SlideShare une entreprise Scribd logo
1  sur  20
Télécharger pour lire hors ligne
Data WarehousingData Warehousing
11
Data WarehousingData Warehousing
Lecture-28Lecture-28
Need for Speed: Join TechniquesNeed for Speed: Join Techniques
Virtual University of PakistanVirtual University of Pakistan
Ahsan Abdullah
Assoc. Prof. & Head
Center for Agro-Informatics Research
www.nu.edu.pk/cairindex.asp
National University of Computers & Emerging Sciences, Islamabad
Email: ahsan101@yahoo.com
Data Warehousing
2
Need for Speed: Join TechniquesNeed for Speed: Join Techniques
Data Warehousing
3
BackgroundBackground
Data Warehousing
4
About Nested-Loop JoinAbout Nested-Loop Join
Data Warehousing
5
FOR i = 1 to N DO BEGINFOR i = 1 to N DO BEGIN /*/* N rows in T1N rows in T1 */*/
IF iIF ithth
row of T1 qualifies THEN BEGINrow of T1 qualifies THEN BEGIN
For j = 1 to M DO BEGINFor j = 1 to M DO BEGIN /* M rows in T2/* M rows in T2 */*/
IF the iIF the ithth
row of T1 matches to jrow of T1 matches to jthth
row of T2 on join keyrow of T2 on join key
THEN BEGINTHEN BEGIN
IF the jIF the jthth
row of T2 qualifies THEN BEGINrow of T2 qualifies THEN BEGIN
produce output rowproduce output row
ENDEND
ENDEND
ENDEND
ENDEND
ENDEND
Nested-Loop Join: CodeNested-Loop Join: Code
GOES TO GRAPHICSGOES TO GRAPHICS
Data Warehousing
6
““What is the average GPA ofWhat is the average GPA of
undergraduate male students?”undergraduate male students?”
For each qualifying row of Personal table,
Academic table is examined for matching rows.
Student Personal Table Student Academic Table
298-----------------
----------------------
----------------------
62------------------
----------------------
----------------------
440------------------
Nested-Loop Join: Working ExampleNested-Loop Join: Working Example
Results
Search
Results
Search
Results
Search
GOES TO GRAPHICSGOES TO GRAPHICS
Data Warehousing
7
Nested-Loop Join: Order of TablesNested-Loop Join: Order of Tables
Data Warehousing
8
Nested-Loop Join: Cost FormulaNested-Loop Join: Cost Formula
Join cost =Join cost = Cost of accessing Table_A +
# of qualifying rows in Table_A × Blocks of
Table_B to be scanned for each qualifying row
OR
Join cost =Join cost = Blocks accessed for Table_A +
Blocks accessed for Table_A × Blocks
accessed for Table_B
GOES TO GRAPHICSGOES TO GRAPHICS
Data Warehousing
9
Nested-Loop Join: Cost of reorderNested-Loop Join: Cost of reorder
Table_A = 500 blocks and
Table_B = 700 blocks.
Qualifying blocks for Table_A QB(A) = 50
Qualifying blocks for Table_B QB(B) = 100
Join cost A&B = 500 + 50×700 = 35,500 I/Os
Join cost B&A = 700 + 100×500 = 50,700 I/Os
i.e. an increase in I/O of about 43%.
GOES TO GRAPHICSGOES TO GRAPHICS
Data Warehousing
10
Nested-Loop Join: VariantsNested-Loop Join: Variants
Data Warehousing
11
Sort-Merge JoinSort-Merge Join
Data Warehousing
12
Sort-Merge Join: ProcessSort-Merge Join: Process
Data WarehousingData Warehousing
1313
1
1
2
2
2
4
5
5
5
6
6
6
6
6
7
8
1
3
3
4
4
4
5
5
6
6
6
6
7
7
7
7
Table_A Table_B
1
1
2
2
2
4
5
5
5
6
6
6
6
6
7
8
1
3
3
4
4
4
5
5
6
6
6
6
7
7
7
7
Table_A Table_B
1
1
2
2
2
4
5
5
5
6
6
6
6
6
7
8
1
3
3
4
4
4
5
5
6
6
6
6
7
7
7
7
Table_A Table_B
Sort-Merge Join Example
Data Warehousing
14
Sort-Merge Join: NoteSort-Merge Join: Note
Data Warehousing
15
Hash-Based joinHash-Based join
Data Warehousing
16
Hash-Based Join: WorkingHash-Based Join: Working
Data Warehousing
17
Hash-Based Join: ExampleHash-Based Join: Example
Table_B on disk
DiskDisk
Original
Relation
Table_A
hash
function
h
Join Result
. . .
Table_B
M N
N
2
1
.
.
.
1
2
.
.
.
Table_A in main memory
MAIN MEMORY
GOES TO GRAPHICSGOES TO GRAPHICS
Data Warehousing
18
Hash-Based Join: Large “small” TableHash-Based Join: Large “small” Table
Data Warehousing
19
Hash-Based Join: Partition SkewHash-Based Join: Partition Skew
Data Warehousing
20
Hash-Based Join: Intrinsic SkewHash-Based Join: Intrinsic Skew

Contenu connexe

Tendances

presentation
presentationpresentation
presentationjie ren
 
05 heap 20161110_jintaeks
05 heap 20161110_jintaeks05 heap 20161110_jintaeks
05 heap 20161110_jintaeksJinTaek Seo
 
Data_Visualization_LP Result_Dashboard_Using_R_Graphics
Data_Visualization_LP Result_Dashboard_Using_R_GraphicsData_Visualization_LP Result_Dashboard_Using_R_Graphics
Data_Visualization_LP Result_Dashboard_Using_R_GraphicsNoli Sicad
 
Intellectual technologies
Intellectual technologiesIntellectual technologies
Intellectual technologiesPolad Saruxanov
 
Advanced Analytics and Data Visualisation in Forest Management and Planning
Advanced Analytics and Data Visualisation in  Forest Management and PlanningAdvanced Analytics and Data Visualisation in  Forest Management and Planning
Advanced Analytics and Data Visualisation in Forest Management and PlanningNoli Sicad
 
R and Visualization: A match made in Heaven
R and Visualization: A match made in HeavenR and Visualization: A match made in Heaven
R and Visualization: A match made in HeavenEdureka!
 
Hadoop for Data Science: Moving from BI dashboards to R models, using Hive st...
Hadoop for Data Science: Moving from BI dashboards to R models, using Hive st...Hadoop for Data Science: Moving from BI dashboards to R models, using Hive st...
Hadoop for Data Science: Moving from BI dashboards to R models, using Hive st...huguk
 
heap Sort Algorithm
heap  Sort Algorithmheap  Sort Algorithm
heap Sort AlgorithmLemia Algmri
 
8th TUC Meeting | Lijun Chang (University of New South Wales). Efficient Subg...
8th TUC Meeting | Lijun Chang (University of New South Wales). Efficient Subg...8th TUC Meeting | Lijun Chang (University of New South Wales). Efficient Subg...
8th TUC Meeting | Lijun Chang (University of New South Wales). Efficient Subg...LDBC council
 
Brief introduction on GAN
Brief introduction on GANBrief introduction on GAN
Brief introduction on GANDai-Hai Nguyen
 
Data Analysis in Python-NumPy
Data Analysis in Python-NumPyData Analysis in Python-NumPy
Data Analysis in Python-NumPyDevashish Kumar
 
One Algorithm to Rule Them All: How to Automate Statistical Computation
One Algorithm to Rule Them All: How to Automate Statistical ComputationOne Algorithm to Rule Them All: How to Automate Statistical Computation
One Algorithm to Rule Them All: How to Automate Statistical ComputationWork-Bench
 
Aaa ped-10-Supervised Learning: Introduction to Supervised Learning
Aaa ped-10-Supervised Learning: Introduction to Supervised LearningAaa ped-10-Supervised Learning: Introduction to Supervised Learning
Aaa ped-10-Supervised Learning: Introduction to Supervised LearningAminaRepo
 

Tendances (20)

presentation
presentationpresentation
presentation
 
Data visualization with R
Data visualization with RData visualization with R
Data visualization with R
 
05 heap 20161110_jintaeks
05 heap 20161110_jintaeks05 heap 20161110_jintaeks
05 heap 20161110_jintaeks
 
Data_Visualization_LP Result_Dashboard_Using_R_Graphics
Data_Visualization_LP Result_Dashboard_Using_R_GraphicsData_Visualization_LP Result_Dashboard_Using_R_Graphics
Data_Visualization_LP Result_Dashboard_Using_R_Graphics
 
Intellectual technologies
Intellectual technologiesIntellectual technologies
Intellectual technologies
 
Advanced Analytics and Data Visualisation in Forest Management and Planning
Advanced Analytics and Data Visualisation in  Forest Management and PlanningAdvanced Analytics and Data Visualisation in  Forest Management and Planning
Advanced Analytics and Data Visualisation in Forest Management and Planning
 
An Evaluation of Models for Runtime Approximation in Link Discovery
An Evaluation of Models for Runtime Approximation in Link DiscoveryAn Evaluation of Models for Runtime Approximation in Link Discovery
An Evaluation of Models for Runtime Approximation in Link Discovery
 
R and Visualization: A match made in Heaven
R and Visualization: A match made in HeavenR and Visualization: A match made in Heaven
R and Visualization: A match made in Heaven
 
Apriori algorithm
Apriori algorithmApriori algorithm
Apriori algorithm
 
Hadoop for Data Science: Moving from BI dashboards to R models, using Hive st...
Hadoop for Data Science: Moving from BI dashboards to R models, using Hive st...Hadoop for Data Science: Moving from BI dashboards to R models, using Hive st...
Hadoop for Data Science: Moving from BI dashboards to R models, using Hive st...
 
heap Sort Algorithm
heap  Sort Algorithmheap  Sort Algorithm
heap Sort Algorithm
 
Tree
TreeTree
Tree
 
Heap sort
Heap sortHeap sort
Heap sort
 
8th TUC Meeting | Lijun Chang (University of New South Wales). Efficient Subg...
8th TUC Meeting | Lijun Chang (University of New South Wales). Efficient Subg...8th TUC Meeting | Lijun Chang (University of New South Wales). Efficient Subg...
8th TUC Meeting | Lijun Chang (University of New South Wales). Efficient Subg...
 
9-Figures in LaTex
9-Figures in LaTex9-Figures in LaTex
9-Figures in LaTex
 
Brief introduction on GAN
Brief introduction on GANBrief introduction on GAN
Brief introduction on GAN
 
Data Analysis in Python-NumPy
Data Analysis in Python-NumPyData Analysis in Python-NumPy
Data Analysis in Python-NumPy
 
One Algorithm to Rule Them All: How to Automate Statistical Computation
One Algorithm to Rule Them All: How to Automate Statistical ComputationOne Algorithm to Rule Them All: How to Automate Statistical Computation
One Algorithm to Rule Them All: How to Automate Statistical Computation
 
Essential NumPy
Essential NumPyEssential NumPy
Essential NumPy
 
Aaa ped-10-Supervised Learning: Introduction to Supervised Learning
Aaa ped-10-Supervised Learning: Introduction to Supervised LearningAaa ped-10-Supervised Learning: Introduction to Supervised Learning
Aaa ped-10-Supervised Learning: Introduction to Supervised Learning
 

En vedette

En vedette (20)

Lecture 26
Lecture 26Lecture 26
Lecture 26
 
Lecture 30
Lecture 30Lecture 30
Lecture 30
 
Lecture 22
Lecture 22Lecture 22
Lecture 22
 
Lecture 24
Lecture 24Lecture 24
Lecture 24
 
Lecture 25
Lecture 25Lecture 25
Lecture 25
 
Lecture 27
Lecture 27Lecture 27
Lecture 27
 
Lecture 29
Lecture 29Lecture 29
Lecture 29
 
Lecture 23
Lecture 23Lecture 23
Lecture 23
 
Lecture 10
Lecture 10Lecture 10
Lecture 10
 
Lecture 11
Lecture 11Lecture 11
Lecture 11
 
Lecture 6
Lecture 6Lecture 6
Lecture 6
 
Lecture 4
Lecture 4Lecture 4
Lecture 4
 
Lecture 3
Lecture 3Lecture 3
Lecture 3
 
Lecture 2
Lecture 2Lecture 2
Lecture 2
 
Lecture 34
Lecture 34Lecture 34
Lecture 34
 
Lecture 32
Lecture 32Lecture 32
Lecture 32
 
Lecture 33
Lecture 33Lecture 33
Lecture 33
 
Lecture 31
Lecture 31Lecture 31
Lecture 31
 
Lecture 9
Lecture 9Lecture 9
Lecture 9
 
Lecture 12
Lecture 12Lecture 12
Lecture 12
 

Similaire à Lecture 28

Stacks in algorithems & data structure
Stacks in algorithems & data structureStacks in algorithems & data structure
Stacks in algorithems & data structurefaran nawaz
 
Efficient top k retrieval on massive data
Efficient top k retrieval on massive dataEfficient top k retrieval on massive data
Efficient top k retrieval on massive dataPvrtechnologies Nellore
 
i-Eclat: performance enhancement of Eclat via incremental approach in frequen...
i-Eclat: performance enhancement of Eclat via incremental approach in frequen...i-Eclat: performance enhancement of Eclat via incremental approach in frequen...
i-Eclat: performance enhancement of Eclat via incremental approach in frequen...TELKOMNIKA JOURNAL
 
Data Structures and Algorithms (DSA) is a fundamental part of Computer Scienc...
Data Structures and Algorithms (DSA) is a fundamental part of Computer Scienc...Data Structures and Algorithms (DSA) is a fundamental part of Computer Scienc...
Data Structures and Algorithms (DSA) is a fundamental part of Computer Scienc...ssuser6478a8
 
Data Structures and Algorithms (DSA) is a fundamental part of Computer Scienc...
Data Structures and Algorithms (DSA) is a fundamental part of Computer Scienc...Data Structures and Algorithms (DSA) is a fundamental part of Computer Scienc...
Data Structures and Algorithms (DSA) is a fundamental part of Computer Scienc...ssuser6478a8
 
DT-08-Hashing.PPTX
DT-08-Hashing.PPTXDT-08-Hashing.PPTX
DT-08-Hashing.PPTXBhuvanBalan1
 
Data Structures and Algorithm - Week 8 - Minimum Spanning Trees
Data Structures and Algorithm - Week 8 - Minimum Spanning TreesData Structures and Algorithm - Week 8 - Minimum Spanning Trees
Data Structures and Algorithm - Week 8 - Minimum Spanning TreesFerdin Joe John Joseph PhD
 

Similaire à Lecture 28 (11)

Stacks in algorithems & data structure
Stacks in algorithems & data structureStacks in algorithems & data structure
Stacks in algorithems & data structure
 
Efficient top k retrieval on massive data
Efficient top k retrieval on massive dataEfficient top k retrieval on massive data
Efficient top k retrieval on massive data
 
final_presentation
final_presentationfinal_presentation
final_presentation
 
Vldb14
Vldb14Vldb14
Vldb14
 
Chapter 6 ds
Chapter 6 dsChapter 6 ds
Chapter 6 ds
 
Python - Lecture 12
Python - Lecture 12Python - Lecture 12
Python - Lecture 12
 
i-Eclat: performance enhancement of Eclat via incremental approach in frequen...
i-Eclat: performance enhancement of Eclat via incremental approach in frequen...i-Eclat: performance enhancement of Eclat via incremental approach in frequen...
i-Eclat: performance enhancement of Eclat via incremental approach in frequen...
 
Data Structures and Algorithms (DSA) is a fundamental part of Computer Scienc...
Data Structures and Algorithms (DSA) is a fundamental part of Computer Scienc...Data Structures and Algorithms (DSA) is a fundamental part of Computer Scienc...
Data Structures and Algorithms (DSA) is a fundamental part of Computer Scienc...
 
Data Structures and Algorithms (DSA) is a fundamental part of Computer Scienc...
Data Structures and Algorithms (DSA) is a fundamental part of Computer Scienc...Data Structures and Algorithms (DSA) is a fundamental part of Computer Scienc...
Data Structures and Algorithms (DSA) is a fundamental part of Computer Scienc...
 
DT-08-Hashing.PPTX
DT-08-Hashing.PPTXDT-08-Hashing.PPTX
DT-08-Hashing.PPTX
 
Data Structures and Algorithm - Week 8 - Minimum Spanning Trees
Data Structures and Algorithm - Week 8 - Minimum Spanning TreesData Structures and Algorithm - Week 8 - Minimum Spanning Trees
Data Structures and Algorithm - Week 8 - Minimum Spanning Trees
 

Plus de Shani729

Python tutorialfeb152012
Python tutorialfeb152012Python tutorialfeb152012
Python tutorialfeb152012Shani729
 
Python tutorial
Python tutorialPython tutorial
Python tutorialShani729
 
Interaction design _beyond_human_computer_interaction
Interaction design _beyond_human_computer_interactionInteraction design _beyond_human_computer_interaction
Interaction design _beyond_human_computer_interactionShani729
 
Fm lecturer 13(final)
Fm lecturer 13(final)Fm lecturer 13(final)
Fm lecturer 13(final)Shani729
 
Lecture slides week14-15
Lecture slides week14-15Lecture slides week14-15
Lecture slides week14-15Shani729
 
Frequent itemset mining using pattern growth method
Frequent itemset mining using pattern growth methodFrequent itemset mining using pattern growth method
Frequent itemset mining using pattern growth methodShani729
 
Dwh lecture slides-week15
Dwh lecture slides-week15Dwh lecture slides-week15
Dwh lecture slides-week15Shani729
 
Dwh lecture slides-week10
Dwh lecture slides-week10Dwh lecture slides-week10
Dwh lecture slides-week10Shani729
 
Dwh lecture slidesweek7&8
Dwh lecture slidesweek7&8Dwh lecture slidesweek7&8
Dwh lecture slidesweek7&8Shani729
 
Dwh lecture slides-week5&6
Dwh lecture slides-week5&6Dwh lecture slides-week5&6
Dwh lecture slides-week5&6Shani729
 
Dwh lecture slides-week3&4
Dwh lecture slides-week3&4Dwh lecture slides-week3&4
Dwh lecture slides-week3&4Shani729
 
Dwh lecture slides-week2
Dwh lecture slides-week2Dwh lecture slides-week2
Dwh lecture slides-week2Shani729
 
Dwh lecture slides-week1
Dwh lecture slides-week1Dwh lecture slides-week1
Dwh lecture slides-week1Shani729
 
Dwh lecture slides-week 13
Dwh lecture slides-week 13Dwh lecture slides-week 13
Dwh lecture slides-week 13Shani729
 
Dwh lecture slides-week 12&13
Dwh lecture slides-week 12&13Dwh lecture slides-week 12&13
Dwh lecture slides-week 12&13Shani729
 
Data warehousing and mining furc
Data warehousing and mining furcData warehousing and mining furc
Data warehousing and mining furcShani729
 
Lecture 40
Lecture 40Lecture 40
Lecture 40Shani729
 
Lecture 39
Lecture 39Lecture 39
Lecture 39Shani729
 
Lecture 38
Lecture 38Lecture 38
Lecture 38Shani729
 
Lecture 37
Lecture 37Lecture 37
Lecture 37Shani729
 

Plus de Shani729 (20)

Python tutorialfeb152012
Python tutorialfeb152012Python tutorialfeb152012
Python tutorialfeb152012
 
Python tutorial
Python tutorialPython tutorial
Python tutorial
 
Interaction design _beyond_human_computer_interaction
Interaction design _beyond_human_computer_interactionInteraction design _beyond_human_computer_interaction
Interaction design _beyond_human_computer_interaction
 
Fm lecturer 13(final)
Fm lecturer 13(final)Fm lecturer 13(final)
Fm lecturer 13(final)
 
Lecture slides week14-15
Lecture slides week14-15Lecture slides week14-15
Lecture slides week14-15
 
Frequent itemset mining using pattern growth method
Frequent itemset mining using pattern growth methodFrequent itemset mining using pattern growth method
Frequent itemset mining using pattern growth method
 
Dwh lecture slides-week15
Dwh lecture slides-week15Dwh lecture slides-week15
Dwh lecture slides-week15
 
Dwh lecture slides-week10
Dwh lecture slides-week10Dwh lecture slides-week10
Dwh lecture slides-week10
 
Dwh lecture slidesweek7&8
Dwh lecture slidesweek7&8Dwh lecture slidesweek7&8
Dwh lecture slidesweek7&8
 
Dwh lecture slides-week5&6
Dwh lecture slides-week5&6Dwh lecture slides-week5&6
Dwh lecture slides-week5&6
 
Dwh lecture slides-week3&4
Dwh lecture slides-week3&4Dwh lecture slides-week3&4
Dwh lecture slides-week3&4
 
Dwh lecture slides-week2
Dwh lecture slides-week2Dwh lecture slides-week2
Dwh lecture slides-week2
 
Dwh lecture slides-week1
Dwh lecture slides-week1Dwh lecture slides-week1
Dwh lecture slides-week1
 
Dwh lecture slides-week 13
Dwh lecture slides-week 13Dwh lecture slides-week 13
Dwh lecture slides-week 13
 
Dwh lecture slides-week 12&13
Dwh lecture slides-week 12&13Dwh lecture slides-week 12&13
Dwh lecture slides-week 12&13
 
Data warehousing and mining furc
Data warehousing and mining furcData warehousing and mining furc
Data warehousing and mining furc
 
Lecture 40
Lecture 40Lecture 40
Lecture 40
 
Lecture 39
Lecture 39Lecture 39
Lecture 39
 
Lecture 38
Lecture 38Lecture 38
Lecture 38
 
Lecture 37
Lecture 37Lecture 37
Lecture 37
 

Dernier

Carbohydrates principles of biochemistry
Carbohydrates principles of biochemistryCarbohydrates principles of biochemistry
Carbohydrates principles of biochemistryKomakeTature
 
Power System electrical and electronics .pptx
Power System electrical and electronics .pptxPower System electrical and electronics .pptx
Power System electrical and electronics .pptxMUKULKUMAR210
 
solar wireless electric vechicle charging system
solar wireless electric vechicle charging systemsolar wireless electric vechicle charging system
solar wireless electric vechicle charging systemgokuldongala
 
How to Write a Good Scientific Paper.pdf
How to Write a Good Scientific Paper.pdfHow to Write a Good Scientific Paper.pdf
How to Write a Good Scientific Paper.pdfRedhwan Qasem Shaddad
 
Guardians and Glitches: Navigating the Duality of Gen AI in AppSec
Guardians and Glitches: Navigating the Duality of Gen AI in AppSecGuardians and Glitches: Navigating the Duality of Gen AI in AppSec
Guardians and Glitches: Navigating the Duality of Gen AI in AppSecTrupti Shiralkar, CISSP
 
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...Amil baba
 
Phase noise transfer functions.pptx
Phase noise transfer      functions.pptxPhase noise transfer      functions.pptx
Phase noise transfer functions.pptxSaiGouthamSunkara
 
SATELITE COMMUNICATION UNIT 1 CEC352 REGULATION 2021 PPT BASICS OF SATELITE ....
SATELITE COMMUNICATION UNIT 1 CEC352 REGULATION 2021 PPT BASICS OF SATELITE ....SATELITE COMMUNICATION UNIT 1 CEC352 REGULATION 2021 PPT BASICS OF SATELITE ....
SATELITE COMMUNICATION UNIT 1 CEC352 REGULATION 2021 PPT BASICS OF SATELITE ....santhyamuthu1
 
The relationship between iot and communication technology
The relationship between iot and communication technologyThe relationship between iot and communication technology
The relationship between iot and communication technologyabdulkadirmukarram03
 
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...Sean Meyn
 
GENERAL CONDITIONS FOR CONTRACTS OF CIVIL ENGINEERING WORKS
GENERAL CONDITIONS  FOR  CONTRACTS OF CIVIL ENGINEERING WORKS GENERAL CONDITIONS  FOR  CONTRACTS OF CIVIL ENGINEERING WORKS
GENERAL CONDITIONS FOR CONTRACTS OF CIVIL ENGINEERING WORKS Bahzad5
 
Design Analysis of Alogorithm 1 ppt 2024.pptx
Design Analysis of Alogorithm 1 ppt 2024.pptxDesign Analysis of Alogorithm 1 ppt 2024.pptx
Design Analysis of Alogorithm 1 ppt 2024.pptxrajesshs31r
 
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdfSummer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdfNaveenVerma126
 
ChatGPT-and-Generative-AI-Landscape Working of generative ai search
ChatGPT-and-Generative-AI-Landscape Working of generative ai searchChatGPT-and-Generative-AI-Landscape Working of generative ai search
ChatGPT-and-Generative-AI-Landscape Working of generative ai searchrohitcse52
 
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptx
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptxVertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptx
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptxLMW Machine Tool Division
 
Lecture 1: Basics of trigonometry (surveying)
Lecture 1: Basics of trigonometry (surveying)Lecture 1: Basics of trigonometry (surveying)
Lecture 1: Basics of trigonometry (surveying)Bahzad5
 
Graphics Primitives and CG Display Devices
Graphics Primitives and CG Display DevicesGraphics Primitives and CG Display Devices
Graphics Primitives and CG Display DevicesDIPIKA83
 

Dernier (20)

Carbohydrates principles of biochemistry
Carbohydrates principles of biochemistryCarbohydrates principles of biochemistry
Carbohydrates principles of biochemistry
 
Power System electrical and electronics .pptx
Power System electrical and electronics .pptxPower System electrical and electronics .pptx
Power System electrical and electronics .pptx
 
solar wireless electric vechicle charging system
solar wireless electric vechicle charging systemsolar wireless electric vechicle charging system
solar wireless electric vechicle charging system
 
How to Write a Good Scientific Paper.pdf
How to Write a Good Scientific Paper.pdfHow to Write a Good Scientific Paper.pdf
How to Write a Good Scientific Paper.pdf
 
Guardians and Glitches: Navigating the Duality of Gen AI in AppSec
Guardians and Glitches: Navigating the Duality of Gen AI in AppSecGuardians and Glitches: Navigating the Duality of Gen AI in AppSec
Guardians and Glitches: Navigating the Duality of Gen AI in AppSec
 
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
Popular-NO1 Kala Jadu Expert Specialist In Germany Kala Jadu Expert Specialis...
 
Phase noise transfer functions.pptx
Phase noise transfer      functions.pptxPhase noise transfer      functions.pptx
Phase noise transfer functions.pptx
 
Lecture 2 .pptx
Lecture 2                            .pptxLecture 2                            .pptx
Lecture 2 .pptx
 
SATELITE COMMUNICATION UNIT 1 CEC352 REGULATION 2021 PPT BASICS OF SATELITE ....
SATELITE COMMUNICATION UNIT 1 CEC352 REGULATION 2021 PPT BASICS OF SATELITE ....SATELITE COMMUNICATION UNIT 1 CEC352 REGULATION 2021 PPT BASICS OF SATELITE ....
SATELITE COMMUNICATION UNIT 1 CEC352 REGULATION 2021 PPT BASICS OF SATELITE ....
 
The relationship between iot and communication technology
The relationship between iot and communication technologyThe relationship between iot and communication technology
The relationship between iot and communication technology
 
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
Quasi-Stochastic Approximation: Algorithm Design Principles with Applications...
 
GENERAL CONDITIONS FOR CONTRACTS OF CIVIL ENGINEERING WORKS
GENERAL CONDITIONS  FOR  CONTRACTS OF CIVIL ENGINEERING WORKS GENERAL CONDITIONS  FOR  CONTRACTS OF CIVIL ENGINEERING WORKS
GENERAL CONDITIONS FOR CONTRACTS OF CIVIL ENGINEERING WORKS
 
Design Analysis of Alogorithm 1 ppt 2024.pptx
Design Analysis of Alogorithm 1 ppt 2024.pptxDesign Analysis of Alogorithm 1 ppt 2024.pptx
Design Analysis of Alogorithm 1 ppt 2024.pptx
 
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdfSummer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
Summer training report on BUILDING CONSTRUCTION for DIPLOMA Students.pdf
 
ChatGPT-and-Generative-AI-Landscape Working of generative ai search
ChatGPT-and-Generative-AI-Landscape Working of generative ai searchChatGPT-and-Generative-AI-Landscape Working of generative ai search
ChatGPT-and-Generative-AI-Landscape Working of generative ai search
 
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptx
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptxVertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptx
Vertical- Machining - Center - VMC -LMW-Machine-Tool-Division.pptx
 
Présentation IIRB 2024 Marine Cordonnier.pdf
Présentation IIRB 2024 Marine Cordonnier.pdfPrésentation IIRB 2024 Marine Cordonnier.pdf
Présentation IIRB 2024 Marine Cordonnier.pdf
 
Lecture 1: Basics of trigonometry (surveying)
Lecture 1: Basics of trigonometry (surveying)Lecture 1: Basics of trigonometry (surveying)
Lecture 1: Basics of trigonometry (surveying)
 
Graphics Primitives and CG Display Devices
Graphics Primitives and CG Display DevicesGraphics Primitives and CG Display Devices
Graphics Primitives and CG Display Devices
 
Présentation IIRB 2024 Chloe Dufrane.pdf
Présentation IIRB 2024 Chloe Dufrane.pdfPrésentation IIRB 2024 Chloe Dufrane.pdf
Présentation IIRB 2024 Chloe Dufrane.pdf
 

Lecture 28

Notes de l'éditeur

  1. <number>
  2. <number> Pictorial representation of the nested loop join algorithm.
  3. Implementation Strategies [3] Top Down approach: It is generally useful for projects where the technology is mature and well understood, as well as where the business problems that must be solved are clear and well understood. A Bottom Up approach,: is useful, on the other hand, in making technology assessments and is a good technique for organizations that are not leading edge technology implementers. This approach is used when the business objectives that are to be met by the data warehouse are unclear, or when the current or proposed business process will be affected by the data warehouse. Development Methodologies A Development Methodology describes the expected evolution and management of the engineering system. Waterfall Model: The model is a linear sequence comprised of the stages like requirements definition, system design, detailed design, integration and testing, and finally operations and maintenance. This model is used when the system requirements and objectives are known and clearly specified. · Spiral Model: The model is a sequence of waterfall models which corresponds to a risk oriented iterative enhancement, and it recognizes that requirements are not always available and clear when the system is first implemented [3]. RAD: Rapid Application Development (RAD) is an iterative model consisting of steps like scope, analyze, design, construct, test, implement, and review . Since designing and building a data warehouse is an iterative process, the spiral method is the best development methodology [3] . The iterative RAD process is much better suited to the development of a data warehouse. Development and delivery of early prototypes will drive future requirements as business users are given direct access to information and the ability to manipulate it. Management of expectations requires that the content of the data warehouse be clearly communicated for each iteration [4]. While one can use the traditional waterfall approach to developing a data warehouse, there are several drawbacks. First and foremost, the project is likely to occur over an extended period of time, during which the users may not have had an opportunity to review what will be delivered. Second, in today's demanding competitive environment there is a need to produce results in a much shorter timeframe [4].