Data mining and data warehousing have evolved since the 1960s due to increases in data collection and storage. Data mining automates the extraction of patterns and knowledge from large databases. It uses predictive and descriptive models like classification, clustering, and association rule mining. The data mining process involves problem definition, data preparation, model building, evaluation, and deployment. Data warehouses integrate data from multiple sources for analysis and decision making. They are large, subject-oriented databases designed for querying and analysis rather than transactions. Data warehousing addresses the need to consolidate organizational data spread across various locations and systems.

1. Yogesh Benawat Sameer Deshmukh

2. Outline Data Mining Data Warehousing Q ‘n’ A Conclusion

3. 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

4. Data Mining

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

6. 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

7. Data Mining Models Predictive Model Descriptive Model

8. 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

9. 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

10. Data mining process Fig. General Phases of 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

11. 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

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

13. Summary Need of data mining Data mining models Process of data mining Some applications

14. Data Warehousing

15. 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

16. 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 ???

17. 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

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

19. Data Warehousing Data Warehouse - Architecture

20. 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……

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

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

23. 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/

24. Q ‘n’ A

25. Thank You!