1. Data Mining and Data Warehousing – a connected view

2. Introduction Data mining describes a collection of techniques that aim to find useful but undiscovered patterns in collected data The goal of data mining is to create models for decision-making that predict future behavior based on analysis of past activity

3. Introduction Data warehousing is a blend of technologies aimed at the effective integration of operational databases into an environment that enables the strategic use of data. These technologies include relational and multidimensional database management systems, client/server architecture, metadata modeling and repositories, graphical user interfaces, and much more.

4. Operational vs Informational Databases

5. Table 2-1 Operational Versus informational Databases

6. Operational vs Informational Databases

7. Table 2-2 Comparison of Data Stores, and Data Warehouses

8. Definition and characteristics of a data warehouse It’s a database designed for analytical tasks It supports a relatively small number of users Its usage is read-intensive Its content is periodically updated (mostly additions) It contains current and historical dta It contains a few large tables Each query frequently results in a large result set and involves frequent full table scan and multi-table joins A formal definition of the data warehouse is offered by W.H. Inmon –A data warehouse is a subject-oriented, integrated, time-variant, non-volatile collection of data in support of management decisions

9. Data warehouse architecture

10. Figure 2-1 Data Warehouse Environment

11. Data warehouse architecture

12. Figure 2-1 Data Warehouse and Data Operational Data Store

13. Data warehouse architecture

14. Figure 2-3 Two-tiered Data WarehouseArchitecture

15. Data warehouse architecture

16. Figure 2-4 Multi-tiered Data WarehouseArchitecture

17. Data mining defined Data mining as the process of discovering meaningful new correlations, patterns, and trends by digging into (mining) large amounts of data stored in warehouse. The major attraction of data mining is its capability to build predictive rather than retrospective models

18. Predictive versus Retrospective Models

19. Table 2-3 Predictive Versus Retrospective Models

20. Data Mining application Domain Customer retention Sales and customer service Marketing Risk Assessment and Fraud Detection

21. Data Mining Categories and Research Focus Data mining techniques deal with discovery and learning, and as such fall into three major learning modes: supervised, unsupervised, and reinforcement learning Data mining techniques can be categorized: –Representation of models and results –The type of data the techniques operates on –Application type –Pattern attributes

22. Data Mining Categories and Research Focus Data mining categorized by business problems –Retrospective Analysis –Predictive Analysis These two classes of business problems can be further classified by –Classification –Clustering/Segmentation –Associations –Sequencing

23. Data Mining Categories and Research Focus Approaches that underlie the most contemporary research in data mining: –The induction approach –The database querying approach –The compression approach –The approach of approximation and searching