SlideShare a Scribd company logo
1 of 108
[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]

More Related Content

What's hot

multi dimensional data model
multi dimensional data modelmulti dimensional data model
multi dimensional data modelmoni sindhu
 
Business Intelligence and Multidimensional Database
Business Intelligence and Multidimensional DatabaseBusiness Intelligence and Multidimensional Database
Business Intelligence and Multidimensional DatabaseRussel Chowdhury
 
Multidimensional Database Design & Architecture
Multidimensional Database Design & ArchitectureMultidimensional Database Design & Architecture
Multidimensional Database Design & Architecturehasanshan
 
DATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALA
DATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALADATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALA
DATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALASaikiran Panjala
 
Steps To Build A Datawarehouse
Steps To Build A DatawarehouseSteps To Build A Datawarehouse
Steps To Build A DatawarehouseHendra Saputra
 
Using SSRS Reports with SSAS Cubes
Using SSRS Reports with SSAS CubesUsing SSRS Reports with SSAS Cubes
Using SSRS Reports with SSAS CubesCode Mastery
 
Data Warehouse Back to Basics: Dimensional Modeling
Data Warehouse Back to Basics: Dimensional ModelingData Warehouse Back to Basics: Dimensional Modeling
Data Warehouse Back to Basics: Dimensional ModelingDunn Solutions Group
 
Designing the business process dimensional model
Designing the business process dimensional modelDesigning the business process dimensional model
Designing the business process dimensional modelGersiton Pila Challco
 
Advanced Dimensional Modelling
Advanced Dimensional ModellingAdvanced Dimensional Modelling
Advanced Dimensional ModellingVincent Rainardi
 
Star ,Snow and Fact-Constullation Schemas??
Star ,Snow and  Fact-Constullation Schemas??Star ,Snow and  Fact-Constullation Schemas??
Star ,Snow and Fact-Constullation Schemas??Abdul Aslam
 
Data Warehouse Modeling
Data Warehouse ModelingData Warehouse Modeling
Data Warehouse Modelingvivekjv
 
Data ware house design
Data ware house designData ware house design
Data ware house designSayed Ahmed
 
Schemas for multidimensional databases
Schemas for multidimensional databasesSchemas for multidimensional databases
Schemas for multidimensional databasesyazad dumasia
 

What's hot (20)

Data integration
Data integrationData integration
Data integration
 
Dimensional Modeling
Dimensional ModelingDimensional Modeling
Dimensional Modeling
 
multi dimensional data model
multi dimensional data modelmulti dimensional data model
multi dimensional data model
 
Business Intelligence and Multidimensional Database
Business Intelligence and Multidimensional DatabaseBusiness Intelligence and Multidimensional Database
Business Intelligence and Multidimensional Database
 
Multidimensional Database Design & Architecture
Multidimensional Database Design & ArchitectureMultidimensional Database Design & Architecture
Multidimensional Database Design & Architecture
 
DATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALA
DATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALADATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALA
DATA WAREHOUSE IMPLEMENTATION BY SAIKIRAN PANJALA
 
Steps To Build A Datawarehouse
Steps To Build A DatawarehouseSteps To Build A Datawarehouse
Steps To Build A Datawarehouse
 
Data warehouse logical design
Data warehouse logical designData warehouse logical design
Data warehouse logical design
 
Using SSRS Reports with SSAS Cubes
Using SSRS Reports with SSAS CubesUsing SSRS Reports with SSAS Cubes
Using SSRS Reports with SSAS Cubes
 
Data Warehouse Back to Basics: Dimensional Modeling
Data Warehouse Back to Basics: Dimensional ModelingData Warehouse Back to Basics: Dimensional Modeling
Data Warehouse Back to Basics: Dimensional Modeling
 
Designing the business process dimensional model
Designing the business process dimensional modelDesigning the business process dimensional model
Designing the business process dimensional model
 
Advanced Dimensional Modelling
Advanced Dimensional ModellingAdvanced Dimensional Modelling
Advanced Dimensional Modelling
 
Data warehouse physical design
Data warehouse physical designData warehouse physical design
Data warehouse physical design
 
Star ,Snow and Fact-Constullation Schemas??
Star ,Snow and  Fact-Constullation Schemas??Star ,Snow and  Fact-Constullation Schemas??
Star ,Snow and Fact-Constullation Schemas??
 
Data Warehouse Designing: Dimensional Modelling and E-R Modelling
Data Warehouse Designing: Dimensional Modelling and E-R ModellingData Warehouse Designing: Dimensional Modelling and E-R Modelling
Data Warehouse Designing: Dimensional Modelling and E-R Modelling
 
Data Warehouse Modeling
Data Warehouse ModelingData Warehouse Modeling
Data Warehouse Modeling
 
Data preprocessing
Data preprocessingData preprocessing
Data preprocessing
 
Data ware house design
Data ware house designData ware house design
Data ware house design
 
Star schema PPT
Star schema PPTStar schema PPT
Star schema PPT
 
Schemas for multidimensional databases
Schemas for multidimensional databasesSchemas for multidimensional databases
Schemas for multidimensional databases
 

Viewers also liked

1 Pil курсы
1 Pil курсы1 Pil курсы
1 Pil курсыtbt
 
Olympische Spelen Parijs 2012
Olympische Spelen Parijs 2012Olympische Spelen Parijs 2012
Olympische Spelen Parijs 2012misslily
 
Irwin, Barclay & Green
Irwin, Barclay & GreenIrwin, Barclay & Green
Irwin, Barclay & Greenstu_dev
 
Reeldvd User Guide
Reeldvd User GuideReeldvd User Guide
Reeldvd User Guidegueste6b460
 
Climate Change Actions
Climate Change ActionsClimate Change Actions
Climate Change Actionsjefmoi
 

Viewers also liked (6)

1 Pil курсы
1 Pil курсы1 Pil курсы
1 Pil курсы
 
Olympische Spelen Parijs 2012
Olympische Spelen Parijs 2012Olympische Spelen Parijs 2012
Olympische Spelen Parijs 2012
 
sss
ssssss
sss
 
Irwin, Barclay & Green
Irwin, Barclay & GreenIrwin, Barclay & Green
Irwin, Barclay & Green
 
Reeldvd User Guide
Reeldvd User GuideReeldvd User Guide
Reeldvd User Guide
 
Climate Change Actions
Climate Change ActionsClimate Change Actions
Climate Change Actions
 

Similar to 11667 Bitt I 2008 Lect4

11666 Bitt I 2008 Lect3
11666 Bitt I 2008 Lect311666 Bitt I 2008 Lect3
11666 Bitt I 2008 Lect3ambujm
 
1.4 data warehouse
1.4 data warehouse1.4 data warehouse
1.4 data warehouseKrish_ver2
 
Chapter 4. Data Warehousing and On-Line Analytical Processing.ppt
Chapter 4. Data Warehousing and On-Line Analytical Processing.pptChapter 4. Data Warehousing and On-Line Analytical Processing.ppt
Chapter 4. Data Warehousing and On-Line Analytical Processing.pptSubrata Kumer Paul
 
Datawarehousing
DatawarehousingDatawarehousing
Datawarehousingsumit621
 
Data warehousing and online analytical processing
Data warehousing and online analytical processingData warehousing and online analytical processing
Data warehousing and online analytical processingVijayasankariS
 
Data Mining Concept & Technique-ch04.ppt
Data Mining Concept & Technique-ch04.pptData Mining Concept & Technique-ch04.ppt
Data Mining Concept & Technique-ch04.pptMutiaSari53
 
20IT501_DWDM_PPT_Unit_I.ppt
20IT501_DWDM_PPT_Unit_I.ppt20IT501_DWDM_PPT_Unit_I.ppt
20IT501_DWDM_PPT_Unit_I.pptPalaniKumarR2
 
Data Mining: Concepts and Techniques (3rd ed.) — Chapter _04 olap
Data Mining:  Concepts and Techniques (3rd ed.)— Chapter _04 olapData Mining:  Concepts and Techniques (3rd ed.)— Chapter _04 olap
Data Mining: Concepts and Techniques (3rd ed.) — Chapter _04 olapSalah Amean
 
BI Chapter 03.pdf business business business business business business
BI Chapter 03.pdf business business business business business businessBI Chapter 03.pdf business business business business business business
BI Chapter 03.pdf business business business business business businessJawaherAlbaddawi
 
Data Warehousing and Data Mining
Data Warehousing and Data MiningData Warehousing and Data Mining
Data Warehousing and Data Miningidnats
 
Introduction to Data Warehouse
Introduction to Data WarehouseIntroduction to Data Warehouse
Introduction to Data WarehouseSOMASUNDARAM T
 
Dataware house multidimensionalmodelling
Dataware house multidimensionalmodellingDataware house multidimensionalmodelling
Dataware house multidimensionalmodellingmeghu123
 
Dataware housing
Dataware housingDataware housing
Dataware housingwork
 
DATA WAREHOUSING
DATA WAREHOUSINGDATA WAREHOUSING
DATA WAREHOUSINGKing Julian
 

Similar to 11667 Bitt I 2008 Lect4 (20)

11666 Bitt I 2008 Lect3
11666 Bitt I 2008 Lect311666 Bitt I 2008 Lect3
11666 Bitt I 2008 Lect3
 
1.4 data warehouse
1.4 data warehouse1.4 data warehouse
1.4 data warehouse
 
Chapter 4. Data Warehousing and On-Line Analytical Processing.ppt
Chapter 4. Data Warehousing and On-Line Analytical Processing.pptChapter 4. Data Warehousing and On-Line Analytical Processing.ppt
Chapter 4. Data Warehousing and On-Line Analytical Processing.ppt
 
Data warehousing
Data warehousingData warehousing
Data warehousing
 
Datawarehousing
DatawarehousingDatawarehousing
Datawarehousing
 
Data warehousing and online analytical processing
Data warehousing and online analytical processingData warehousing and online analytical processing
Data warehousing and online analytical processing
 
Chapter 2
Chapter 2Chapter 2
Chapter 2
 
Unit 1
Unit 1Unit 1
Unit 1
 
Data Mining Concept & Technique-ch04.ppt
Data Mining Concept & Technique-ch04.pptData Mining Concept & Technique-ch04.ppt
Data Mining Concept & Technique-ch04.ppt
 
20IT501_DWDM_PPT_Unit_I.ppt
20IT501_DWDM_PPT_Unit_I.ppt20IT501_DWDM_PPT_Unit_I.ppt
20IT501_DWDM_PPT_Unit_I.ppt
 
Data Mining: Concepts and Techniques (3rd ed.) — Chapter _04 olap
Data Mining:  Concepts and Techniques (3rd ed.)— Chapter _04 olapData Mining:  Concepts and Techniques (3rd ed.)— Chapter _04 olap
Data Mining: Concepts and Techniques (3rd ed.) — Chapter _04 olap
 
BI Chapter 03.pdf business business business business business business
BI Chapter 03.pdf business business business business business businessBI Chapter 03.pdf business business business business business business
BI Chapter 03.pdf business business business business business business
 
DW 101
DW 101DW 101
DW 101
 
Data Warehousing and Data Mining
Data Warehousing and Data MiningData Warehousing and Data Mining
Data Warehousing and Data Mining
 
Introduction to Data Warehouse
Introduction to Data WarehouseIntroduction to Data Warehouse
Introduction to Data Warehouse
 
Dataware house multidimensionalmodelling
Dataware house multidimensionalmodellingDataware house multidimensionalmodelling
Dataware house multidimensionalmodelling
 
Datawarehouse and OLAP
Datawarehouse and OLAPDatawarehouse and OLAP
Datawarehouse and OLAP
 
Chpt2.ppt
Chpt2.pptChpt2.ppt
Chpt2.ppt
 
Dataware housing
Dataware housingDataware housing
Dataware housing
 
DATA WAREHOUSING
DATA WAREHOUSINGDATA WAREHOUSING
DATA WAREHOUSING
 

Recently uploaded

FULL ENJOY Call Girls In Majnu Ka Tilla, Delhi Contact Us 8377877756
FULL ENJOY Call Girls In Majnu Ka Tilla, Delhi Contact Us 8377877756FULL ENJOY Call Girls In Majnu Ka Tilla, Delhi Contact Us 8377877756
FULL ENJOY Call Girls In Majnu Ka Tilla, Delhi Contact Us 8377877756dollysharma2066
 
MONA 98765-12871 CALL GIRLS IN LUDHIANA LUDHIANA CALL GIRL
MONA 98765-12871 CALL GIRLS IN LUDHIANA LUDHIANA CALL GIRLMONA 98765-12871 CALL GIRLS IN LUDHIANA LUDHIANA CALL GIRL
MONA 98765-12871 CALL GIRLS IN LUDHIANA LUDHIANA CALL GIRLSeo
 
Cracking the Cultural Competence Code.pptx
Cracking the Cultural Competence Code.pptxCracking the Cultural Competence Code.pptx
Cracking the Cultural Competence Code.pptxWorkforce Group
 
Famous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st CenturyFamous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st Centuryrwgiffor
 
Business Model Canvas (BMC)- A new venture concept
Business Model Canvas (BMC)-  A new venture conceptBusiness Model Canvas (BMC)-  A new venture concept
Business Model Canvas (BMC)- A new venture conceptP&CO
 
Dr. Admir Softic_ presentation_Green Club_ENG.pdf
Dr. Admir Softic_ presentation_Green Club_ENG.pdfDr. Admir Softic_ presentation_Green Club_ENG.pdf
Dr. Admir Softic_ presentation_Green Club_ENG.pdfAdmir Softic
 
Insurers' journeys to build a mastery in the IoT usage
Insurers' journeys to build a mastery in the IoT usageInsurers' journeys to build a mastery in the IoT usage
Insurers' journeys to build a mastery in the IoT usageMatteo Carbone
 
It will be International Nurses' Day on 12 May
It will be International Nurses' Day on 12 MayIt will be International Nurses' Day on 12 May
It will be International Nurses' Day on 12 MayNZSG
 
Call Girls Service In Old Town Dubai ((0551707352)) Old Town Dubai Call Girl ...
Call Girls Service In Old Town Dubai ((0551707352)) Old Town Dubai Call Girl ...Call Girls Service In Old Town Dubai ((0551707352)) Old Town Dubai Call Girl ...
Call Girls Service In Old Town Dubai ((0551707352)) Old Town Dubai Call Girl ...allensay1
 
Russian Call Girls In Gurgaon ❤️8448577510 ⊹Best Escorts Service In 24/7 Delh...
Russian Call Girls In Gurgaon ❤️8448577510 ⊹Best Escorts Service In 24/7 Delh...Russian Call Girls In Gurgaon ❤️8448577510 ⊹Best Escorts Service In 24/7 Delh...
Russian Call Girls In Gurgaon ❤️8448577510 ⊹Best Escorts Service In 24/7 Delh...lizamodels9
 
Katrina Personal Brand Project and portfolio 1
Katrina Personal Brand Project and portfolio 1Katrina Personal Brand Project and portfolio 1
Katrina Personal Brand Project and portfolio 1kcpayne
 
RSA Conference Exhibitor List 2024 - Exhibitors Data
RSA Conference Exhibitor List 2024 - Exhibitors DataRSA Conference Exhibitor List 2024 - Exhibitors Data
RSA Conference Exhibitor List 2024 - Exhibitors DataExhibitors Data
 
The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...
The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...
The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...Aggregage
 
Uneak White's Personal Brand Exploration Presentation
Uneak White's Personal Brand Exploration PresentationUneak White's Personal Brand Exploration Presentation
Uneak White's Personal Brand Exploration Presentationuneakwhite
 
B.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptx
B.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptxB.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptx
B.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptxpriyanshujha201
 
Call Girls Electronic City Just Call 👗 7737669865 👗 Top Class Call Girl Servi...
Call Girls Electronic City Just Call 👗 7737669865 👗 Top Class Call Girl Servi...Call Girls Electronic City Just Call 👗 7737669865 👗 Top Class Call Girl Servi...
Call Girls Electronic City Just Call 👗 7737669865 👗 Top Class Call Girl Servi...amitlee9823
 
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...rajveerescorts2022
 

Recently uploaded (20)

FULL ENJOY Call Girls In Majnu Ka Tilla, Delhi Contact Us 8377877756
FULL ENJOY Call Girls In Majnu Ka Tilla, Delhi Contact Us 8377877756FULL ENJOY Call Girls In Majnu Ka Tilla, Delhi Contact Us 8377877756
FULL ENJOY Call Girls In Majnu Ka Tilla, Delhi Contact Us 8377877756
 
MONA 98765-12871 CALL GIRLS IN LUDHIANA LUDHIANA CALL GIRL
MONA 98765-12871 CALL GIRLS IN LUDHIANA LUDHIANA CALL GIRLMONA 98765-12871 CALL GIRLS IN LUDHIANA LUDHIANA CALL GIRL
MONA 98765-12871 CALL GIRLS IN LUDHIANA LUDHIANA CALL GIRL
 
Cracking the Cultural Competence Code.pptx
Cracking the Cultural Competence Code.pptxCracking the Cultural Competence Code.pptx
Cracking the Cultural Competence Code.pptx
 
Famous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st CenturyFamous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st Century
 
Business Model Canvas (BMC)- A new venture concept
Business Model Canvas (BMC)-  A new venture conceptBusiness Model Canvas (BMC)-  A new venture concept
Business Model Canvas (BMC)- A new venture concept
 
Dr. Admir Softic_ presentation_Green Club_ENG.pdf
Dr. Admir Softic_ presentation_Green Club_ENG.pdfDr. Admir Softic_ presentation_Green Club_ENG.pdf
Dr. Admir Softic_ presentation_Green Club_ENG.pdf
 
Insurers' journeys to build a mastery in the IoT usage
Insurers' journeys to build a mastery in the IoT usageInsurers' journeys to build a mastery in the IoT usage
Insurers' journeys to build a mastery in the IoT usage
 
It will be International Nurses' Day on 12 May
It will be International Nurses' Day on 12 MayIt will be International Nurses' Day on 12 May
It will be International Nurses' Day on 12 May
 
Call Girls Service In Old Town Dubai ((0551707352)) Old Town Dubai Call Girl ...
Call Girls Service In Old Town Dubai ((0551707352)) Old Town Dubai Call Girl ...Call Girls Service In Old Town Dubai ((0551707352)) Old Town Dubai Call Girl ...
Call Girls Service In Old Town Dubai ((0551707352)) Old Town Dubai Call Girl ...
 
Russian Call Girls In Gurgaon ❤️8448577510 ⊹Best Escorts Service In 24/7 Delh...
Russian Call Girls In Gurgaon ❤️8448577510 ⊹Best Escorts Service In 24/7 Delh...Russian Call Girls In Gurgaon ❤️8448577510 ⊹Best Escorts Service In 24/7 Delh...
Russian Call Girls In Gurgaon ❤️8448577510 ⊹Best Escorts Service In 24/7 Delh...
 
Forklift Operations: Safety through Cartoons
Forklift Operations: Safety through CartoonsForklift Operations: Safety through Cartoons
Forklift Operations: Safety through Cartoons
 
Falcon Invoice Discounting platform in india
Falcon Invoice Discounting platform in indiaFalcon Invoice Discounting platform in india
Falcon Invoice Discounting platform in india
 
unwanted pregnancy Kit [+918133066128] Abortion Pills IN Dubai UAE Abudhabi
unwanted pregnancy Kit [+918133066128] Abortion Pills IN Dubai UAE Abudhabiunwanted pregnancy Kit [+918133066128] Abortion Pills IN Dubai UAE Abudhabi
unwanted pregnancy Kit [+918133066128] Abortion Pills IN Dubai UAE Abudhabi
 
Katrina Personal Brand Project and portfolio 1
Katrina Personal Brand Project and portfolio 1Katrina Personal Brand Project and portfolio 1
Katrina Personal Brand Project and portfolio 1
 
RSA Conference Exhibitor List 2024 - Exhibitors Data
RSA Conference Exhibitor List 2024 - Exhibitors DataRSA Conference Exhibitor List 2024 - Exhibitors Data
RSA Conference Exhibitor List 2024 - Exhibitors Data
 
The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...
The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...
The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...
 
Uneak White's Personal Brand Exploration Presentation
Uneak White's Personal Brand Exploration PresentationUneak White's Personal Brand Exploration Presentation
Uneak White's Personal Brand Exploration Presentation
 
B.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptx
B.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptxB.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptx
B.COM Unit – 4 ( CORPORATE SOCIAL RESPONSIBILITY ( CSR ).pptx
 
Call Girls Electronic City Just Call 👗 7737669865 👗 Top Class Call Girl Servi...
Call Girls Electronic City Just Call 👗 7737669865 👗 Top Class Call Girl Servi...Call Girls Electronic City Just Call 👗 7737669865 👗 Top Class Call Girl Servi...
Call Girls Electronic City Just Call 👗 7737669865 👗 Top Class Call Girl Servi...
 
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...
 

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.
  • 58.
  • 59.
  • 60.
  • 61.
  • 62.
  • 63.
  • 64.
  • 66.
  • 67.
  • 68.
  • 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.
  • 72.
  • 73.
  • 74.
  • 76.
  • 77.
  • 78.
  • 79.
  • 80.
  • 81. Data Integration and the Extraction, Transformation, and Load (ETL) Process
  • 82.
  • 83.
  • 84.
  • 85.
  • 86.
  • 87.
  • 88.
  • 90.
  • 91.
  • 92.
  • 93.
  • 94.
  • 95.
  • 96.
  • 97.
  • 98.
  • 99.
  • 100.
  • 101.
  • 102.
  • 105.
  • 106.
  • 107.
  • 108.