1. Databases & Data Mining CPS 181s April 3, 2003

2. Databases in eCommerce The move to eCommerce is in part driven by the ability to gather data that benefits the business

3. What is a Database?

4.  A system that stores data  “Persistent” - exists beyond immediate use  Centralized storage  Single or multiple users

5. AdvantagesAdvantages  Reduces redundancy  Reduces inconsistency  Shared  Data representations standards can be enforced  Enables security restrictions

6. More Advantages  Integrity maintained  Valid cross-references between records  Allows data-independent applications  Applications ignorant of how data is stored

7. DBMSDBMS  Database Management System  Examples  Oracle  IBM DB2  Microsoft SQL Server  Sybase  MySQL

8. DBMS Features  Optimizes queries  Manage memory  Control concurrent data access

9. Client-Server Architecture  2-Tier architecture  Client  Application server & DBMS  Advantages  Rapid development  Mature tools  Less network traffic Server (Data Access) Client (User Interface) Business Rules

10. Client-Server Architecture  3-tier architecture  Client  Applications  DBMS  Database  Advantages  Distributed processing  Replication  Update multiple DBMS’s  Variety of data sources  Attach transaction priorities  Robust security Database Client Web Server DBMS HTTP URL HTML Data

11. Why Construct an eCommerce Database?  Time pressure of new economy business  Pace of data acquisition  Continuous quality improvement  Cost containment  Competitive advantage

12. eCommerce Data Systems  The collection, analysis, and discerning interpretation of data are essential for e-business to survive and flourish  A well designed data system can:  Increase market reach  Ensure regulatory compliance  Serve business processes  Help efficient use of resources  Spot emerging trends  Improve customer relations (CRM)

13. Database Technologies  Static webpages  HTML  Dynamic webpages  Client-side scripts (JavaScript)  Server-side includes (SSI markers)  Server-side scripts (JSP, CGI, ASP, PHP)

14. Database Construction Criteria  Flexibility and power  Developer expertise required  Development and testing time  Adaptability to change  Life-cycle costs  Operational risks  CPU overhead (computing resources consumed)  Compatibility

15. Types of Databases  Flat-file  Relational  Object-Oriented  Hybrid

16. Flat-File Database  Spreadsheets  Use columns and rows to organize small pieces of data into lists called tables  No metadata Lname FnameAgeSalaryEmploy Date Employ number NelsonWilliams45$50006/1/890001 FulcherCleo50$450011/30/890002 Fields Records (tuples)

17. Relational Database  Relations are two-dimensional data  Reduce data redundancy, duplication of effort, and storage space  Increase speed and versatility  Microsoft Access, IBM DB2, Oracle, Microsoft SQL Server, MySQL

18. Relational Database Lname FnameAgeSalaryEmploy Date Employ number NelsonWilliams45$50006/1/890001 FulcherCleo50$450011/30/890002 HR Table Dept. emailTeam Member Team Position Employ number 3Nelson@ email.com yesPitcher0001 1Fulcher@ email.com no0002 Softball Team Table Key Field

19. Object-Oriented Database  Data assigned to categories called classes  Each piece of data is an object  Limited query capabilities, but handle non-text data well because enables the creation of new data types  Store binary large objects efficiently

20. Hybrid Database  Object-relational systems  Handle both text and non-text data well  Thin object layer above the relational structures

21. What Can be Learned by Data Mining (patterns in large data)?  Characterization - sum characteristics  E.g. - traffic over lunch  Prediction - value of attribute based on relation to other attributes  E.g. - book orders based on location on Amazon’s welcome page  Class comparison - discover discrimination rules  E.g. - comparison of search engine results

22. What Can be Learned by Data Mining?…….  Association rules - one pattern implies another  E.g. - Lunch traffic and Dilbert site hits  Classification - learning models  E.g. - learn to recognize “fence sitters” and offer them a coupon  Time Series Analysis  E.g. - users who do X and then Y, usually do Z next

23. Web Mining  Web servers have ability to log all requests  Generate vast amounts of data - www.privacy.net/anonymizer www.privacy.net/anonymizer  Benefits of web log analysis  Facilitates personalization/adaptive sites  Learn about users  Improve site design  Predict user’s actions (allows prefetching)  Fraud/intrusion detection