Databases & Data Mining CPS 181s April 3, 2003

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

What is a Database?

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

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

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

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

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

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

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

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

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)

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

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

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

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/ FulcherCleo50$450011/30/ Fields Records (tuples)

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

Relational Database Lname FnameAgeSalaryEmploy Date Employ number NelsonWilliams45$50006/1/ FulcherCleo50$450011/30/ HR Table Dept. Team Member Team Position Employ number .com yesPitcher com no0002 Softball Team Table Key Field

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

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

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

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

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