1. Advanced Database Concepts
Unit 7

2. Key Concepts Data mining terminology Data warehousing terminology
Data mining goals
Data mining discovery
Applying data mining concepts
Data mining tools
Data warehousing terms
Data warehouse characteristics
Modeling and implementing a data warehouse.
Data warehouse functionality
Data warehouse issues

3. Data Mining
Discovery of new and often unexpected information from vast stores of information in the form of patterns or rules.

4. Data Mining and Data Warehousing
Data warehousing provides a data store as a source for making business decisions.
Data mining uses the data in the data warehouse to make focused decisions or look for patterns at affect decisions.

5. Data Mining Goals Prediction Identification Classification
Optimization

6. Knowledge Discovery Association rules Classification hierarchies
Sequential patterns
Patterns within time series
Clustering

7. Data Mining Applications
Marketing
Finance
Manufacturing
Health Care

8. Data Warehouse

9. Data Processing Terms Online Transaction Processing (OLTP)
Online Analytical Processing (OLAP)
Decision-Support Systems (DSS)

10. Comparing Databases Traditional database Data warehouse
Volatile data
Supports transaction processing
Optimized for updates and simple queries
Data warehouse
Relatively non-volatile data
Supports data extraction and analysis
Optimized for data retrieval and analysis

11. Data Warehouse Integrated data, often from multiple sources
Stored in a multidimensional data model
Updates from operations databases are non-real-time and require extraction, transformation, and loading (ETL) operations.

12. Data Warehouse Types Enterprise-wide data warehouse
Massive data store collecting data from various sources.
Virtual data warehouse
Based on virtual views of operational databases
Data marts
Data set targeted at a subset of an organization

13. Data Cube

14. Pivoted Data Cube

15. Data Warehouse Schemas

16. Data Warehouse Schemas (Con’t)

17. Data Acquisition Extraction from multiple data sources
Formatting for consistency
Data cleaning (scrubbing) for validity
Fitting data into the data warehouse model
Loading the data into the warehouse

18. Data Warehouse Data Storage
Store data based on data model
Create and maintain data structures
Create and maintain access paths to data
Provide time-variant data as new data is added
Support warehouse data updates
Refresh data
Purge obsolete or inappropriate data

19. Data Design Considerations
Use projections
Data model fit
Characteristics of available sources
Metadata component design
Modular component design
Designing for manageability and change
Distribution and parallel architecture

20. Access Component Functionality
Roll-up
Drill-down
Pivot
Slice and dice
Sorting
Selection
Derived attributes