Data Warehousing and Data Mining
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Data Warehouse concept and Data Mining
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Data Warehousing and Data Mining
- 1. Data Warehousing and OLAP Technology Oleh : Nama : Sunaryo Tandi N I M : (0801050005)
- 4. A producer wants to know…. Which are our lowest/highest margin customers ? Who are my customers and what products are they buying? Which customers are most likely to go to the competition ? What impact will new products/services have on revenue and margins? What product prom- -otions have the biggest impact on revenue? What is the most effective distribution channel?
- 13. OLTP vs. OLAP
- 14. Application-Orientation vs. Subject-Orientation Application-Orientation Operational Database Loans Credit Card Trust Savings Subject-Orientation Data Warehouse Customer Vendor Product Activity
- 19. Cube: A Lattice of Cuboids time,item time,item,location time, item, location, supplier all time item location supplier time,location time,supplier item,location item,supplier location,supplier time,item,supplier time,location,supplier item,location,supplier 0-D(apex) cuboid 1-D cuboids 2-D cuboids 3-D cuboids 4-D(base) cuboid
- 21. Example of Star Schema Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures time_key day day_of_the_week month quarter year time location_key street city state_or_province country location item_key item_name brand type supplier_type item branch_key branch_name branch_type branch
- 22. Example of Snowflake Schema Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures time_key day day_of_the_week month quarter year time location_key street city_key location item_key item_name brand type supplier_key item branch_key branch_name branch_type branch supplier_key supplier_type supplier city_key city state_or_province country city
- 23. Example of Fact Constellation Sales Fact Table time_key item_key branch_key location_key units_sold dollars_sold avg_sales Measures Shipping Fact Table time_key item_key shipper_key from_location to_location dollars_cost units_shipped time_key day day_of_the_week month quarter year time location_key street city province_or_state country location item_key item_name brand type supplier_type item branch_key branch_name branch_type branch shipper_key shipper_name location_key shipper_type shipper
- 29. A Concept Hierarchy: Dimension (location) all Europe North_America Mexico Canada Spain Germany Vancouver M. Wind L. Chan ... ... ... ... ... ... all region office country Toronto Frankfurt city
- 32. A Sample Data Cube Total annual sales of TV in U.S.A. Date Product Country All, All, All sum sum TV VCR PC 1Qtr 2Qtr 3Qtr 4Qtr U.S.A Canada Mexico sum
- 33. Cuboids Corresponding to the Cube all product date country product,date product,country date, country product, date, country 0-D(apex) cuboid 1-D cuboids 2-D cuboids 3-D(base) cuboid
- 36. Fig. 3.10 Typical OLAP Operations
- 37. A Star-Net Query Model Shipping Method AIR-EXPRESS TRUCK ORDER Customer Orders CONTRACTS Customer Product PRODUCT GROUP PRODUCT LINE PRODUCT ITEM SALES PERSON DISTRICT DIVISION Organization Promotion CITY COUNTRY REGION Location DAILY QTRLY ANNUALY Time Each circle is called a footprint
- 41. Data Warehouse: A Multi-Tiered Architecture Data Warehouse OLAP Engine Analysis Query Reports Data mining Monitor & Integrator Metadata Data Sources Front-End Tools Serve Data Marts Data Storage OLAP Server Extract Transform Load Refresh Operational DBs Other sources
- 43. Data Warehouse Development: A Recommended Approach Define a high-level corporate data model Data Mart Data Mart Distributed Data Marts Multi-Tier Data Warehouse Enterprise Data Warehouse Model refinement Model refinement
- 44. Data Mart Centric Data Marts Data Sources Data Warehouse
- 45. Problems with Data Mart Centric Solution If you end up creating multiple warehouses, integrating them is a problem
- 46. True Warehouse Data Marts Data Sources Data Warehouse
- 60. An OLAM System Architecture Data Warehouse Meta Data MDDB OLAM Engine OLAP Engine User GUI API Data Cube API Database API Data cleaning Data integration Layer3 OLAP/OLAM Layer2 MDDB Layer1 Data Repository Layer4 User Interface Filtering&Integration Filtering Databases Mining query Mining result
- 65. Thank You