SlideShare uma empresa Scribd logo
1 de 93
UNIT-1 Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Unit-1 Data warehouse and OLAP ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object]
Evolution of Database Technology ,[object Object],[object Object],[object Object]
Evolution of Database Technology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Evolution of Database Technology ,[object Object],[object Object],[object Object],[object Object],[object Object]
Evolution of Database Technology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Evolution of Database Technology ,[object Object],[object Object]
[object Object],[object Object]
What Is Data Mining? ,[object Object],[object Object],[object Object],[object Object]
Data Mining: A KDD Process ,[object Object],Data Cleaning Data Integration Databases Data Warehouse Knowledge Task-relevant Data Selection Data Mining Pattern Evaluation
Steps of a KDD Process ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Steps of a KDD Process   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Steps of a KDD Process ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Architecture of a Typical Data Mining System Data  Warehouse Data cleaning & data integration Filtering Databases Database or data warehouse server Data mining engine Pattern evaluation Graphical user interface Knowledge-base
Data Mining and Business Intelligence   Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration OLAP, MDA Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts Data Sources Paper, Files, Information Providers, Database Systems, OLTP
[object Object],[object Object]
Data Mining: On What Kind of Data? ,[object Object],[object Object],[object Object]
Data Mining: On What Kind of Data? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object]
Data Mining Functionalities  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Mining Functionalities ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Mining Functionalities ,[object Object],[object Object],[object Object],[object Object]
Data Mining Functionalities   ,[object Object],[object Object],[object Object],[object Object],[object Object]
Data Mining Functionalities ,[object Object],[object Object],[object Object]
Data Mining Functionalities  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object]
Data Mining: Confluence of Multiple Disciplines   Data Mining Database  Technology Statistics Other Disciplines Visualization Information Science MachineLearning
Data Mining: Classification Schemes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Mining: Classification Schemes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Mining: Classification Schemes ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object]
Major Issues in Data Mining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Major Issues in Data Mining ,[object Object],[object Object],[object Object]
Major Issues in Data Mining ,[object Object],[object Object],[object Object]
Lecture-7   What is Data Warehouse?
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. Operational  DBMS ,[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]
OLTP vs. OLAP
Why Separate Data Warehouse? ,[object Object],[object Object],[object Object]
Why Separate Data Warehouse? ,[object Object],[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
A Data Mining Query Language, DMQL: Language Primitives ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Defining a Star Schema in DMQL ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Defining a Snowflake Schema in DMQL ,[object Object],[object Object],[object Object],[object Object]
Defining a Snowflake Schema in DMQL ,[object Object],[object Object]
Defining a Fact Constellation in DMQL ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Defining a Fact Constellation in DMQL ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Measures: Three Categories ,[object Object],[object Object],[object Object],[object Object]
Measures: Three Categories ,[object Object],[object Object]
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
OLAP Operations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
OLAP Operations ,[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object]
Steps for the Design and Construction of Data Warehouse ,[object Object],[object Object],[object Object]
Design of a Data Warehouse: A Business Analysis Framework ,[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]
Data Warehouse Design Process   ,[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]
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
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]
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
Types of OLAP Servers  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Types of OLAP Servers ,[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]
Multi-way Array Aggregation for Cube Computation ,[object Object],[object Object],[object Object]
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  ,[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]
[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

Mais conteúdo relacionado

Mais procurados

Chapter 1. Introduction
Chapter 1. IntroductionChapter 1. Introduction
Chapter 1. Introduction
butest
 
Major issues in data mining
Major issues in data miningMajor issues in data mining
Major issues in data mining
Slideshare
 
Data Mining Concepts and Techniques
Data Mining Concepts and TechniquesData Mining Concepts and Techniques
Data Mining Concepts and Techniques
Pratik Tambekar
 

Mais procurados (20)

Introduction to Datamining Concept and Techniques
Introduction to Datamining Concept and TechniquesIntroduction to Datamining Concept and Techniques
Introduction to Datamining Concept and Techniques
 
Data Mining
Data MiningData Mining
Data Mining
 
Chapter 1. Introduction
Chapter 1. IntroductionChapter 1. Introduction
Chapter 1. Introduction
 
Odam: Open Data, Access and Mining
Odam: Open Data, Access and MiningOdam: Open Data, Access and Mining
Odam: Open Data, Access and Mining
 
Introduction to Data Mining
Introduction to Data MiningIntroduction to Data Mining
Introduction to Data Mining
 
2 Data-mining process
2   Data-mining process2   Data-mining process
2 Data-mining process
 
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & KamberChapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
Chapter - 5 Data Mining Concepts and Techniques 2nd Ed slides Han & Kamber
 
Knowledge discovery process
Knowledge discovery process Knowledge discovery process
Knowledge discovery process
 
Introduction to dm and dw
Introduction to dm and dwIntroduction to dm and dw
Introduction to dm and dw
 
3 Data Mining Tasks
3  Data Mining Tasks3  Data Mining Tasks
3 Data Mining Tasks
 
Major issues in data mining
Major issues in data miningMajor issues in data mining
Major issues in data mining
 
Introduction to Data Mining
Introduction to Data MiningIntroduction to Data Mining
Introduction to Data Mining
 
Data mining , Knowledge Discovery Process, Classification
Data mining , Knowledge Discovery Process, ClassificationData mining , Knowledge Discovery Process, Classification
Data mining , Knowledge Discovery Process, Classification
 
Data mining concepts and work
Data mining concepts and workData mining concepts and work
Data mining concepts and work
 
Dwdm
DwdmDwdm
Dwdm
 
Data Mining: Applying data mining
Data Mining: Applying data miningData Mining: Applying data mining
Data Mining: Applying data mining
 
Data mining an introduction
Data mining an introductionData mining an introduction
Data mining an introduction
 
Data Mining Concepts and Techniques
Data Mining Concepts and TechniquesData Mining Concepts and Techniques
Data Mining Concepts and Techniques
 
Introduction to DataMining
Introduction to DataMiningIntroduction to DataMining
Introduction to DataMining
 
03 data mining : data warehouse
03 data mining : data warehouse03 data mining : data warehouse
03 data mining : data warehouse
 

Destaque (10)

Lecture 2
Lecture 2Lecture 2
Lecture 2
 
data warehousing & minining 1st unit
data warehousing & minining 1st unitdata warehousing & minining 1st unit
data warehousing & minining 1st unit
 
Artificial Intelligence: Data Mining
Artificial Intelligence: Data MiningArtificial Intelligence: Data Mining
Artificial Intelligence: Data Mining
 
EDI
EDIEDI
EDI
 
Data mining
Data miningData mining
Data mining
 
ELECTRONIC DATA INTERCHANGE
ELECTRONIC DATA INTERCHANGE ELECTRONIC DATA INTERCHANGE
ELECTRONIC DATA INTERCHANGE
 
Ch 1 Intro to Data Mining
Ch 1 Intro to Data MiningCh 1 Intro to Data Mining
Ch 1 Intro to Data Mining
 
Data Mining Concepts
Data Mining ConceptsData Mining Concepts
Data Mining Concepts
 
Data mining
Data miningData mining
Data mining
 
Data mining slides
Data mining slidesData mining slides
Data mining slides
 

Semelhante a Dwdmunit1 a

Unit 1 (Chapter-1) on data mining concepts.ppt
Unit 1 (Chapter-1) on data mining concepts.pptUnit 1 (Chapter-1) on data mining concepts.ppt
Unit 1 (Chapter-1) on data mining concepts.ppt
PadmajaLaksh
 
Cssu dw dm
Cssu dw dmCssu dw dm
Cssu dw dm
sumit621
 

Semelhante a Dwdmunit1 a (20)

Introduction to data warehouse
Introduction to data warehouseIntroduction to data warehouse
Introduction to data warehouse
 
Dma unit 1
Dma unit   1Dma unit   1
Dma unit 1
 
Chapter 1. Introduction.ppt
Chapter 1. Introduction.pptChapter 1. Introduction.ppt
Chapter 1. Introduction.ppt
 
Dm unit i r16
Dm unit i   r16Dm unit i   r16
Dm unit i r16
 
Unit 3 part i Data mining
Unit 3 part i Data miningUnit 3 part i Data mining
Unit 3 part i Data mining
 
Unit 1 (Chapter-1) on data mining concepts.ppt
Unit 1 (Chapter-1) on data mining concepts.pptUnit 1 (Chapter-1) on data mining concepts.ppt
Unit 1 (Chapter-1) on data mining concepts.ppt
 
20IT501_DWDM_PPT_Unit_II.ppt
20IT501_DWDM_PPT_Unit_II.ppt20IT501_DWDM_PPT_Unit_II.ppt
20IT501_DWDM_PPT_Unit_II.ppt
 
20IT501_DWDM_PPT_Unit_II.ppt
20IT501_DWDM_PPT_Unit_II.ppt20IT501_DWDM_PPT_Unit_II.ppt
20IT501_DWDM_PPT_Unit_II.ppt
 
Data Warehouse and Data Mining
Data Warehouse and Data MiningData Warehouse and Data Mining
Data Warehouse and Data Mining
 
2 introductory slides
2 introductory slides2 introductory slides
2 introductory slides
 
Lect 1 introduction
Lect 1 introductionLect 1 introduction
Lect 1 introduction
 
Data Mining Intro
Data Mining IntroData Mining Intro
Data Mining Intro
 
data mining
data miningdata mining
data mining
 
01Intro.ppt
01Intro.ppt01Intro.ppt
01Intro.ppt
 
01Introduction to data mining chapter 1.ppt
01Introduction to data mining chapter 1.ppt01Introduction to data mining chapter 1.ppt
01Introduction to data mining chapter 1.ppt
 
01Intro.ppt
01Intro.ppt01Intro.ppt
01Intro.ppt
 
Data mining 1
Data mining 1Data mining 1
Data mining 1
 
Upstate CSCI 525 Data Mining Chapter 1
Upstate CSCI 525 Data Mining Chapter 1Upstate CSCI 525 Data Mining Chapter 1
Upstate CSCI 525 Data Mining Chapter 1
 
Data Mining mod1 ppt.pdf bca sixth semester notes
Data Mining mod1 ppt.pdf bca sixth semester notesData Mining mod1 ppt.pdf bca sixth semester notes
Data Mining mod1 ppt.pdf bca sixth semester notes
 
Cssu dw dm
Cssu dw dmCssu dw dm
Cssu dw dm
 

Último

Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in DelhiRussian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
kauryashika82
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
ciinovamais
 
An Overview of Mutual Funds Bcom Project.pdf
An Overview of Mutual Funds Bcom Project.pdfAn Overview of Mutual Funds Bcom Project.pdf
An Overview of Mutual Funds Bcom Project.pdf
SanaAli374401
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
QucHHunhnh
 

Último (20)

INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptxINDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
 
Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024
 
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in DelhiRussian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
 
Unit-IV; Professional Sales Representative (PSR).pptx
Unit-IV; Professional Sales Representative (PSR).pptxUnit-IV; Professional Sales Representative (PSR).pptx
Unit-IV; Professional Sales Representative (PSR).pptx
 
SECOND SEMESTER TOPIC COVERAGE SY 2023-2024 Trends, Networks, and Critical Th...
SECOND SEMESTER TOPIC COVERAGE SY 2023-2024 Trends, Networks, and Critical Th...SECOND SEMESTER TOPIC COVERAGE SY 2023-2024 Trends, Networks, and Critical Th...
SECOND SEMESTER TOPIC COVERAGE SY 2023-2024 Trends, Networks, and Critical Th...
 
Advance Mobile Application Development class 07
Advance Mobile Application Development class 07Advance Mobile Application Development class 07
Advance Mobile Application Development class 07
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17
 
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptxBasic Civil Engineering first year Notes- Chapter 4 Building.pptx
Basic Civil Engineering first year Notes- Chapter 4 Building.pptx
 
Web & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdfWeb & Social Media Analytics Previous Year Question Paper.pdf
Web & Social Media Analytics Previous Year Question Paper.pdf
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and Mode
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
 
fourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writingfourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writing
 
An Overview of Mutual Funds Bcom Project.pdf
An Overview of Mutual Funds Bcom Project.pdfAn Overview of Mutual Funds Bcom Project.pdf
An Overview of Mutual Funds Bcom Project.pdf
 
Application orientated numerical on hev.ppt
Application orientated numerical on hev.pptApplication orientated numerical on hev.ppt
Application orientated numerical on hev.ppt
 
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104
 
Measures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SDMeasures of Dispersion and Variability: Range, QD, AD and SD
Measures of Dispersion and Variability: Range, QD, AD and SD
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi  6.pdf1029-Danh muc Sach Giao Khoa khoi  6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
 
Accessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactAccessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impact
 
PROCESS RECORDING FORMAT.docx
PROCESS      RECORDING        FORMAT.docxPROCESS      RECORDING        FORMAT.docx
PROCESS RECORDING FORMAT.docx
 

Dwdmunit1 a

  • 1.
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15. Architecture of a Typical Data Mining System Data Warehouse Data cleaning & data integration Filtering Databases Database or data warehouse server Data mining engine Pattern evaluation Graphical user interface Knowledge-base
  • 16. Data Mining and Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration OLAP, MDA Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts Data Sources Paper, Files, Information Providers, Database Systems, OLTP
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28. Data Mining: Confluence of Multiple Disciplines Data Mining Database Technology Statistics Other Disciplines Visualization Information Science MachineLearning
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36. Lecture-7 What is Data Warehouse?
  • 37.
  • 38.
  • 39.
  • 40.
  • 41.
  • 42.
  • 43.
  • 45.
  • 46.
  • 47.
  • 48. 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
  • 49.
  • 50. 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
  • 51. 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
  • 52. 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
  • 53.
  • 54.
  • 55.
  • 56.
  • 57.
  • 58.
  • 59.
  • 60.
  • 61. 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
  • 62.
  • 63. 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
  • 64. 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
  • 65.
  • 66.
  • 67.
  • 68.
  • 69.
  • 70.
  • 71.
  • 72.
  • 73. 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
  • 74.
  • 75.
  • 76.
  • 77. 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
  • 78.
  • 79.
  • 80.
  • 81.
  • 82.
  • 83.
  • 84.
  • 85. 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
  • 86.
  • 87.
  • 88.
  • 89.
  • 90.
  • 91.
  • 92.
  • 93. 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