passive Interactive Proactive Role of Software Business Insight Canned reporting Ad-hoc reporting OLAP Data mining
Data Mining Problems
From Scenarios to Tasks
From Tasks to Techniques
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 2005 Analysis Services Information  Worker Data Mining Add-ins for  the 2007 Microsoft Office system Microsoft SQL Server 2005 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
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
Lifecycle – Understand & Prepare Excel Explore Data Clean Data Add-ins
Lifecycle - Prepare Excel Partition Data ADOMD.Net Add-ins SQL Server Analysis Services Data Source Data
Understand & Prepare specifics
Demo 1 – Explore / Clean / Partition Data
Lifecycle – Model & Evaluate Excel Modeling & Evaluation Add-ins SQL Server Analysis Services Data Source Data Mining Models
Modeling Specifics
Demo 3 – Modeling
Evaluation Specifics
Demo 4 – Evaluation
Data Mining – Logical Model Mining Model Mining Model Training Data DB data Client data Application data Data Mining Engine Data 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
Architecture Excel Modeling Query Add-ins SQL Server Analysis Services Data Source Data Mining Models
Architecture Excel Modeling Query Add-ins SQL Server Analysis Services Data Source Data Mining Models
Architecture Excel Modeling Query Add-ins SQL Server Analysis Services Data Source Data Mining Models
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]
Advanced Techniques - DMX
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
CREATE MINING MODEL CREATE MINING MODEL MyModel ( [CustID] LONG KEY, [Gender] TEXT  DISCRETE, [Marital Status] TEXT DISCRETE, [Education] TEXT DISCRETE, [Home Ownership] TEXT DISCRETE PREDICT, [Age] LONG CONTINUOUS, [Income] DOUBLE CONTINUOUS ) USING Microsoft_Decision_Trees
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],[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
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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
BI Resources from Lynn Langit Foundations of SQL Server 2005 Business Intelligence published by Apress in April 2007 Blog: http://blogs.msdn.com/SoCalDevGal
Q&A
[object Object],[object Object],Related Content
© 2007 Microsoft Corporation. All rights reserved. This presentation is for informational purposes only. MICROSOFT MAKES NO WARRANTIES, EXPRESS OR IMPLIED, IN THIS SUMMARY.

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Data Mining for Developers

  • 1. BIN06-IS Understanding the Data Mining Add-Ins for Excel 2007 Lynn Langit MSDN Developer Evangelist – Southern California http://blogs.msdn.com/SoCalDevGal
  • 2.
  • 3.
  • 4. What and Why Data Mining? Predictive Analytics Presentation Exploration Discovery Passive Interactive Proactive Role of Software Business Insight Canned reporting Ad-hoc reporting OLAP Data mining
  • 7. From Tasks to Techniques
  • 8. 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 2005 Analysis Services Information Worker Data Mining Add-ins for the 2007 Microsoft Office system Microsoft SQL Server 2005 Data Mining BI Analyst Custom Algorithms
  • 9. 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
  • 10. 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
  • 11. Lifecycle – Understand & Prepare Excel Explore Data Clean Data Add-ins
  • 12. Lifecycle - Prepare Excel Partition Data ADOMD.Net Add-ins SQL Server Analysis Services Data Source Data
  • 13. Understand & Prepare specifics
  • 14. Demo 1 – Explore / Clean / Partition Data
  • 15. Lifecycle – Model & Evaluate Excel Modeling & Evaluation Add-ins SQL Server Analysis Services Data Source Data Mining Models
  • 17. Demo 3 – Modeling
  • 19. Demo 4 – Evaluation
  • 20. Data Mining – Logical Model Mining Model Mining Model Training Data DB data Client data Application data Data Mining Engine Data To Predict Predicted Data Mining Model DB data Client data Application data “ Just one row ” Data Mining Engine
  • 21. 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
  • 22. Architecture Excel Modeling Query Add-ins SQL Server Analysis Services Data Source Data Mining Models
  • 23. Architecture Excel Modeling Query Add-ins SQL Server Analysis Services Data Source Data Mining Models
  • 24. Architecture Excel Modeling Query Add-ins SQL Server Analysis Services Data Source Data Mining Models
  • 25.
  • 26.
  • 28.
  • 29. 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
  • 30. CREATE MINING MODEL CREATE MINING MODEL MyModel ( [CustID] LONG KEY, [Gender] TEXT DISCRETE, [Marital Status] TEXT DISCRETE, [Education] TEXT DISCRETE, [Home Ownership] TEXT DISCRETE PREDICT, [Age] LONG CONTINUOUS, [Income] DOUBLE CONTINUOUS ) USING Microsoft_Decision_Trees
  • 31.
  • 32. 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
  • 33.
  • 34. 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
  • 35. BI Resources from Lynn Langit Foundations of SQL Server 2005 Business Intelligence published by Apress in April 2007 Blog: http://blogs.msdn.com/SoCalDevGal
  • 36. Q&A
  • 37.
  • 38. © 2007 Microsoft Corporation. All rights reserved. This presentation is for informational purposes only. MICROSOFT MAKES NO WARRANTIES, EXPRESS OR IMPLIED, IN THIS SUMMARY.