1. © Stefano Grazioli - Ask for permission for using/quoting: grazioli@virginia.edu

2.  Doing Well  Easy meter

3.  Name, major  Learning objectives  Things you like about the class  Things that can be improved  Attitude towards the Tournament

4. © Stefano Grazioli - Ask for permission for using/quoting: grazioli@virginia.edu

5. Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id Legend “zero/none” “one” “many”

6. Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id

7. Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id

8. Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id

9. Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id

10.  Directly related to business operations  Single customer, single contract, deal, service…  “Real time”  Often INSERTs  “Small” amount of data  Large numbers of fast, “simple” queries

11. Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id

12. Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id

13. Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id

14.  Relate to decision making more than business operations  Aggregate customers, contracts, deals, services…  Not necessarily “Real time”  Mostly Selects  “Large” amount of data  Small number of “large”, “complex” queries

15. Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id

16.  Questions relate to decision making, more than business operations  SQL provides the input, but is not sufficient. Require additional software (SPSS, SAS, R, Data miner…)  More similar to BI queries than operational queries.

17.  Idea: create a copy of the data and perform analysis on it  The DB that contains this offline data is called a Data Warehouse (aka data mart, data hub…)

18. Source: TDWI Smart Companies Report 2003 + sg edits Transactional (Ops) Right now, individual, action Informational (BI/Analytics) Historical, aggregate, decision O PERATIONAL ENVIRONMENT

19. © Stefano Grazioli - Ask for permission for using/quoting: grazioli@virginia.edu