Data warehouses are repositories that provide access to data for complex analysis and knowledge discovery. They involve extracting, transforming, and loading data from source systems and storing it in a multidimensional database. Online transaction processing (OLTP) is used for traditional database operations like inserts and updates, while online analytical processing (OLAP) enables analysis of complex warehouse data. The goal of data mining is knowledge discovery through predicting, classifying, identifying patterns, or optimizing from stored warehouse data using techniques like classification, clustering, and regression.

2. Groups Members

3. OLAP, OLTP and Data Mining Title Data Warehouse DW Diagram OLTP OLAP Data Mining Data Mining goal Data Mining Elements Data Mining Application DW VS Data Mining Thanks

4. Data Warehouses Repository is a key data warehouse component Data warehouses provide access to data for complex analysis, knowledge discovery, and decision making. Data warehousing more generally as a collection of decision support technologies , aimed at enabling the knowledge worker (executive, manager, analyst) to make better and faster decisions.

6. Extract, Transform and Load Pulling data out of the source system and placing it into a data warehouse Cleaning Filtering Splitting a column into multiple columns Joining together. loading the data into a data warehouse

8. On-line Transaction Processing Use in Traditional databases Includes insertions, updates, and deletions, while also supporting information query requirements

9. On-line Analytical Processing To describe the analysis of complex data from the data warehouse ROLAP (relational OLAP) and MOLAP (multidimensional OLAP) functions

11. Knowledge Discovery Process The knowledge discovery process comprises six phases Data selection, Data cleansing, Enrichment, Data transformation or encoding, Data mining, Reporting and display of the discovered information.

12. Data Mining Data Mining as a Part of the Knowledge Discovery Process Used for knowledge discovery, the process of searching data for the new knowledge.

13. Data mining consists of five major elements: Extract, transform, and load transaction data onto the data warehouse system. Store and manage the data in a multidimensional database system. Provide data access to business analysts and information technology professionals. Analyse the data by application software. Present the data in a useful format, such as a graph or table.

14. Goal of Data Mining Prediction Identification Classification( combinations of parameters ) Optimization( Goal of data mining may be to optimize the use of limited resources such as time, space, money, or materials and to maximize output variables such as sales or profits under a given set of constraints)

15. Applications of Data Mining Marketing Finance Manufacturing Health Care Many people only need read-access to data, but still need a very rapid access to a larger volume of data than can conveniently be downloaded to the desktop. Data comes from multiple databases Such types of functionality provide:- Data warehousing, on-line analytical processing (OLAP), and data mining

16. DW VS DM Data warehousing can be seen as a process that requires a variety of activities to precede it; Data mining may be thought as an activity that draws knowledge from an existing data warehouse.