Conceptual Study about Data Warehousing and Mining.

1. Outline
 Data Mining
 Data Warehousing

2. Historical Perspective
 1960s:
 Data collection, database creation, IMS
and network DBMS
 1970s:
 Relational data model, relational DBMS
implementation
 1980s:
 RDBMS, advanced data models (extended-relational,
OO, deductive, etc.) and
application-oriented DBMS (spatial,
scientific, engineering, etc.)
 1990s—2000s:
 Data mining and data warehousing,
multimedia databases, and Web databases

3. Data Mining

4. Definition
 Data mining automates the process of locating and
extracting the hidden patterns and knowledge
 In simple words
 Searching for new knowledge

5. Why we need data mining
 Data explosion problem
 Automated data collection tools and mature database technology
lead to tremendous amounts of data stored in databases, data
warehouses and other information repositories
 We are drowning in data, but starving for knowledge!
 Solution: Data mining
 Data warehousing and on-line analytical processing
 Extraction of interesting knowledge (rules, regularities, patterns,
constraints) from data in large databases

6. Data Mining Models
 Predictive Model
 Descriptive Model

7. Predictive Model
 Prediction
 determining how certain attributes will behave in the future
 Regression
 mapping of data item to real valued prediction variable
 Classification
 categorization of data based on combinations of attributes
 Time Series analysis
 examining values of attributes with respect to time

8. Descriptive Model
 Clustering
 most closely data clubbed together into clusters
 Data Summarization
 extracting representative information about database
 Association Rules
 associativity defined between data items to form relationship
 Sequence Discovery
 it is used to determine sequential patterns in data based on
time sequence of action

9. Data mining process
Problem Definition
Creating Database
Exploring database
Preparation for creating a data mining model
Building Data Mining Model
Evaluation Phase
Deploying the Data Mining model
Fig. General Phases of Data Mining Process

10. Who needs data mining?
 Whoever has information fastest and uses it wins
 Don McKeough former president of Coke Cola
 Businesses are looking for new ways to let end users
find the data they need to:
 make decisions
 Serve customers
 Gain the competitive edge

11. Applications
 Business analysis and management
 Computer security
 Customer relationships analysis and management
 Telecommunication analysis and management
 News and entertainment
 Bioinformatics and Healthcare analysis

12. Summary
 Need of data mining
 Data mining models
 Process of data mining
 Some applications

13. Data Warehousing

14. Data Warehousing
 Data Warehouse
 What is Data Warehouse?
 Database & Data Warehouse.
 How to distinguish?
 Purpose
 Database : Transactional
 Data Warehouse :Intended for Decision Supporting
Applications.
 Functionality
 Optimized for data retrieval, not routine transaction
processing.
 Structure
 Performance

15. Data Warehousing
 Modern Organization’s needs ?
 Companies spread world wide.
 Have
 So many Data Sources
 Different Operational Systems
 Different Schemas
 Need Data for
 Complex Analysis
 Knowledge Discovery
 Decision Making.
Solution ???

16. Data Warehousing
 Solution…Data Warehouse.
 Data Warehouse . Definition ??
 No single definition….
 Data Warehouse
 Collection of Information gathered from multiple sources,
stored under unified schema, at a single site & mainly
intended for decision support applications.
 A subject oriented, integrated, nonvolatile, time-variant,
collection of data in support of management’s decision.
~ W.H. Inmon

17. Warehouses are Very Large
Databases
35%
30%
25%
20%
15%
10%
5%
0%
5GB
10-19GB 50-99GB 250-499GB
5-9GB
20-49GB 100-249GB 500GB-1TB
Initial
Projected 2Q96
Source: META Group, Inc.
Respondents

18. Data Warehousing
 Data Warehouse - Architecture

19. Data Warehousing
 Data Warehouse building
 When & how to gather data
 Source-driven architecture
 Destination-driven architecture
 What schema to use
 Data Cleansing
 Task of correcting and processing data
 How to propagate updates
 What data to summarize
 And many more……

20. Summary
 What is Data Warehousing?
 Data Warehouse.
 Data Warehouse – Architecture
 Data Warehouse vs. Data Mining

21. Conclusion
 Your data is full of undiscovered gems;
start digging!

22. References
 Data Mining Introductory and advanced Topics
Margaret H. Dunham
 Modern Data Warehousing, Mining, and visualization
George M. Marakas
 Data Mining
BPB Publications
 Database System Concepts
Silbershatz, Korth,
Sudarshan
 www.statoo.info/
 www.crm2day.com/
 www.trilliumsoftware.com/