Introduction to some basic concepts of Data Mining

1. Introduction to Data Mining with SQL Server Hugo Olivera Alonso

3. Secure and personalized

4. Collaborative

6. Data transformation and synthesis

7. Data enrichment, with business logic, hierarchical views

8. Data discovery via data mining

9. Data presentation and distribution

11. SQL Server Integration Services shows up to 400% performance improvement

12. Deliver more meaningful, up-to-date info

13. End to end business intelligence with: SQL Server, Reporting Services, Analysis Services, Integration Services and Data Mining

14. 1.6 TB Raw Data, 64-bit, 20-way server

15. Instances of store out of stock

16. Inappropriate stock levels

17. Difficult to perform analysis of large data volumes

19. Explores Your Data Performs Predictions Finds Patterns What does Data Mining Do?

20. Comparative BenefitsPredictive Projects versus Nonpredictive Projects

22. Target developer audience

24. Database object

25. Client/Server

26. Security Model

27. Backup/Restore

28. Specified Access

29. OLEDB  ADO, ADO.Net

32. Comprehensive feature set

33. Build data mining ecosystem

35. Target developer audience

37. Full suite of algorithms to automatically extract information from your data

39. Data Mining algorithms learn business rules directly from the data, freeing you from trying discover and code them yourselves

40. Customized for each client

43. Creation and exploration environment

44. Data Mining projects inside Visual Studio solutions with related projects

45. Source Control Integration

46. SQL Server Management Studio

47. Single place for management of all SQL Server technologies

49. Data Mining processing and results integrate directly into the operational pipeline

50. OLAP

51. Process Mining Models from Unified Dimensional Models (Cubes), use learned content to slice cubes based on data-specific patterns

52. Reporting Services

54. Classification

55. Regression

56. Segmentation orClustering

57. Association

58. Forecasting

59. Text Analysis

62. OLEDB, ADO, ADO.Net, ADOMD.Net, XMLADim cmd as ADOMD.Command Dim reader as ADOMD.DataReader Cmd.Connection = conn Set reader = Cmd.ExecuteReader(“Select Predict(Gender)…”)

63. Data Mining Programmability Data Mining Object Model Analysis Management Objects (AMO) ADOMD.Net, Server ADOMD.Net Direct access to Mining content CLR User Defined Procedures execute on the server Expandability Plug-In Algorithms Plug-In Viewers

64. Model Browsing LOB Application Reporting Historical Dataset Data Transform (DTS) Prediction Mining Models Cube Cube DM data flow New Dataset

65. Resources SQL Server 2008 Data Mining Overview http://www.microsoft.com/sql/2008/datamining/index.html Microsoft SQL Server 2008 Web Site http://www.microsoft.com/sqlserver/2008/en/us/data-mining.aspx SQL Server 2008 Books Online http://msdn.microsoft.com/en-us/library/bb510517.aspx SQL Server Data Mining Community Site http://www.sqlserverdatamining.com/ MSDN Forum for SQL Server Data Mining http://forums.microsoft.com/MSDN/ShowForum.aspx?ForumID=81&SiteID=1

66. No damoscursos Transferimosconocimiento www.btskills.com cursos@btskills.com consultas@btskills.com