SlideShare une entreprise Scribd logo
1  sur  17
DATAWAREHOUSING AND MINING

BY
G.RAJESH CHANDRA
EVOLUTION OF DATABASE TECHNOLOGY


1960s (Primitive File Processing)




1970s to early 1980s (DBMS)




Data collection, database creation, IMS and network DBMS
Relational data model, relational DBMS implementation ,SQL,
OLTP,User Interfaces.etc

1980s: to Present (Advanced Data Bases)






RDBMS, advanced data models (extended-relational, OO, deductive,
etc.)

Application-oriented DBMS (spatial, scientific, engineering, etc.)

1990s: (Advanced Data Analysis)




Data mining, data warehousing, multimedia databases, and Web
databases

2000s


Stream data management and mining



Data mining and its applications
WHY MINE DATA? COMMERCIAL VIEWPOINT


Lots of data is being collected
and warehoused






Web data, e-commerce
purchases at department/
grocery stores
Bank/Credit Card
transactions

Competitive Pressure is Strong


Provide better, customized services for an edge (e.g. in
Customer Relationship Management)
WHAT IS DATA MINING…..?


•

Data mining (sometimes called data
Discovery or Knowledge Discovery Data)
is the process of analyzing data from
different perspectives and summarizing it
into useful information.
Extraction of interesting (non-trivial,
implicit, previously unknown and
potentially useful) patterns or knowledge
from huge amount of data
WHY MINE DATA? SCIENTIFIC VIEWPOINT


Data collected and stored at
enormous speeds (GB/hour)








remote sensors on a satellite
telescopes scanning the skies
microarrays generating gene
expression data
scientific simulations
generating terabytes of data

Traditional techniques infeasible for raw
data
Data mining may help scientists



in classifying and segmenting data
in Hypothesis Formation
EXAMPLES: WHAT IS (NOT) DATA MINING?
 What is not Data

 What is Data Mining?

Mining?

– Look up phone

– Certain names are more

number in phone
directory

prevalent in certain US locations
(O’Brien, O’Rurke, O’Reilly… in
Boston area)

– Query a Web

– Group together similar
documents returned by search
engine according to their context
(e.g. Amazon rainforest,
Amazon.com,)

search engine for
information about
―Amazon‖
DATA MINING IS ALSO CALLED AS..?
•

•

Knowledge discovery (mining) in
databases (KDD), knowledge extraction,
data/pattern analysis, data archeology,
data dredging, information harvesting,
business intelligence, etc.
Real Time Example Gold Mining
DATA WARE HOUSE = COLLECTION OF DATA BASES
WE HAVE TO USE DIFFERENT METHODS
RAW DATA =DATA BASES + NOISE DATA
DATA SELECTION AND TRANSFORMATION
DATA CLEANING AND INTEGRATION
DATA MINING
PATTERN EVALUATION
KNOWLEDGE REPRASENTATION
KNOWLEDGE REPRASENTATION
December 26, 2013

KNOWLEDGE DISCOVERY (KDD) PROCESS
 Data

mining—core of
knowledge discovery
process

Pattern Evaluation

Data Mining
Task-relevant Data
Data Warehouse
Data Cleaning
Data Integration
Databases

Selection

Contenu connexe

Tendances

Dbms Existentes
Dbms ExistentesDbms Existentes
Dbms Existentes
eder8
 
Registros de control y estados de la CPU
Registros de control y estados de la CPURegistros de control y estados de la CPU
Registros de control y estados de la CPU
Ivan Porras
 
Conexion servidor con Samba
Conexion servidor con SambaConexion servidor con Samba
Conexion servidor con Samba
The Killer
 
Uso de modelos en capas
Uso de modelos en capasUso de modelos en capas
Uso de modelos en capas
EliasRamosMendez
 
Database management system by Neeraj Bhandari ( Surkhet.Nepal )
Database management system by Neeraj Bhandari ( Surkhet.Nepal )Database management system by Neeraj Bhandari ( Surkhet.Nepal )
Database management system by Neeraj Bhandari ( Surkhet.Nepal )
Neeraj Bhandari
 
Database management system chapter16
Database management system chapter16Database management system chapter16
Database management system chapter16
Md. Mahedi Mahfuj
 

Tendances (20)

Inter process communication
Inter process communicationInter process communication
Inter process communication
 
Dbms Existentes
Dbms ExistentesDbms Existentes
Dbms Existentes
 
Analizar mediante-ejemplos-de-la-vida-real-el-concepto-de-procesos
Analizar mediante-ejemplos-de-la-vida-real-el-concepto-de-procesosAnalizar mediante-ejemplos-de-la-vida-real-el-concepto-de-procesos
Analizar mediante-ejemplos-de-la-vida-real-el-concepto-de-procesos
 
Dbms
DbmsDbms
Dbms
 
Ordbms
OrdbmsOrdbms
Ordbms
 
Soa unit-1-well formed and valid document08.07.2019
Soa unit-1-well formed and valid document08.07.2019Soa unit-1-well formed and valid document08.07.2019
Soa unit-1-well formed and valid document08.07.2019
 
Base de datos
Base de datosBase de datos
Base de datos
 
Tema 4: Procesamiento paralelo.
Tema 4: Procesamiento paralelo.Tema 4: Procesamiento paralelo.
Tema 4: Procesamiento paralelo.
 
Registros de control y estados de la CPU
Registros de control y estados de la CPURegistros de control y estados de la CPU
Registros de control y estados de la CPU
 
Data Replication in Distributed System
Data Replication in  Distributed SystemData Replication in  Distributed System
Data Replication in Distributed System
 
Conexion servidor con Samba
Conexion servidor con SambaConexion servidor con Samba
Conexion servidor con Samba
 
Resumen Unidades 16/17/18 So Tanembau
Resumen Unidades 16/17/18 So TanembauResumen Unidades 16/17/18 So Tanembau
Resumen Unidades 16/17/18 So Tanembau
 
Usuarios y administrador de bases de datos
Usuarios y administrador de bases de datosUsuarios y administrador de bases de datos
Usuarios y administrador de bases de datos
 
Funciones de DBA Y Tipos de base de datos
Funciones de DBA Y Tipos de base de datosFunciones de DBA Y Tipos de base de datos
Funciones de DBA Y Tipos de base de datos
 
Advantages of DBMS
Advantages of DBMSAdvantages of DBMS
Advantages of DBMS
 
Uso de modelos en capas
Uso de modelos en capasUso de modelos en capas
Uso de modelos en capas
 
Database management system by Neeraj Bhandari ( Surkhet.Nepal )
Database management system by Neeraj Bhandari ( Surkhet.Nepal )Database management system by Neeraj Bhandari ( Surkhet.Nepal )
Database management system by Neeraj Bhandari ( Surkhet.Nepal )
 
Architecture of dbms(lecture 3)
Architecture of dbms(lecture 3)Architecture of dbms(lecture 3)
Architecture of dbms(lecture 3)
 
Temporal databases
Temporal databasesTemporal databases
Temporal databases
 
Database management system chapter16
Database management system chapter16Database management system chapter16
Database management system chapter16
 

En vedette

introduction to datawarehouse
introduction to datawarehouseintroduction to datawarehouse
introduction to datawarehouse
kiran14360
 
Data warehousing
Data warehousingData warehousing
Data warehousing
Varun Jain
 
Data Warehousing & Basic Architectural Framework
Data Warehousing & Basic Architectural FrameworkData Warehousing & Basic Architectural Framework
Data Warehousing & Basic Architectural Framework
Dr. Sunil Kr. Pandey
 
Introduction to Data Warehousing
Introduction to Data WarehousingIntroduction to Data Warehousing
Introduction to Data Warehousing
Eyad Manna
 
Data warehouse architecture
Data warehouse architectureData warehouse architecture
Data warehouse architecture
pcherukumalla
 

En vedette (16)

DATA WAREHOUSING AND DATA MINING
DATA WAREHOUSING AND DATA MININGDATA WAREHOUSING AND DATA MINING
DATA WAREHOUSING AND DATA MINING
 
data warehousing and data mining
data warehousing and data mining data warehousing and data mining
data warehousing and data mining
 
Data mining and data warehousing
Data mining and data warehousingData mining and data warehousing
Data mining and data warehousing
 
introduction to datawarehouse
introduction to datawarehouseintroduction to datawarehouse
introduction to datawarehouse
 
Data warehousing
Data warehousingData warehousing
Data warehousing
 
Session7part1
Session7part1Session7part1
Session7part1
 
An introduction to data warehousing
An introduction to data warehousingAn introduction to data warehousing
An introduction to data warehousing
 
Introduction to Data Warehousing
Introduction to Data WarehousingIntroduction to Data Warehousing
Introduction to Data Warehousing
 
Data Warehousing & Basic Architectural Framework
Data Warehousing & Basic Architectural FrameworkData Warehousing & Basic Architectural Framework
Data Warehousing & Basic Architectural Framework
 
Introduction to Data Warehousing
Introduction to Data WarehousingIntroduction to Data Warehousing
Introduction to Data Warehousing
 
Data warehousing and Data mining
Data warehousing and Data mining Data warehousing and Data mining
Data warehousing and Data mining
 
DATA WAREHOUSING
DATA WAREHOUSINGDATA WAREHOUSING
DATA WAREHOUSING
 
Data Mining and Data Warehousing
Data Mining and Data WarehousingData Mining and Data Warehousing
Data Mining and Data Warehousing
 
Data warehouse architecture
Data warehouse architectureData warehouse architecture
Data warehouse architecture
 
Introduction to Data Warehousing
Introduction to Data WarehousingIntroduction to Data Warehousing
Introduction to Data Warehousing
 
Data Warehousing and Data Mining
Data Warehousing and Data MiningData Warehousing and Data Mining
Data Warehousing and Data Mining
 

Similaire à introduction to data warehousing and mining

Chapter 1. Introduction
Chapter 1. IntroductionChapter 1. Introduction
Chapter 1. Introduction
butest
 
Dwdmunit1 a
Dwdmunit1 aDwdmunit1 a
Dwdmunit1 a
bhagathk
 

Similaire à introduction to data warehousing and mining (20)

Lect 1 introduction
Lect 1 introductionLect 1 introduction
Lect 1 introduction
 
Data mining
Data miningData mining
Data mining
 
Lect 1 introduction
Lect 1 introductionLect 1 introduction
Lect 1 introduction
 
Introduction to data warehouse
Introduction to data warehouseIntroduction to data warehouse
Introduction to data warehouse
 
Dm unit i r16
Dm unit i   r16Dm unit i   r16
Dm unit i r16
 
Chapter 1. Introduction
Chapter 1. IntroductionChapter 1. Introduction
Chapter 1. Introduction
 
Dwdmunit1 a
Dwdmunit1 aDwdmunit1 a
Dwdmunit1 a
 
Knowledge discovery thru data mining
Knowledge discovery thru data miningKnowledge discovery thru data mining
Knowledge discovery thru data mining
 
Chapter 1. Introduction.ppt
Chapter 1. Introduction.pptChapter 1. Introduction.ppt
Chapter 1. Introduction.ppt
 
Data Mining @ BSU Malolos 2019
Data Mining @ BSU Malolos 2019Data Mining @ BSU Malolos 2019
Data Mining @ BSU Malolos 2019
 
Data Warehouse and Data Mining
Data Warehouse and Data MiningData Warehouse and Data Mining
Data Warehouse and Data Mining
 
Cs501 dm intro
Cs501 dm introCs501 dm intro
Cs501 dm intro
 
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 Introduction
Data mining IntroductionData mining Introduction
Data mining Introduction
 
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
 
6months industrial training in data mining,ludhiana
6months industrial training in data mining,ludhiana6months industrial training in data mining,ludhiana
6months industrial training in data mining,ludhiana
 

Dernier

Gardella_Mateo_IntellectualProperty.pdf.
Gardella_Mateo_IntellectualProperty.pdf.Gardella_Mateo_IntellectualProperty.pdf.
Gardella_Mateo_IntellectualProperty.pdf.
MateoGardella
 
Gardella_PRCampaignConclusion Pitch Letter
Gardella_PRCampaignConclusion Pitch LetterGardella_PRCampaignConclusion Pitch Letter
Gardella_PRCampaignConclusion Pitch Letter
MateoGardella
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
heathfieldcps1
 
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 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
QucHHunhnh
 

Dernier (20)

psychiatric nursing HISTORY COLLECTION .docx
psychiatric  nursing HISTORY  COLLECTION  .docxpsychiatric  nursing HISTORY  COLLECTION  .docx
psychiatric nursing HISTORY COLLECTION .docx
 
Application orientated numerical on hev.ppt
Application orientated numerical on hev.pptApplication orientated numerical on hev.ppt
Application orientated numerical on hev.ppt
 
Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"
Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"
Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"
 
Gardella_Mateo_IntellectualProperty.pdf.
Gardella_Mateo_IntellectualProperty.pdf.Gardella_Mateo_IntellectualProperty.pdf.
Gardella_Mateo_IntellectualProperty.pdf.
 
Unit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptxUnit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptx
 
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
 
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
Ecological Succession. ( ECOSYSTEM, B. Pharmacy, 1st Year, Sem-II, Environmen...
 
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
 
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
 
Gardella_PRCampaignConclusion Pitch Letter
Gardella_PRCampaignConclusion Pitch LetterGardella_PRCampaignConclusion Pitch Letter
Gardella_PRCampaignConclusion Pitch Letter
 
Z Score,T Score, Percential Rank and Box Plot Graph
Z Score,T Score, Percential Rank and Box Plot GraphZ Score,T Score, Percential Rank and Box Plot Graph
Z Score,T Score, Percential Rank and Box Plot Graph
 
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
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
 
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
 
How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17How to Give a Domain for a Field in Odoo 17
How to Give a Domain for a Field in Odoo 17
 
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
 
Class 11th Physics NEET formula sheet pdf
Class 11th Physics NEET formula sheet pdfClass 11th Physics NEET formula sheet pdf
Class 11th Physics NEET formula sheet pdf
 
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
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy Consulting
 
1029 - Danh muc Sach Giao Khoa 10 . pdf
1029 -  Danh muc Sach Giao Khoa 10 . pdf1029 -  Danh muc Sach Giao Khoa 10 . pdf
1029 - Danh muc Sach Giao Khoa 10 . pdf
 

introduction to data warehousing and mining

  • 2. EVOLUTION OF DATABASE TECHNOLOGY  1960s (Primitive File Processing)   1970s to early 1980s (DBMS)   Data collection, database creation, IMS and network DBMS Relational data model, relational DBMS implementation ,SQL, OLTP,User Interfaces.etc 1980s: to Present (Advanced Data Bases)    RDBMS, advanced data models (extended-relational, OO, deductive, etc.) Application-oriented DBMS (spatial, scientific, engineering, etc.) 1990s: (Advanced Data Analysis)   Data mining, data warehousing, multimedia databases, and Web databases 2000s  Stream data management and mining  Data mining and its applications
  • 3. WHY MINE DATA? COMMERCIAL VIEWPOINT  Lots of data is being collected and warehoused     Web data, e-commerce purchases at department/ grocery stores Bank/Credit Card transactions Competitive Pressure is Strong  Provide better, customized services for an edge (e.g. in Customer Relationship Management)
  • 4. WHAT IS DATA MINING…..?  • Data mining (sometimes called data Discovery or Knowledge Discovery Data) is the process of analyzing data from different perspectives and summarizing it into useful information. Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data
  • 5. WHY MINE DATA? SCIENTIFIC VIEWPOINT  Data collected and stored at enormous speeds (GB/hour)       remote sensors on a satellite telescopes scanning the skies microarrays generating gene expression data scientific simulations generating terabytes of data Traditional techniques infeasible for raw data Data mining may help scientists   in classifying and segmenting data in Hypothesis Formation
  • 6. EXAMPLES: WHAT IS (NOT) DATA MINING?  What is not Data  What is Data Mining? Mining? – Look up phone – Certain names are more number in phone directory prevalent in certain US locations (O’Brien, O’Rurke, O’Reilly… in Boston area) – Query a Web – Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com,) search engine for information about ―Amazon‖
  • 7. DATA MINING IS ALSO CALLED AS..? • • Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. Real Time Example Gold Mining
  • 8. DATA WARE HOUSE = COLLECTION OF DATA BASES
  • 9. WE HAVE TO USE DIFFERENT METHODS
  • 10. RAW DATA =DATA BASES + NOISE DATA
  • 11. DATA SELECTION AND TRANSFORMATION
  • 12. DATA CLEANING AND INTEGRATION
  • 17. December 26, 2013 KNOWLEDGE DISCOVERY (KDD) PROCESS  Data mining—core of knowledge discovery process Pattern Evaluation Data Mining Task-relevant Data Data Warehouse Data Cleaning Data Integration Databases Selection