STUDIEREN<br />UND DURCHSTARTEN.<br />Author I:	M.Sc. Johannes Hofmeister<br />Author II:	Dip.-Inf. (FH) Johannes Hoppe<br...
Basics – Adventure Works<br />Author I:	M.Sc. Johannes Hofmeister<br />Author II:	Dip.-Inf. (FH) Johannes Hoppe<br />Date:...
01<br />Adventure Works<br /> Slide 3<br />
Resources<br />Microsoft Visual Studio 2008(NOT 2010)<br />SQL Server 2008 (NOT Express Edition)<br />MSSQL Server Communi...
Adventure Works<br />Example Database of fictional companynamed „Adventure Works“<br />SSAS Integration (SQL Server Analys...
Available Scenarios<br />DM/DW Scenarios<br />Mining Szenarios<br />Forecasting<br />Bikes by Region/Time<br />Targeted Ma...
Available Scenarios<br />OLAP Scenarios<br />Financial Reporting<br />Actual versus Budget<br />ProductProfitability Analy...
Adventure Works Data Warehouse<br />Data from OLTP DB + Additional „External“ Datasource<br />Synchronization via availabl...
02<br />Simple Datamining with View<br /> Slide 9<br />
Homework!<br /> Slide 10<br />
Data Mining Applied with AW DB<br />Read andtryit out!!!<br />Preparation<br />1. Get Visual Studio 2008<br />2. Get SQL S...
Data Mining Applied with AW DB<br />Don‘tgetconfused*<br />“SQL Server Business Intelligence Development Studio”<br />is t...
A lookintothedatabase<br />Adventure Works 2008<br />AdventureWorksDW2008<br />13<br />ProductCategory<br />vDMPrep<br />v...
Table: ProductCategory<br />14<br />Id		  Name   rowguidModified<br />--- ------------ ------------------- -----------<br ...
View: vTargetMail<br />15<br />-- vTargetMail supports targeted mailing data model<br />-- Uses vDMPrep to determine if a ...
Create Project<br />Add Source<br />Add Source View<br />Add Mining Structure<br />Add Models (Algorithms)<br />DecisionTr...
03<br />Algorithm: Decision Tree<br /> Slide 17<br />
AlgorithmOverview<br />Used to identify relationships<br />Column 1, Column 2, Column 3<br />Most cases: 4 Steps<br />Anal...
DecisionTrees<br />Also: ClassificationTrees<br />Partition Data<br />Can detect non-linear relationships<br />Machine Lea...
DecisionTrees: Example<br />20<br />Income > $30 000: 3,6 %<br />Male 3,0%<br />Income < $30 000: 2,3 %<br />2,6 % respose...
Pros andCons of DecisionTrees<br />21<br />Pros<br />Very flexible, white box Model<br />Occams Razor: Kiss – Keep it simp...
THANK YOU<br />FOR YOUR ATTENTION<br /> Slide 22<br />
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DMDW Lesson 02 - Basics with Adventure Works

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DMDW Lesson 02 - Basics with Adventure Works

  1. 1. STUDIEREN<br />UND DURCHSTARTEN.<br />Author I: M.Sc. Johannes Hofmeister<br />Author II: Dip.-Inf. (FH) Johannes Hoppe<br />Date: 25.02.2011<br />
  2. 2. Basics – Adventure Works<br />Author I: M.Sc. Johannes Hofmeister<br />Author II: Dip.-Inf. (FH) Johannes Hoppe<br />Date:25.02.2011<br />
  3. 3. 01<br />Adventure Works<br /> Slide 3<br />
  4. 4. Resources<br />Microsoft Visual Studio 2008(NOT 2010)<br />SQL Server 2008 (NOT Express Edition)<br />MSSQL Server Community Projects & Sampleshttp://www.codeplex.com/SqlServerSamples<br />Adventure Works Databasesfor SQL Server 2008http://msftdbprodsamples.codeplex.com/<br />Adventure Works Sample Data Warehouse Documentationhttp://technet.microsoft.com/en-us/library/ms124623(SQL.90).aspx<br />SQL Authority Adventure Works Tutorialhttp://blog.sqlauthority.com/2008/08/10/sql-server-2008-download-and-install-samples-database-adventureworks-2005-detail-tutorial/<br />
  5. 5. Adventure Works<br />Example Database of fictional companynamed „Adventure Works“<br />SSAS Integration (SQL Server Analysis Services)<br />Finance<br />Franchises<br />Currency Rates (daily exchange rates)<br />Sales<br />Reseller<br />Contracts<br />5<br />
  6. 6. Available Scenarios<br />DM/DW Scenarios<br />Mining Szenarios<br />Forecasting<br />Bikes by Region/Time<br />Targeted Mailing Campaign<br />Algorithmsfordemographicdata<br />Age, Region, Volume, etc.<br />Market Basked Analysis<br />„suggesting a product“<br />Sequence Clustering<br />6<br />
  7. 7. Available Scenarios<br />OLAP Scenarios<br />Financial Reporting<br />Actual versus Budget<br />ProductProfitability Analysis<br />Sales Force Performance<br />Trend/Growth Analysis<br />Promotion Effectiveness<br />Source: http://msdn.microsoft.com/en-us/library/ms124623.aspx<br />7<br />
  8. 8. Adventure Works Data Warehouse<br />Data from OLTP DB + Additional „External“ Datasource<br />Synchronization via available SSIS Packages<br />Copy of actual (live) data<br />Can bechanged, mergedformining<br />8<br />
  9. 9. 02<br />Simple Datamining with View<br /> Slide 9<br />
  10. 10. Homework!<br /> Slide 10<br />
  11. 11. Data Mining Applied with AW DB<br />Read andtryit out!!!<br />Preparation<br />1. Get Visual Studio 2008<br />2. Get SQL Server 2008<br />3. InstallAdventure Works Database (DW)<br />Homework<br />http://msdn.microsoft.com/en-us/library/ms167167.aspx<br />
  12. 12. Data Mining Applied with AW DB<br />Don‘tgetconfused*<br />“SQL Server Business Intelligence Development Studio”<br />is the combination of<br />Microsoft Visual Studio 2008<br />+ SQL Server 2008 (not Express)<br />+ with Feature “Business Intelligence”<br />(*For the first time everybody is confused here! )<br />
  13. 13. A lookintothedatabase<br />Adventure Works 2008<br />AdventureWorksDW2008<br />13<br />ProductCategory<br />vDMPrep<br />vTargetMail<br />
  14. 14. Table: ProductCategory<br />14<br />Id Name rowguidModified<br />--- ------------ ------------------- -----------<br />1 Bikes CFBDA25C-DF71-[...] 1998-06-01 <br />2 Components C657828D-D808-[...] 1998-06-01 <br />3 Clothing 10A7C342-CA82-[...] 1998-06-01 <br />4 Accessories 2BE3BE36-D9A2-[...] 1998-06-01<br />
  15. 15. View: vTargetMail<br />15<br />-- vTargetMail supports targeted mailing data model<br />-- Uses vDMPrep to determine if a customer buys a bike and joins to DimCustomer<br />CREATE VIEW [dbo].[vTargetMail] <br />AS<br /> SELECT c.[CustomerKey], -- [...]<br />CASE x.[Bikes] WHEN 0 THEN 0 ELSE 1 END AS [BikeBuyer]<br />FROM [dbo].[DimCustomer] c <br />INNER JOIN(SELECT [CustomerKey],[Region],[Age]<br />,Sum(CASE [EnglishProductCategoryName] <br />WHEN 'Bikes' THEN 1 <br /> ELSE 0 END) AS [Bikes]<br />FROM [dbo].[vDMPrep] <br />GROUP BY [CustomerKey],[Region],[Age]) AS [x]<br />ON c.[CustomerKey] = x.[CustomerKey];<br />GO<br />
  16. 16. Create Project<br />Add Source<br />Add Source View<br />Add Mining Structure<br />Add Models (Algorithms)<br />DecisionTrees<br />(Clustering)<br />(NaiveBayes)<br />16<br />
  17. 17. 03<br />Algorithm: Decision Tree<br /> Slide 17<br />
  18. 18. AlgorithmOverview<br />Used to identify relationships<br />Column 1, Column 2, Column 3<br />Most cases: 4 Steps<br />Analyze<br />Create Model (Training)<br />Verify Model (Testing)<br />Predict Future Data<br />18<br />
  19. 19. DecisionTrees<br />Also: ClassificationTrees<br />Partition Data<br />Can detect non-linear relationships<br />Machine Learning Technique<br />Sepearateinto Training andTestingset<br />Training setiscreatedtocreate model based on certaincriteria<br />Test setisusedtoverifythe model<br />19<br />
  20. 20. DecisionTrees: Example<br />20<br />Income > $30 000: 3,6 %<br />Male 3,0%<br />Income < $30 000: 2,3 %<br />2,6 % respose rate<br />Age > 40: 3,8%<br />Female 2,9%<br />Age < 40: 3,2 %<br />TrainedTree<br />Males: $30 000<br />Response Rate: > 3,5 %<br />Female: 40+<br />
  21. 21. Pros andCons of DecisionTrees<br />21<br />Pros<br />Very flexible, white box Model<br />Occams Razor: Kiss – Keep it simple, stupid!<br />Little preparation and resources needed<br />Cons<br />Can be tuned until death<br />Long time to build<br />Wisley select training data<br />False training yields false results<br />Big tree might require disk swapping <br />
  22. 22. THANK YOU<br />FOR YOUR ATTENTION<br /> Slide 22<br />

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