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[object Object],[object Object]
What is Data Warehouse? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse—Subject-Oriented ,[object Object],[object Object],[object Object]
Data Warehouse—Integrated ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse—Time Variant ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse—Non-Volatile ,[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse vs. Heterogeneous DBMS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse vs. Operational DBMS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
OLTP vs. OLAP
Why Separate Data Warehouse? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
From Tables and Spreadsheets to Data Cubes ,[object Object],[object Object],[object Object],[object Object],[object Object]
Cube: A Lattice of Cuboids all time item location supplier time,item time,location time,supplier item,location item,supplier location,supplier time,item,location time,item,supplier time,location,supplier item,location,supplier time, item, location, supplier 0-D(apex) cuboid 1-D cuboids 2-D cuboids 3-D cuboids 4-D(base) cuboid
Conceptual Modeling of Data Warehouses ,[object Object],[object Object],[object Object],[object Object]
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 province_or_street country location item_key item_name brand type supplier_type item branch_key branch_name branch_type branch
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 province_or_street country city
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_street 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
Data Warehousing  Definitions and Concepts   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehousing  Definitions and Concepts   ,[object Object],[object Object],[object Object],[object Object]
Data Warehousing  Definitions and Concepts   ,[object Object],[object Object],[object Object],[object Object]
Data Warehousing  Process Overview   ,[object Object],[object Object]
Data Warehousing  Process Overview
Data Warehousing  Process Overview   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehousing Architectures   ,[object Object],[object Object],[object Object],[object Object]
Data Warehousing Architectures
Data Warehousing Architectures
Data Warehousing Architectures
Data Warehousing Architectures   ,[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehousing Architectures
Data Warehousing Architectures
Data Warehousing Architectures
Data Warehousing Architectures
Data Warehousing Architectures
Data Warehousing Architectures
Data Warehousing Architectures   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Ten factors that potentially affect the architecture selection decision:
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
Multidimensional Data ,[object Object],Product Region Month Dimensions: Product, Location, Time Hierarchical summarization paths Industry  Region  Year Category  Country  Quarter Product  City  Month  Week Office  Day
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
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
Browsing a Data Cube ,[object Object],[object Object],[object Object]
Typical OLAP Operations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
OLAP Server Architectures ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object]
Design of a Data Warehouse: A Business Analysis Framework ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse Design Process  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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
Three Data Warehouse Models ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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
OLAP Server Architectures ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Chapter 2: Data Warehousing and OLAP Technology for Data Mining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Efficient Data Cube Computation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cube Operation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],(item) (city) () (year) (city, item) (city, year) (item, year) (city, item, year)
Cube Computation: ROLAP-Based Method ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Cube Computation: ROLAP-Based Method (2) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Multi-way Array Aggregation for Cube Computation ,[object Object],[object Object],[object Object],What is the best traversing order to do multi-way aggregation? A B 29 30 31 32 1 2 3 4 5 9 13 14 15 16 64 63 62 61 48 47 46 45 a1 a0 c3 c2 c1 c 0 b3 b2 b1 b0 a2 a3 C B 44 28 56 40 24 52 36 20 60
Multi-way Array Aggregation for Cube Computation B A B 29 30 31 32 1 2 3 4 5 9 13 14 15 16 64 63 62 61 48 47 46 45 a1 a0 c3 c2 c1 c 0 b3 b2 b1 b0 a2 a3 C 44 28 56 40 24 52 36 20 60
Multi-way Array Aggregation for Cube Computation A B 29 30 31 32 1 2 3 4 5 9 13 14 15 16 64 63 62 61 48 47 46 45 a1 a0 c3 c2 c1 c 0 b3 b2 b1 b0 a2 a3 C 44 28 56 40 24 52 36 20 60 B
Multi-Way Array Aggregation for Cube Computation (Cont.) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Indexing OLAP Data: Bitmap Index ,[object Object],[object Object],[object Object],[object Object],[object Object],Base table Index on Region Index on Type
Indexing OLAP Data: Join Indices ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Efficient Processing OLAP Queries ,[object Object],[object Object],[object Object],[object Object]
Metadata Repository ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse Back-End Tools and Utilities ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Chapter 2: Data Warehousing and OLAP Technology for Data Mining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Discovery-Driven Exploration of Data Cubes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Examples: Discovery-Driven Data Cubes
Complex Aggregation at Multiple Granularities: Multi-Feature Cubes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Chapter 2: Data Warehousing and OLAP Technology for Data Mining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse Usage ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
From On-Line Analytical Processing to On Line Analytical Mining (OLAM) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
An OLAM 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
Summary ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References (I) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
References (II) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
http://www.cs.sfu.ca/~han ,[object Object]
Data Warehousing Architectures
Data Warehousing Architectures   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Ten factors that potentially affect the architecture selection decision:
Data Integration and the  Extraction, Transformation,  and Load (ETL) Process ,[object Object],[object Object]
Data Integration and the  Extraction, Transformation,  and Load (ETL) Process ,[object Object],[object Object]
Data Integration and the  Extraction, Transformation,  and Load (ETL) Process ,[object Object],[object Object]
Data Integration and the  Extraction, Transformation,  and Load (ETL) Process ,[object Object],[object Object]
Data Integration and the  Extraction, Transformation,  and Load (ETL) Process
Data Integration and the  Extraction, Transformation,  and Load (ETL) Process ,[object Object],[object Object],[object Object],[object Object]
Data Integration and the  Extraction, Transformation,  and Load (ETL) Process ,[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse Development   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse Development   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse Development   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse Development   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse Development   ,[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse Development
Data Warehouse Development   ,[object Object],[object Object],[object Object],[object Object]
Data Warehouse Development   ,[object Object],[object Object],[object Object]
Data Warehouse Development ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Eleven major tasks that could be performed in parallel for successful implementation of a data warehouse  (Solomon, 2005) :
Data Warehouse Development   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse Development   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse Development   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse Development   ,[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse Development   ,[object Object],[object Object],[object Object],[object Object]
Data Warehouse Development   ,[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse Development   ,[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse Development   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Real-Time Data Warehousing   ,[object Object],[object Object]
Real-Time Data Warehousing   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Real-Time Data Warehousing
Real-Time Data Warehousing
Real-Time Data Warehousing   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Real-Time Data Warehousing   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Warehouse  Administration and Security Issues   ,[object Object],[object Object]
Data Warehouse  Administration and Security Issues   ,[object Object],[object Object],[object Object],[object Object],[object Object]

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11667 Bitt I 2008 Lect4

  • 1.
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 10.
  • 11.
  • 12. Cube: A Lattice of Cuboids all time item location supplier time,item time,location time,supplier item,location item,supplier location,supplier time,item,location time,item,supplier time,location,supplier item,location,supplier time, item, location, supplier 0-D(apex) cuboid 1-D cuboids 2-D cuboids 3-D cuboids 4-D(base) cuboid
  • 13.
  • 14. 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 province_or_street country location item_key item_name brand type supplier_type item branch_key branch_name branch_type branch
  • 15. 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 province_or_street country city
  • 16. 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_street 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
  • 17.
  • 18.
  • 19.
  • 20.
  • 21. Data Warehousing Process Overview
  • 22.
  • 23.
  • 27.
  • 34.
  • 35. 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
  • 36.
  • 37. 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
  • 38. 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
  • 39.
  • 40.
  • 41.
  • 42.
  • 43.
  • 44.
  • 45. 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
  • 46.
  • 47. 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
  • 48.
  • 49.
  • 50.
  • 51.
  • 52.
  • 53.
  • 54.
  • 55. Multi-way Array Aggregation for Cube Computation B A B 29 30 31 32 1 2 3 4 5 9 13 14 15 16 64 63 62 61 48 47 46 45 a1 a0 c3 c2 c1 c 0 b3 b2 b1 b0 a2 a3 C 44 28 56 40 24 52 36 20 60
  • 56. Multi-way Array Aggregation for Cube Computation A B 29 30 31 32 1 2 3 4 5 9 13 14 15 16 64 63 62 61 48 47 46 45 a1 a0 c3 c2 c1 c 0 b3 b2 b1 b0 a2 a3 C 44 28 56 40 24 52 36 20 60 B
  • 57.
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  • 64.
  • 66.
  • 67.
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  • 69.
  • 70. An OLAM 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
  • 71.
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  • 81. Data Integration and the Extraction, Transformation, and Load (ETL) Process
  • 82.
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