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SQL Server 2008 for Business Intelligence<br />UTS Short Course<br />
Peter Gfader<br />Specializes in <br />C# and .NET (Java not anymore)<br />TestingAutomated tests<br />Agile, ScrumCertifi...
Admin Stuff<br />Attendance<br />You initial sheet<br />Hands On Lab<br />You get me to initial sheet<br />Homework<br />C...
Course Website<br />Course Timetable & Materials<br />http://www.ssw.com.au/ssw/Events/2010UTSSQL/<br />Resources<br />htt...
Course Overview<br />
Last week(s)<br />Other cube browsers<br />Microsoft Data Analyzer<br />Proclarity<br />Excel 2003/2007/2010<br />Excel se...
Create report on top of Northwind<br />Top 10 customers (Table)<br />Top 10 products (Table)<br />Top 10 employees (Table)...
The plan<br />
Step by step to BI<br />Create Data Warehouse<br />Copy data to data warehouse <br />Create OLAP Cubes<br />Create Reports...
Agenda<br />What is Data Mining?<br />Why?<br />Uses<br />Algorithms<br />Demo<br />Hands on Lab<br />
What is Data Mining?<br />“Data mining is the use of powerful software tools to discover significant traits or relationshi...
What is Data Mining?<br />It exploits statistical algorithms <br />Once the “knowledge” is extracted it:<br />Can be used ...
Why Data Mining?<br />Marketing<br />Who picks the movie? The kids, the wife, me<br />Who are our Customers and what sort ...
Get new information from data, future trends, past trends, outlier, maximums, minimums<br />Analyse data from different pe...
Who are our biggest customers?<br />What are customers buying with cigars?<br />What are the customer retention levels of ...
Ad hoc query<br />Drill through to details<br />Business Intelligence tool<br />What’s not data mining<br />
<ul><li>Huge amount of data
Good raw material  good data mining</li></ul>Samples should be representative<br />Samples "similar" to domain<br />Not a...
OLAP<br />Is about fast ad hoc querying<br />Analysis by dimensions and measures<br />Gives precise answers<br />Data Mini...
Classification algorithms <br />predictone or more discrete variables, based on the other attributes in the dataset<br />R...
Clustering<br />Time Series<br />Decision Trees<br />Naïve Bayes<br />Association<br />Linear Regression<br />Complete Set...
Split data<br />Each of branch is like an attribute<br />Brightness = amount of data<br />Decision trees<br />
Decision Trees (1)<br />Decision Trees assign (classify) each case to one of a few (discrete) broad categories of selected...
Decision Trees (2)<br />The algorithm tries all possible breaks in classes using all possible values of each input attribu...
Decision Trees (3)<br />Decision trees are used for classification and prediction<br />Typical questions:<br />Predict whi...
Decision Trees – Who Decides<br />
Naïve Bayes<br />Bayes Formula<br />Uses statistics to say falls into certain category or not with probability<br />Spam f...
Naïve Bayes<br />Quickly builds mining models that can be used for classification and prediction<br />It calculates probab...
Cluster Analysis (1)<br />Grouping data into clusters<br />Objects within a cluster have high similarity based on the attr...
Cluster Analysis (2)<br />Segments a heterogeneous population into a number of more homogenous subgroups or clusters<br />...
Clustering<br />Annual <br />Income<br />Age<br />
Time series<br />Timebaseddata  prediction<br />
Sequence clustering<br />Numbers orders stronger associations<br />Direction of association (not necessary the other direc...
If you own certain stocks ' you own maybe other ones as well<br />Probability = thickness of line<br />Association<br />
Let system learn how to classify data<br />Neural Network adapts to the new data<br />Formulate statement/hypothesis<br />...
Both have directions<br />Sequence Clustering has probability number and colour<br />They are very similar. The difference...
Conclusion: When To Use What<br />
Visual Numerics<br />3rd party algorithms<br />http://www.vni.com/company/whitepapers/                              Micros...
Excel Data Mining<br />Microsoft SQL Server 2008 Data Mining Add-ins for Microsoft Office 2007<br />http://www.microsoft.c...
Train station / airport <br />Who is the bad guy<br />Farmers <br />Find the best crops<br />Supermarket <br />Find to fig...
SSIS 2008 - Data profiling task<br />Get a profile of the data in a table <br />potential candidate keys<br />length of da...
Video: Simple data mining model<br />http://www.sqlservercentral.com/articles/Video/65055/<br />Video: Data mining and Rep...
Jamie MacLennan<br />http://blogs.msdn.com/b/jamiemac/<br />Richard Lees on BI<br />http://richardlees.blogspot.com/<br />...
Summary<br />Why Data Mining?<br />Uses<br />Algorithms<br />Demo<br />Hands on Lab<br />
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Data Mining with SQL Server 2008

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What is Data Mining?
Why?
Discovering relationships
Predict future events
Usage scenarios
Algorithms

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Data Mining with SQL Server 2008

  1. 1. SQL Server 2008 for Business Intelligence<br />UTS Short Course<br />
  2. 2. Peter Gfader<br />Specializes in <br />C# and .NET (Java not anymore)<br />TestingAutomated tests<br />Agile, ScrumCertified Scrum Trainer<br />Technology aficionado <br />Silverlight<br />ASP.NET<br />Windows Forms<br />
  3. 3. Admin Stuff<br />Attendance<br />You initial sheet<br />Hands On Lab<br />You get me to initial sheet<br />Homework<br />Certificate <br />At end of 5 sessions<br />If I say if you have completed successfully <br />
  4. 4. Course Website<br />Course Timetable & Materials<br />http://www.ssw.com.au/ssw/Events/2010UTSSQL/<br />Resources<br />http://sharepoint.ssw.com.au/Training/UTSSQL/<br />
  5. 5. Course Overview<br />
  6. 6. Last week(s)<br />Other cube browsers<br />Microsoft Data Analyzer<br />Proclarity<br />Excel 2003/2007/2010<br />Excel services<br />Thinslicer<br />Performance Point<br />Power Pivot<br />
  7. 7. Create report on top of Northwind<br />Top 10 customers (Table)<br />Top 10 products (Table)<br />Top 10 employees (Table)<br />1 chart that shows the top 10 customers<br />1 usage of the gauge control (surprise me)<br />Homework<br />
  8. 8. The plan<br />
  9. 9. Step by step to BI<br />Create Data Warehouse<br />Copy data to data warehouse <br />Create OLAP Cubes<br />Create Reports<br />Browse the cube<br />Do some Data Mining<br />Discovering relationships<br />Predict future events<br />
  10. 10. Agenda<br />What is Data Mining?<br />Why?<br />Uses<br />Algorithms<br />Demo<br />Hands on Lab<br />
  11. 11. What is Data Mining?<br />“Data mining is the use of powerful software tools to discover significant traits or relationships,from databases or data warehouses and often used to predict future events”<br />
  12. 12. What is Data Mining?<br />It exploits statistical algorithms <br />Once the “knowledge” is extracted it:<br />Can be used to discover<br />Can be used to predict values of other cases<br />
  13. 13. Why Data Mining?<br />Marketing<br />Who picks the movie? The kids, the wife, me<br />Who are our Customers and what sort of films do they hire?<br />Is a 30 year old woman with 2 children going to hire Arnie’s latest film<br />Validation<br />Is this data sensible? Terminator 2 and Toy Story<br />Prediction<br />Sales Next Year<br />
  14. 14. Get new information from data, future trends, past trends, outlier, maximums, minimums<br />Analyse data from different perspectives and summarizing it into useful information<br />New information to<br />increase revenue<br />cuts costs<br />or both :-)<br />Why? Its all about money<br />
  15. 15. Who are our biggest customers?<br />What are customers buying with cigars?<br />What are the customer retention levels of our branches?<br />Which customers have bought olives, feta cheese but no ciabatta bread?<br />Which regions have the highest male/female ratio of single 20 somethings?<br />Which region has lowest customer retention levels and list out lost customers?<br />Which Questions are Data Mining?<br />
  16. 16. Ad hoc query<br />Drill through to details<br />Business Intelligence tool<br />What’s not data mining<br />
  17. 17. <ul><li>Huge amount of data
  18. 18. Good raw material  good data mining</li></ul>Samples should be representative<br />Samples "similar" to domain<br />Not all-seeing crystal ball<br />Verify and Validate!<br />Data - Uncover patterns in samples<br />
  19. 19. OLAP<br />Is about fast ad hoc querying<br />Analysis by dimensions and measures<br />Gives precise answers<br />Data Mining<br />May use RDBMS or OLAP source<br />Is about discovering and predicting<br />Gives imprecise answers<br />OLAP is not a prerequisite for data mining, but it almost always comes first<br />OLAP versus Data Mining<br />(learning to ride a bike before a car)<br />
  20. 20. Classification algorithms <br />predictone or more discrete variables, based on the other attributes in the dataset<br />Regression algorithms <br />predictone or more continuous variables, such as profit or loss, based on other attributes in the dataset<br />Segmentation algorithms <br />dividedata into groups, or clusters, of items that have similar properties<br />Association algorithms <br />find correlations between different attributes in a dataset<br />Sequence analysis algorithms <br />summarize frequent sequences or episodes in data, such as a Web path flow<br />Types of Data Mining Algorithms<br />
  21. 21. Clustering<br />Time Series<br />Decision Trees<br />Naïve Bayes<br />Association<br />Linear Regression<br />Complete Set Of AlgorithmsWays to analyze your data<br />Neural Network<br />Sequence Clustering<br />Logistic Regression<br />
  22. 22. Split data<br />Each of branch is like an attribute<br />Brightness = amount of data<br />Decision trees<br />
  23. 23. Decision Trees (1)<br />Decision Trees assign (classify) each case to one of a few (discrete) broad categories of selected attribute (variable) and explains the classification with few selected input variables<br />The process of building is recursive partitioning – splitting data into partitions and then splitting it up more<br />Initially all cases are in one big box <br />
  24. 24. Decision Trees (2)<br />The algorithm tries all possible breaks in classes using all possible values of each input attribute; it then selects the split that partitions data to the purest classes of the searched variable<br />Several measures of purity<br />Then it repeats splitting for each new class<br />Again testing all possible breaks<br />Unuseful branches of the tree can be pre-pruned or post-pruned<br />
  25. 25. Decision Trees (3)<br />Decision trees are used for classification and prediction<br />Typical questions:<br />Predict which customers will leave<br />Help in mailing and promotion campaigns<br />Explain reasons for a decision<br />What are the movies young female customers like to buy?<br />
  26. 26. Decision Trees – Who Decides<br />
  27. 27. Naïve Bayes<br />Bayes Formula<br />Uses statistics to say falls into certain category or not with probability<br />Spam filtering: score of spam (Bayes)<br />Testing only a particular attribute<br />
  28. 28. Naïve Bayes<br />Quickly builds mining models that can be used for classification and prediction<br />It calculates probabilities for each possible state of the input attribute, given each state of the predictable attribute<br />This can later be used to predict an outcome of the predicted attribute based on the known input attributes <br />This makes the model a good option for exploring the data <br />
  29. 29. Cluster Analysis (1)<br />Grouping data into clusters<br />Objects within a cluster have high similarity based on the attribute values<br />The class label of each object is not known<br />Several techniques<br />Partitioning methods<br />Hierarchical methods<br />Density based methods<br />Model based methods<br />And more…<br />
  30. 30. Cluster Analysis (2)<br />Segments a heterogeneous population into a number of more homogenous subgroups or clusters<br />Some typical questions:<br />Discover distinct groups of customers<br />Identification of groups of houses in a city<br />In biology, derive animal and plant taxonomies<br />Find outliers<br />
  31. 31. Clustering<br />Annual <br />Income<br />Age<br />
  32. 32. Time series<br />Timebaseddata  prediction<br />
  33. 33. Sequence clustering<br />Numbers orders stronger associations<br />Direction of association (not necessary the other direction)<br />
  34. 34. If you own certain stocks ' you own maybe other ones as well<br />Probability = thickness of line<br />Association<br />
  35. 35. Let system learn how to classify data<br />Neural Network adapts to the new data<br />Formulate statement/hypothesis<br />Outcome is know<br />(Data / Surveys)<br />1. 70% data to train network (outcome is known)<br />2. 30% of data to test network (outcome is known)<br />3. New data (no survey needed, predict from network)<br />Other example: OCR <br />Neural Nets<br />
  36. 36. Both have directions<br />Sequence Clustering has probability number and colour<br />They are very similar. The difference is that Association analyses items that occur together whereas sequence clustering analyses items that follow one another.<br />An example is that Sequence Clustering might be used by credit card companies to spot fraud, e.g. a petrol station refill followed by another petrol station refill followed by a big purchase = fraud (different transactions)<br />Whereas Association will be more like: when someone buys popcorn at the cinemas, they also buy a drink (same transaction)<br />Difference between algorithms: Association and Sequence<br />
  37. 37. Conclusion: When To Use What<br />
  38. 38. Visual Numerics<br />3rd party algorithms<br />http://www.vni.com/company/whitepapers/ MicrosoftBIwithNumericalLibraries.pdf<br />There is more...<br />
  39. 39. Excel Data Mining<br />Microsoft SQL Server 2008 Data Mining Add-ins for Microsoft Office 2007<br />http://www.microsoft.com/downloads/en/details.aspx?familyid=896A493A-2502-4795-94AE-E00632BA6DE7&displaylang=en<br />
  40. 40. Train station / airport <br />Who is the bad guy<br />Farmers <br />Find the best crops<br />Supermarket <br />Find to figure out how to get you to buy more, where the expensive items<br />Other usages of data miningFind patterns - Profiling<br />
  41. 41. SSIS 2008 - Data profiling task<br />Get a profile of the data in a table <br />potential candidate keys<br />length of data values in columns<br />Null percentage of rows<br />distribution of values<br />....<br />Tip<br />
  42. 42. Video: Simple data mining model<br />http://www.sqlservercentral.com/articles/Video/65055/<br />Video: Data mining and Reporting Services<br />http://www.sqlservercentral.com/articles/Video/64190/<br />Data Mining Algorithms<br />http://msdn.microsoft.com/en-us/library/ms175595.aspx<br />Resources 1<br />
  43. 43. Jamie MacLennan<br />http://blogs.msdn.com/b/jamiemac/<br />Richard Lees on BI<br />http://richardlees.blogspot.com/<br />Book Data Mining with Microsoft SQL Server 2008<br />http://www.amazon.com/gp/product/0470277742?ie=UTF8&tag=sqlserverda09-20&linkCode=as2&camp=1789&creative=9325&creativeASIN=0470277742<br />Resources 2<br />
  44. 44. Summary<br />Why Data Mining?<br />Uses<br />Algorithms<br />Demo<br />Hands on Lab<br />
  45. 45. 3things…<br />PeterGfader@ssw.com.au<br />http://blog.gfader.com/<br />twitter.com/peitor<br />
  46. 46. Thank You!<br />Gateway Court Suite 10 81 - 91 Military Road Neutral Bay, Sydney NSW 2089 AUSTRALIA <br />ABN: 21 069 371 900 <br />Phone: + 61 2 9953 3000 Fax: + 61 2 9953 3105 <br />info@ssw.com.auwww.ssw.com.au <br />

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