2. Data Mining & OLAP

3. What is Data Mining? Data Mining is the set of activities used to find new, hidden, or unexpected patterns in data.

4. What is OLAP? On-Line Analytical Processing a category of software technology that enables analysts and executives to gain insight to data through fast, consistent, interactive access to a wide variety of possible views of information that has been transformed from raw data to reflect the real dimensionality of the enterprise as understood by the user.

5. OLAP Functionalities dynamic multi-dimensional analysis of consolidated data supporting end user analytical and navigational activities including:  Calculations and modeling applied across dimensions, through hierarchies and/or across members  Trend analysis over sequential time periods  Slicing subsets for on-screen viewing  Drill down to deeper levels of consolidation  Reach-through to underlying detail data  Rotation to new dimensional comparisons in the viewing area

6. 2 Approaches to conduct the analysis Multidimensional OLAP (MOLAP) Hypercube Relational OLAP (ROLAP) In ROLAP, multidimensional database server is replaced with a large relational database server

7. Internal data External data Data Transformation services Mapping measures and dimensions Transaction database Data warehouse Multidimensional cube End User OLAP Interface OLTP Components of OLAP

8. Infrastructure of Data Warehouses & OLAP Systems

9. Hypercube data representations make it convenient to query data along any dimension

10. Sales Performance from Various Markets Country

11. Drill Down Operation of OLAP Cube Country > Region

12. Drill Down Operation of OLAP Cube Country > Region> City

13. Workflow Monitoring

14. CompanyCustomer Customer Sales & Marketing Manufacturing PMCShipper Accounting Warehouse purchase order order request approval joborder delivery note shipping order invoice payment purchaseconfirmation Schematic Diagram of Business Flow

15. Sample Workflow for Electronic Procurement - Participating Organizations SupplierBuyer User Invoice Approver PO Approver Commerce FinanceSupplierReviewerShipper Purchase Request PO Request Approval PO Approval PurchaseOrder Configuration Review Purchase Confirmation and ETA Shipping Order Invoice Invoice Request Approval Invoice Approval Payment App Shipment Verification

16. Management And Monitoring Process SQL SERVER Handle Approval Query Receive Approval Status Update Approve Email User Change Status Call Validate Schedule Biztalk Custom

17. Orchestrating Business Activities BizTalk Orchestration Engine COM Components Web Service (Internal) Web Service (External) MSMQ Exchange Workflows SQL Server Script Files BizTalk Messaging Services Internal Apps

18. Business Orchestration Business Process Flow Implementation

19. BizTalk Server- An Integration Server MS BizTalk Server Scan-based Trading Inventory Management BOM Module PO Module CO Module Other Modules Other Legacy Systems Customers Suppliers ECTools

20. BizTalk Server - An Automation Server MS BizTalk Server Scan-based Trading Inventory Management System Customer Accounting System Begin Receive Inventory Record Issue Delivery Note Update Inventory Record Credit or COD Customer Issue InvoiceCOD Credit customers’ account Receive Payment Account Accounting System End Pre-defined Business Rule could be added for process automation Support various types of protocol for messaging Data format conversion for different formats ECTools

21. Questions for Discussion Determine the potential OLAP applications in business operation? Suggested Answer:  Marketing and sales analysis  Database marketing  Budgeting  Financial reporting  Management reporting  Profitability analysis  Quality analysis

22. Questions for Discussion MOLAP is good for handling what kind of data? Suggested Answer:  MOLAP is good at handling summarized data, it is not particularly well-suited to handle large amount of detailed data

23. Questions for Discussion ROLAP is suitable for handling what kind of data? Suggested Answer:  ROLAP architectures are especially well-suited to those situations where dynamic access to combinations of summarized and detailed data is more important than the performance gains offered by MOLAP approach using only summarized or pre-consolidated data.

24. Questions for Discussion Limitations and Challenges to Data Mining Suggested Answer:  Identification of missing information  Original data set contains the necessary elements for effective mining cannot be detected yet  Data noise and missing values  Large databases and high dimensionality