SlideShare a Scribd company logo
1 of 59
McGraw-Hill/Irwin Copyright © 2008, The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin Copyright © 2008 by The McGraw-Hill Companies, Inc. All rights reserved.
McGraw-Hill/Irwin Copyright © 2008, The McGraw-Hill Companies, Inc. All rights reserved.
Data Resource ManagementData Resource Management
Chapter
5
Chapter
5
5-3
• Explain the business value of implementing
data resource management processes and
technologies in an organization
• Outline the advantages of a database
management approach to managing the data
resources of a business, compared to a file
processing approach
• Explain how database management software
helps business professionals and supports the
operations and management of a business
Learning ObjectivesLearning Objectives
5-4
Learning ObjectivesLearning Objectives
• Provide examples to illustrate the following
concepts
• Major types of databases
• Data warehouses and data mining
• Logical data elements
• Fundamental database structures
• Database development
5-5
Case 1: Sharing Business DatabasesCase 1: Sharing Business Databases
• Amazon’s data vault
• Product descriptions
• Prices
• Sales rankings
• Customer reviews
• Inventory figures
• Countless other layers of content
• Took 10 years and a billion dollars to build
5-6
Case 1: Sharing Business DatabasesCase 1: Sharing Business Databases
• Amazon opened its data vault in 2002
• 65,000 developers, businesses, and entrepreneurs
have tapped into it
• Many have become ambitious business partners
• eBay opened its $3 billion databases in 2003
• 15,000 developers and others have registered
to use it and to access software features
• 1,000 new applications have appeared
• 41 percent of eBay’s listings are uploaded to
the site using these resources
5-7
Case 1: Sharing Business DatabasesCase 1: Sharing Business Databases
• Google recently unlocked access to its desktop
and paid-search products
• Dozens of Google-driven services cropped up
• Developers can grab 1,000 search results a day
for free; anything more requires permission
• In 2005, the Ad-Words paid-search service
was opened to outside applications
5-8
Case Study QuestionsCase Study Questions
• What are the business benefits to Amazon and
eBay of opening up some of their databases to
developers and entrepreneurs?
• Do you agree with this strategy?
• What business factors are causing Google to
move slowly in opening up its databases?
• Do you agree with its go-slow strategy?
5-9
Case Study QuestionsCase Study Questions
• Should other companies follow Amazon and
eBay’s lead and open up some of their databases
to developers and others?
• Defend your position with an example of the risks
and benefits to an actual company
5-10
Logical Data ElementsLogical Data Elements
5-11
Logical Data ElementsLogical Data Elements
• Character
• A single alphabetic, numeric, or other symbol
• Field or data item
• Represents an attribute (characteristic or quality)
of some entity (object, person, place, event)
• Examples: salary, job title
• Record
• Grouping of all the fields used to describe the
attributes of an entity
• Example: payroll record with name, SSN, pay rate
5-12
Logical Data ElementsLogical Data Elements
• File or table
• A group of related records
• Database
• An integrated collection of logically related
data elements
5-13
Electric Utility DatabaseElectric Utility Database
5-14
Database StructuresDatabase Structures
• Common database structures…
• Hierarchical
• Network
• Relational
• Object-oriented
• Multi-dimensional
5-15
Hierarchical StructureHierarchical Structure
• Early DBMS structure
• Records arranged in tree-like structure
• Relationships are one-to-many
5-16
Network StructureNetwork Structure
• Used in some mainframe DBMS packages
• Many-to-many relationships
5-17
Relational StructureRelational Structure
• Most widely used structure
• Data elements are stored in tables
• Row represents a record; column is a field
• Can relate data in one file with data in another,
if both files share a common data element
5-18
Relational OperationsRelational Operations
• Select
• Create a subset of records that meet a stated
criterion
• Example: employees earning more than $30,000
• Join
• Combine two or more tables temporarily
• Looks like one big table
• Project
• Create a subset of columns in a table
5-19
Multidimensional StructureMultidimensional Structure
• Variation of relational model
• Uses multidimensional structures to
organize data
• Data elements are viewed as being in cubes
• Popular for analytical databases that support
Online Analytical Processing (OLAP)
5-20
Multidimensional ModelMultidimensional Model
5-21
Object-Oriented StructureObject-Oriented Structure
• An object consists of
• Data values describing the attributes of an entity
• Operations that can be performed on the data
• Encapsulation
• Combine data and operations
• Inheritance
• New objects can be created by replicating some
or all of the characteristics of parent objects
5-22
Object-Oriented StructureObject-Oriented Structure
Source: Adapted from Ivar Jacobsen, Maria Ericsson, and Ageneta Jacobsen, The Object Advantage: Business Process
Reengineering with Object Technology (New York: ACM Press, 1995), p. 65.
Copyright @ 1995, Association for Computing Machinery. By permission.
5-23
Object-Oriented StructureObject-Oriented Structure
• Used in object-oriented database management
systems (OODBMS)
• Supports complex data types more efficiently
than relational databases
• Examples: graphic images, video clips,
web pages
5-24
Evaluation of Database StructuresEvaluation of Database Structures
• Hierarchical
• Works for structured, routine transactions
• Can’t handle many-to-many relationship
• Network
• More flexible than hierarchical
• Unable to handle ad hoc requests
• Relational
• Easily responds to ad hoc requests
• Easier to work with and maintain
• Not as efficient/quick as hierarchical or network
5-25
Database DevelopmentDatabase Development
• Database Administrator (DBA)
• In charge of enterprise database development
• Improves the integrity and security of
organizational databases
• Uses Data Definition Language (DDL) to develop
and specify data contents, relationships, and
structure
• Stores these specifications in a data dictionary
or a metadata repository
5-26
Data DictionaryData Dictionary
• A data dictionary
• Contains data about data (metadata)
• Relies on specialized software component to
manage a database of data definitions
• It contains information on..
• The names and descriptions of all types of data
records and their interrelationships
• Requirements for end users’ access and use of
application programs
• Database maintenance
• Security
5-27
Database DevelopmentDatabase Development
5-28
Data Planning ProcessData Planning Process
• Database development is a top-down process
• Develop an enterprise model that defines the
basic business process of the enterprise
• Define the information needs of end users in
a business process
• Identify the key data elements that are needed
to perform specific business activities
(entity relationship diagrams)
5-29
Entity Relationship DiagramEntity Relationship Diagram
5-30
Database Design ProcessDatabase Design Process
• Data relationships are represented in a data
model that supports a business process
• This model is the schema or subschema on
which to base…
• The physical design of the database
• The development of application programs to
support business processes
5-31
Database Design ProcessDatabase Design Process
• Logical Design
• Schema - overall logical view of relationships
• Subschema - logical view for specific end users
• Data models for DBMS
• Physical Design
• How data are to be physically stored and
accessed on storage devices
5-32
Logical and Physical Database ViewsLogical and Physical Database Views
5-33
Types of DatabasesTypes of Databases
5-34
Operational DatabasesOperational Databases
• Stores detailed data needed to support business
processes and operations
• Also called subject area databases (SADB),
transaction databases, and production
databases
• Database examples: customer, human resource,
inventory
5-35
Distributed DatabasesDistributed Databases
• Distributed databases are copies or parts of
databases stored on servers at multiple locations
• Improves database performance at worksites
• Advantages
• Protection of valuable data
• Data can be distributed into smaller databases
• Each location has control of its local data
• All locations can access any data, any where
• Disadvantages
• Maintaining data accuracy
5-36
Distributed DatabasesDistributed Databases
• Replication
• Look at each distributed database and find
changes
• Apply changes to each distributed database
• Very complex
• Duplication
• One database is master
• Duplicate the master after hours, in all locations
• Easier to accomplish
5-37
External DatabasesExternal Databases
• Databases available for a fee from commercial
online services, or free from the Web
• Examples: hypermedia databases, statistical
databases, bibliographic and full text databases
• Search engines like Google or Yahoo are
external databases
5-38
Hypermedia DatabasesHypermedia Databases
• A hypermedia database contains
• Hyperlinked pages of multimedia
• Interrelated hypermedia page elements,
rather than interrelated data records
5-39
Components of Web-Based SystemComponents of Web-Based System
5-40
Data WarehousesData Warehouses
• Stores static data that has been extracted from
other databases in an organization
• Central source of data that has been cleaned,
transformed, and cataloged
• Data is used for data mining, analytical
processing, analysis, research, decision support
• Data warehouses may be divided into data marts
• Subsets of data that focus on specific aspects
of a company (department or business process)
5-41
Data Warehouse ComponentsData Warehouse Components
5-42
Applications and Data MartsApplications and Data Marts
5-43
Data MiningData Mining
• Data in data warehouses are analyzed to reveal
hidden patterns and trends
• Market-basket analysis to identify new
product bundles
• Find root cause of qualify or manufacturing
problems
• Prevent customer attrition
• Acquire new customers
• Cross-sell to existing customers
• Profile customers with more accuracy
5-44
Traditional File ProcessingTraditional File Processing
• Data are organized, stored, and processed in
independent files
• Each business application designed to use
specialized data files containing specific
types of data records
• Problems
• Data redundancy
• Lack of data integration
• Data dependence (files, storage devices, software)
• Lack of data integrity or standardization
5-45
Traditional File ProcessingTraditional File Processing
5-46
Database Management ApproachDatabase Management Approach
• The foundation of modern methods of managing
organizational data
• Consolidates data records formerly in separate
files into databases
• Data can be accessed by many different
application programs
• A database management system (DBMS) is the
software interface between users and databases
5-47
Database Management ApproachDatabase Management Approach
5-48
Database Management SystemDatabase Management System
• In mainframe and server computer systems, a
software package that is used to…
• Create new databases and database applications
• Maintain the quality of the data in an
organization’s databases
• Use the databases of an organization to provide
the information needed by end users
5-49
Common DBMS Software ComponentsCommon DBMS Software Components
• Database definition
• Language and graphical tools to define entities,
relationships, integrity constraints, and
authorization rights
• Nonprocedural access
• Language and graphical tools to access data
without complicated coding
• Application development
• Graphical tools to develop menus, data entry
forms, and reports
5-50
Common DBMS Software ComponentsCommon DBMS Software Components
• Procedural language interface
• Language that combines nonprocedural access
with full capabilities of a programming language
• Transaction processing
• Control mechanism prevents interference from
simultaneous users and recovers lost data after
a failure
• Database tuning
• Tools to monitor, improve database performance
5-51
Database Management SystemDatabase Management System
• Database Development
• Defining and organizing the content,
relationships, and structure of the data needed
to build a database
• Database Application Development
• Using DBMS to create prototypes of queries,
forms, reports, Web pages
• Database Maintenance
• Using transaction processing systems and other
tools to add, delete, update, and correct data
5-52
DBMS Major FunctionsDBMS Major Functions
5-53
Database InterrogationDatabase Interrogation
• End users use a DBMS query feature or report
generator
• Response is video display or printed report
• No programming is required
• Query language
• Immediate response to ad hoc data requests
• Report generator
• Quickly specify a format for information you
want to present as a report
5-54
Database InterrogationDatabase Interrogation
• SQL Queries
• Structured, international standard query language
found in many DBMS packages
• Query form is SELECT…FROM…WHERE…
5-55
Database InterrogationDatabase Interrogation
• Boolean Logic
• Developed by George Boole in the mid-1800s
• Used to refine searches to specific information
• Has three logical operators: AND, OR, NOT
• Example
• Cats OR felines AND NOT dogs OR Broadway
5-56
Database InterrogationDatabase Interrogation
• Graphical and Natural Queries
• It is difficult to correctly phrase SQL and other
database language search queries
• Most DBMS packages offer easier-to-use,
point-and-click methods
• Translates queries into SQL commands
• Natural language query statements are similar
to conversational English
5-57
Graphical Query WizardGraphical Query Wizard
5-58
Database MaintenanceDatabase Maintenance
• Accomplished by transaction processing systems
and other applications, with the support of the
DBMS
• Done to reflect new business transactions and
other events
• Updating and correcting data, such as customer
addresses
5-59
Application DevelopmentApplication Development
• Use DBMS software development tools to
develop custom application programs
• Not necessary to develop detailed data-handling
procedures using conventional programming
languages
• Can include data manipulation language (DML)
statements that call on the DBMS to perform
necessary data handling

More Related Content

What's hot

Webinar: Data Modeling and Shortcuts to Success in Scaling Time Series Applic...
Webinar: Data Modeling and Shortcuts to Success in Scaling Time Series Applic...Webinar: Data Modeling and Shortcuts to Success in Scaling Time Series Applic...
Webinar: Data Modeling and Shortcuts to Success in Scaling Time Series Applic...DATAVERSITY
 
Enterprise Master Data Architecture: Design Decisions and Options
Enterprise Master Data Architecture: Design Decisions and OptionsEnterprise Master Data Architecture: Design Decisions and Options
Enterprise Master Data Architecture: Design Decisions and OptionsBoris Otto
 
Rev_3 Components of a Data Warehouse
Rev_3 Components of a Data WarehouseRev_3 Components of a Data Warehouse
Rev_3 Components of a Data WarehouseRyan Andhavarapu
 
Data warehouse architecture
Data warehouse architectureData warehouse architecture
Data warehouse architecturepcherukumalla
 
Big Data Expo 2015 - Barnsten Why Data Modelling is Essential
Big Data Expo 2015 - Barnsten Why Data Modelling is EssentialBig Data Expo 2015 - Barnsten Why Data Modelling is Essential
Big Data Expo 2015 - Barnsten Why Data Modelling is EssentialBigDataExpo
 
SAP Data Services
SAP Data ServicesSAP Data Services
SAP Data ServicesGeetika
 
Dell Technology World - IT as a Business - Multi-Cloud Strategy is your Product
Dell Technology World - IT as a Business - Multi-Cloud Strategy is your ProductDell Technology World - IT as a Business - Multi-Cloud Strategy is your Product
Dell Technology World - IT as a Business - Multi-Cloud Strategy is your ProductManuel "Manny" Rodriguez-Perez
 
Building Search Systems for the Enterprise
Building Search Systems for the EnterpriseBuilding Search Systems for the Enterprise
Building Search Systems for the EnterpriseYunyao Li
 
Nw2008 tips tricks_edw_v10
Nw2008 tips tricks_edw_v10Nw2008 tips tricks_edw_v10
Nw2008 tips tricks_edw_v10Harsha Gowda B R
 
Business Intelligence Presentation 1 (15th March'16)
Business Intelligence Presentation 1 (15th March'16)Business Intelligence Presentation 1 (15th March'16)
Business Intelligence Presentation 1 (15th March'16)Muhammad Fahad
 
Sami patel full_resume
Sami patel full_resumeSami patel full_resume
Sami patel full_resumeJignesh Shah
 
Bi Architecture And Conceptual Framework
Bi Architecture And Conceptual FrameworkBi Architecture And Conceptual Framework
Bi Architecture And Conceptual FrameworkSlava Kokaev
 
Bi Dw Presentation
Bi Dw PresentationBi Dw Presentation
Bi Dw Presentationvickyc
 
Data Warehouse Agility Array Conference2011
Data Warehouse Agility Array Conference2011Data Warehouse Agility Array Conference2011
Data Warehouse Agility Array Conference2011Hans Hultgren
 
Prague data management meetup 2017-02-28
Prague data management meetup 2017-02-28Prague data management meetup 2017-02-28
Prague data management meetup 2017-02-28Martin Bém
 
MGIS 301 Databases and Database Technologies
MGIS 301 Databases and Database TechnologiesMGIS 301 Databases and Database Technologies
MGIS 301 Databases and Database TechnologiesM. Murat Albayrakoglu
 

What's hot (20)

Bi 5
Bi 5Bi 5
Bi 5
 
Webinar: Data Modeling and Shortcuts to Success in Scaling Time Series Applic...
Webinar: Data Modeling and Shortcuts to Success in Scaling Time Series Applic...Webinar: Data Modeling and Shortcuts to Success in Scaling Time Series Applic...
Webinar: Data Modeling and Shortcuts to Success in Scaling Time Series Applic...
 
Enterprise Master Data Architecture: Design Decisions and Options
Enterprise Master Data Architecture: Design Decisions and OptionsEnterprise Master Data Architecture: Design Decisions and Options
Enterprise Master Data Architecture: Design Decisions and Options
 
Rev_3 Components of a Data Warehouse
Rev_3 Components of a Data WarehouseRev_3 Components of a Data Warehouse
Rev_3 Components of a Data Warehouse
 
Data warehouse architecture
Data warehouse architectureData warehouse architecture
Data warehouse architecture
 
8143320263_krishna12
8143320263_krishna128143320263_krishna12
8143320263_krishna12
 
Big Data Expo 2015 - Barnsten Why Data Modelling is Essential
Big Data Expo 2015 - Barnsten Why Data Modelling is EssentialBig Data Expo 2015 - Barnsten Why Data Modelling is Essential
Big Data Expo 2015 - Barnsten Why Data Modelling is Essential
 
SAP Data Services
SAP Data ServicesSAP Data Services
SAP Data Services
 
Data warehouse
Data warehouseData warehouse
Data warehouse
 
Dell Technology World - IT as a Business - Multi-Cloud Strategy is your Product
Dell Technology World - IT as a Business - Multi-Cloud Strategy is your ProductDell Technology World - IT as a Business - Multi-Cloud Strategy is your Product
Dell Technology World - IT as a Business - Multi-Cloud Strategy is your Product
 
Building Search Systems for the Enterprise
Building Search Systems for the EnterpriseBuilding Search Systems for the Enterprise
Building Search Systems for the Enterprise
 
Nw2008 tips tricks_edw_v10
Nw2008 tips tricks_edw_v10Nw2008 tips tricks_edw_v10
Nw2008 tips tricks_edw_v10
 
Business Intelligence Presentation 1 (15th March'16)
Business Intelligence Presentation 1 (15th March'16)Business Intelligence Presentation 1 (15th March'16)
Business Intelligence Presentation 1 (15th March'16)
 
Sami patel full_resume
Sami patel full_resumeSami patel full_resume
Sami patel full_resume
 
[EN] Trends in Records, Document and Enterprise Content Management | Ulrich K...
[EN] Trends in Records, Document and Enterprise Content Management | Ulrich K...[EN] Trends in Records, Document and Enterprise Content Management | Ulrich K...
[EN] Trends in Records, Document and Enterprise Content Management | Ulrich K...
 
Bi Architecture And Conceptual Framework
Bi Architecture And Conceptual FrameworkBi Architecture And Conceptual Framework
Bi Architecture And Conceptual Framework
 
Bi Dw Presentation
Bi Dw PresentationBi Dw Presentation
Bi Dw Presentation
 
Data Warehouse Agility Array Conference2011
Data Warehouse Agility Array Conference2011Data Warehouse Agility Array Conference2011
Data Warehouse Agility Array Conference2011
 
Prague data management meetup 2017-02-28
Prague data management meetup 2017-02-28Prague data management meetup 2017-02-28
Prague data management meetup 2017-02-28
 
MGIS 301 Databases and Database Technologies
MGIS 301 Databases and Database TechnologiesMGIS 301 Databases and Database Technologies
MGIS 301 Databases and Database Technologies
 

Viewers also liked

Phiếu theo dõi thực tập
Phiếu theo dõi thực tậpPhiếu theo dõi thực tập
Phiếu theo dõi thực tậpLong Leemin
 
SKT SOCIAL MANAGER - KANG SE JIN
SKT SOCIAL MANAGER - KANG SE JINSKT SOCIAL MANAGER - KANG SE JIN
SKT SOCIAL MANAGER - KANG SE JINReina Takizawa
 
Minercfat Mods
Minercfat ModsMinercfat Mods
Minercfat Modsclockers69
 
2014학년도 성신여자대학교 입학사정관 전형 안내책자
2014학년도 성신여자대학교 입학사정관 전형 안내책자2014학년도 성신여자대학교 입학사정관 전형 안내책자
2014학년도 성신여자대학교 입학사정관 전형 안내책자admitsungshin
 
Innovacion Tecnologica Informatica
Innovacion Tecnologica InformaticaInnovacion Tecnologica Informatica
Innovacion Tecnologica InformaticaDaniel C. Cirigliano
 

Viewers also liked (13)

Gst on services in india
Gst on services in indiaGst on services in india
Gst on services in india
 
Phiếu theo dõi thực tập
Phiếu theo dõi thực tậpPhiếu theo dõi thực tập
Phiếu theo dõi thực tập
 
SKT SOCIAL MANAGER - KANG SE JIN
SKT SOCIAL MANAGER - KANG SE JINSKT SOCIAL MANAGER - KANG SE JIN
SKT SOCIAL MANAGER - KANG SE JIN
 
Vega
VegaVega
Vega
 
4life Power Point
4life Power Point4life Power Point
4life Power Point
 
4LIFE SPANISH PP NEW
4LIFE SPANISH PP NEW4LIFE SPANISH PP NEW
4LIFE SPANISH PP NEW
 
Minercfat Mods
Minercfat ModsMinercfat Mods
Minercfat Mods
 
Microsoft
MicrosoftMicrosoft
Microsoft
 
자문단설명회
자문단설명회자문단설명회
자문단설명회
 
2014학년도 성신여자대학교 입학사정관 전형 안내책자
2014학년도 성신여자대학교 입학사정관 전형 안내책자2014학년도 성신여자대학교 입학사정관 전형 안내책자
2014학년도 성신여자대학교 입학사정관 전형 안내책자
 
Tema1 concepto y clasificacion de la auditoria
Tema1 concepto y clasificacion de la auditoriaTema1 concepto y clasificacion de la auditoria
Tema1 concepto y clasificacion de la auditoria
 
Determinants
DeterminantsDeterminants
Determinants
 
Innovacion Tecnologica Informatica
Innovacion Tecnologica InformaticaInnovacion Tecnologica Informatica
Innovacion Tecnologica Informatica
 

Similar to Unit 2

CH005
CH005CH005
CH005JUC
 
obrien13e_chap005.ppt
obrien13e_chap005.pptobrien13e_chap005.ppt
obrien13e_chap005.pptPradeep513562
 
obrien13e_chap005.ppt
obrien13e_chap005.pptobrien13e_chap005.ppt
obrien13e_chap005.pptPradeep513562
 
chapter5-220725172250-dc425eb2.pdf
chapter5-220725172250-dc425eb2.pdfchapter5-220725172250-dc425eb2.pdf
chapter5-220725172250-dc425eb2.pdfMahmoudSOLIMAN380726
 
Chapter 5: Data Development
Chapter 5: Data Development Chapter 5: Data Development
Chapter 5: Data Development Ahmed Alorage
 
Building Data Warehouse in SQL Server
Building Data Warehouse in SQL ServerBuilding Data Warehouse in SQL Server
Building Data Warehouse in SQL ServerAntonios Chatzipavlis
 
Management information system database management
Management information system database managementManagement information system database management
Management information system database managementOnline
 
What is a Data Warehouse and How Do I Test It?
What is a Data Warehouse and How Do I Test It?What is a Data Warehouse and How Do I Test It?
What is a Data Warehouse and How Do I Test It?RTTS
 
Creating a Next-Generation Big Data Architecture
Creating a Next-Generation Big Data ArchitectureCreating a Next-Generation Big Data Architecture
Creating a Next-Generation Big Data ArchitecturePerficient, Inc.
 
Creatinganext generationbigdataarchitecture-141204150317-conversion-gate02
Creatinganext generationbigdataarchitecture-141204150317-conversion-gate02Creatinganext generationbigdataarchitecture-141204150317-conversion-gate02
Creatinganext generationbigdataarchitecture-141204150317-conversion-gate02email2jl
 
Chap05 Data Resource Management
Chap05 Data Resource ManagementChap05 Data Resource Management
Chap05 Data Resource ManagementAqib Syed
 
Data warehousing and machine learning primer
Data warehousing and machine learning primerData warehousing and machine learning primer
Data warehousing and machine learning primerTom Donoghue
 
Data Lakehouse, Data Mesh, and Data Fabric (r2)
Data Lakehouse, Data Mesh, and Data Fabric (r2)Data Lakehouse, Data Mesh, and Data Fabric (r2)
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
 

Similar to Unit 2 (20)

Chap005
Chap005Chap005
Chap005
 
Chap05 data resource mgt
Chap05 data resource mgtChap05 data resource mgt
Chap05 data resource mgt
 
Chapter 05
Chapter 05Chapter 05
Chapter 05
 
CH005
CH005CH005
CH005
 
obrien13e_chap005.ppt
obrien13e_chap005.pptobrien13e_chap005.ppt
obrien13e_chap005.ppt
 
obrien13e_chap005.ppt
obrien13e_chap005.pptobrien13e_chap005.ppt
obrien13e_chap005.ppt
 
chapter5-220725172250-dc425eb2.pdf
chapter5-220725172250-dc425eb2.pdfchapter5-220725172250-dc425eb2.pdf
chapter5-220725172250-dc425eb2.pdf
 
Chapter 5: Data Development
Chapter 5: Data Development Chapter 5: Data Development
Chapter 5: Data Development
 
Building Data Warehouse in SQL Server
Building Data Warehouse in SQL ServerBuilding Data Warehouse in SQL Server
Building Data Warehouse in SQL Server
 
Week 5
Week 5Week 5
Week 5
 
Week 5
Week 5Week 5
Week 5
 
Management information system database management
Management information system database managementManagement information system database management
Management information system database management
 
Case mis ch05
Case mis ch05Case mis ch05
Case mis ch05
 
Chap05.ppt
Chap05.pptChap05.ppt
Chap05.ppt
 
What is a Data Warehouse and How Do I Test It?
What is a Data Warehouse and How Do I Test It?What is a Data Warehouse and How Do I Test It?
What is a Data Warehouse and How Do I Test It?
 
Creating a Next-Generation Big Data Architecture
Creating a Next-Generation Big Data ArchitectureCreating a Next-Generation Big Data Architecture
Creating a Next-Generation Big Data Architecture
 
Creatinganext generationbigdataarchitecture-141204150317-conversion-gate02
Creatinganext generationbigdataarchitecture-141204150317-conversion-gate02Creatinganext generationbigdataarchitecture-141204150317-conversion-gate02
Creatinganext generationbigdataarchitecture-141204150317-conversion-gate02
 
Chap05 Data Resource Management
Chap05 Data Resource ManagementChap05 Data Resource Management
Chap05 Data Resource Management
 
Data warehousing and machine learning primer
Data warehousing and machine learning primerData warehousing and machine learning primer
Data warehousing and machine learning primer
 
Data Lakehouse, Data Mesh, and Data Fabric (r2)
Data Lakehouse, Data Mesh, and Data Fabric (r2)Data Lakehouse, Data Mesh, and Data Fabric (r2)
Data Lakehouse, Data Mesh, and Data Fabric (r2)
 

Unit 2

  • 1. McGraw-Hill/Irwin Copyright © 2008, The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin Copyright © 2008 by The McGraw-Hill Companies, Inc. All rights reserved.
  • 2. McGraw-Hill/Irwin Copyright © 2008, The McGraw-Hill Companies, Inc. All rights reserved. Data Resource ManagementData Resource Management Chapter 5 Chapter 5
  • 3. 5-3 • Explain the business value of implementing data resource management processes and technologies in an organization • Outline the advantages of a database management approach to managing the data resources of a business, compared to a file processing approach • Explain how database management software helps business professionals and supports the operations and management of a business Learning ObjectivesLearning Objectives
  • 4. 5-4 Learning ObjectivesLearning Objectives • Provide examples to illustrate the following concepts • Major types of databases • Data warehouses and data mining • Logical data elements • Fundamental database structures • Database development
  • 5. 5-5 Case 1: Sharing Business DatabasesCase 1: Sharing Business Databases • Amazon’s data vault • Product descriptions • Prices • Sales rankings • Customer reviews • Inventory figures • Countless other layers of content • Took 10 years and a billion dollars to build
  • 6. 5-6 Case 1: Sharing Business DatabasesCase 1: Sharing Business Databases • Amazon opened its data vault in 2002 • 65,000 developers, businesses, and entrepreneurs have tapped into it • Many have become ambitious business partners • eBay opened its $3 billion databases in 2003 • 15,000 developers and others have registered to use it and to access software features • 1,000 new applications have appeared • 41 percent of eBay’s listings are uploaded to the site using these resources
  • 7. 5-7 Case 1: Sharing Business DatabasesCase 1: Sharing Business Databases • Google recently unlocked access to its desktop and paid-search products • Dozens of Google-driven services cropped up • Developers can grab 1,000 search results a day for free; anything more requires permission • In 2005, the Ad-Words paid-search service was opened to outside applications
  • 8. 5-8 Case Study QuestionsCase Study Questions • What are the business benefits to Amazon and eBay of opening up some of their databases to developers and entrepreneurs? • Do you agree with this strategy? • What business factors are causing Google to move slowly in opening up its databases? • Do you agree with its go-slow strategy?
  • 9. 5-9 Case Study QuestionsCase Study Questions • Should other companies follow Amazon and eBay’s lead and open up some of their databases to developers and others? • Defend your position with an example of the risks and benefits to an actual company
  • 11. 5-11 Logical Data ElementsLogical Data Elements • Character • A single alphabetic, numeric, or other symbol • Field or data item • Represents an attribute (characteristic or quality) of some entity (object, person, place, event) • Examples: salary, job title • Record • Grouping of all the fields used to describe the attributes of an entity • Example: payroll record with name, SSN, pay rate
  • 12. 5-12 Logical Data ElementsLogical Data Elements • File or table • A group of related records • Database • An integrated collection of logically related data elements
  • 14. 5-14 Database StructuresDatabase Structures • Common database structures… • Hierarchical • Network • Relational • Object-oriented • Multi-dimensional
  • 15. 5-15 Hierarchical StructureHierarchical Structure • Early DBMS structure • Records arranged in tree-like structure • Relationships are one-to-many
  • 16. 5-16 Network StructureNetwork Structure • Used in some mainframe DBMS packages • Many-to-many relationships
  • 17. 5-17 Relational StructureRelational Structure • Most widely used structure • Data elements are stored in tables • Row represents a record; column is a field • Can relate data in one file with data in another, if both files share a common data element
  • 18. 5-18 Relational OperationsRelational Operations • Select • Create a subset of records that meet a stated criterion • Example: employees earning more than $30,000 • Join • Combine two or more tables temporarily • Looks like one big table • Project • Create a subset of columns in a table
  • 19. 5-19 Multidimensional StructureMultidimensional Structure • Variation of relational model • Uses multidimensional structures to organize data • Data elements are viewed as being in cubes • Popular for analytical databases that support Online Analytical Processing (OLAP)
  • 21. 5-21 Object-Oriented StructureObject-Oriented Structure • An object consists of • Data values describing the attributes of an entity • Operations that can be performed on the data • Encapsulation • Combine data and operations • Inheritance • New objects can be created by replicating some or all of the characteristics of parent objects
  • 22. 5-22 Object-Oriented StructureObject-Oriented Structure Source: Adapted from Ivar Jacobsen, Maria Ericsson, and Ageneta Jacobsen, The Object Advantage: Business Process Reengineering with Object Technology (New York: ACM Press, 1995), p. 65. Copyright @ 1995, Association for Computing Machinery. By permission.
  • 23. 5-23 Object-Oriented StructureObject-Oriented Structure • Used in object-oriented database management systems (OODBMS) • Supports complex data types more efficiently than relational databases • Examples: graphic images, video clips, web pages
  • 24. 5-24 Evaluation of Database StructuresEvaluation of Database Structures • Hierarchical • Works for structured, routine transactions • Can’t handle many-to-many relationship • Network • More flexible than hierarchical • Unable to handle ad hoc requests • Relational • Easily responds to ad hoc requests • Easier to work with and maintain • Not as efficient/quick as hierarchical or network
  • 25. 5-25 Database DevelopmentDatabase Development • Database Administrator (DBA) • In charge of enterprise database development • Improves the integrity and security of organizational databases • Uses Data Definition Language (DDL) to develop and specify data contents, relationships, and structure • Stores these specifications in a data dictionary or a metadata repository
  • 26. 5-26 Data DictionaryData Dictionary • A data dictionary • Contains data about data (metadata) • Relies on specialized software component to manage a database of data definitions • It contains information on.. • The names and descriptions of all types of data records and their interrelationships • Requirements for end users’ access and use of application programs • Database maintenance • Security
  • 28. 5-28 Data Planning ProcessData Planning Process • Database development is a top-down process • Develop an enterprise model that defines the basic business process of the enterprise • Define the information needs of end users in a business process • Identify the key data elements that are needed to perform specific business activities (entity relationship diagrams)
  • 30. 5-30 Database Design ProcessDatabase Design Process • Data relationships are represented in a data model that supports a business process • This model is the schema or subschema on which to base… • The physical design of the database • The development of application programs to support business processes
  • 31. 5-31 Database Design ProcessDatabase Design Process • Logical Design • Schema - overall logical view of relationships • Subschema - logical view for specific end users • Data models for DBMS • Physical Design • How data are to be physically stored and accessed on storage devices
  • 32. 5-32 Logical and Physical Database ViewsLogical and Physical Database Views
  • 34. 5-34 Operational DatabasesOperational Databases • Stores detailed data needed to support business processes and operations • Also called subject area databases (SADB), transaction databases, and production databases • Database examples: customer, human resource, inventory
  • 35. 5-35 Distributed DatabasesDistributed Databases • Distributed databases are copies or parts of databases stored on servers at multiple locations • Improves database performance at worksites • Advantages • Protection of valuable data • Data can be distributed into smaller databases • Each location has control of its local data • All locations can access any data, any where • Disadvantages • Maintaining data accuracy
  • 36. 5-36 Distributed DatabasesDistributed Databases • Replication • Look at each distributed database and find changes • Apply changes to each distributed database • Very complex • Duplication • One database is master • Duplicate the master after hours, in all locations • Easier to accomplish
  • 37. 5-37 External DatabasesExternal Databases • Databases available for a fee from commercial online services, or free from the Web • Examples: hypermedia databases, statistical databases, bibliographic and full text databases • Search engines like Google or Yahoo are external databases
  • 38. 5-38 Hypermedia DatabasesHypermedia Databases • A hypermedia database contains • Hyperlinked pages of multimedia • Interrelated hypermedia page elements, rather than interrelated data records
  • 39. 5-39 Components of Web-Based SystemComponents of Web-Based System
  • 40. 5-40 Data WarehousesData Warehouses • Stores static data that has been extracted from other databases in an organization • Central source of data that has been cleaned, transformed, and cataloged • Data is used for data mining, analytical processing, analysis, research, decision support • Data warehouses may be divided into data marts • Subsets of data that focus on specific aspects of a company (department or business process)
  • 41. 5-41 Data Warehouse ComponentsData Warehouse Components
  • 42. 5-42 Applications and Data MartsApplications and Data Marts
  • 43. 5-43 Data MiningData Mining • Data in data warehouses are analyzed to reveal hidden patterns and trends • Market-basket analysis to identify new product bundles • Find root cause of qualify or manufacturing problems • Prevent customer attrition • Acquire new customers • Cross-sell to existing customers • Profile customers with more accuracy
  • 44. 5-44 Traditional File ProcessingTraditional File Processing • Data are organized, stored, and processed in independent files • Each business application designed to use specialized data files containing specific types of data records • Problems • Data redundancy • Lack of data integration • Data dependence (files, storage devices, software) • Lack of data integrity or standardization
  • 46. 5-46 Database Management ApproachDatabase Management Approach • The foundation of modern methods of managing organizational data • Consolidates data records formerly in separate files into databases • Data can be accessed by many different application programs • A database management system (DBMS) is the software interface between users and databases
  • 48. 5-48 Database Management SystemDatabase Management System • In mainframe and server computer systems, a software package that is used to… • Create new databases and database applications • Maintain the quality of the data in an organization’s databases • Use the databases of an organization to provide the information needed by end users
  • 49. 5-49 Common DBMS Software ComponentsCommon DBMS Software Components • Database definition • Language and graphical tools to define entities, relationships, integrity constraints, and authorization rights • Nonprocedural access • Language and graphical tools to access data without complicated coding • Application development • Graphical tools to develop menus, data entry forms, and reports
  • 50. 5-50 Common DBMS Software ComponentsCommon DBMS Software Components • Procedural language interface • Language that combines nonprocedural access with full capabilities of a programming language • Transaction processing • Control mechanism prevents interference from simultaneous users and recovers lost data after a failure • Database tuning • Tools to monitor, improve database performance
  • 51. 5-51 Database Management SystemDatabase Management System • Database Development • Defining and organizing the content, relationships, and structure of the data needed to build a database • Database Application Development • Using DBMS to create prototypes of queries, forms, reports, Web pages • Database Maintenance • Using transaction processing systems and other tools to add, delete, update, and correct data
  • 52. 5-52 DBMS Major FunctionsDBMS Major Functions
  • 53. 5-53 Database InterrogationDatabase Interrogation • End users use a DBMS query feature or report generator • Response is video display or printed report • No programming is required • Query language • Immediate response to ad hoc data requests • Report generator • Quickly specify a format for information you want to present as a report
  • 54. 5-54 Database InterrogationDatabase Interrogation • SQL Queries • Structured, international standard query language found in many DBMS packages • Query form is SELECT…FROM…WHERE…
  • 55. 5-55 Database InterrogationDatabase Interrogation • Boolean Logic • Developed by George Boole in the mid-1800s • Used to refine searches to specific information • Has three logical operators: AND, OR, NOT • Example • Cats OR felines AND NOT dogs OR Broadway
  • 56. 5-56 Database InterrogationDatabase Interrogation • Graphical and Natural Queries • It is difficult to correctly phrase SQL and other database language search queries • Most DBMS packages offer easier-to-use, point-and-click methods • Translates queries into SQL commands • Natural language query statements are similar to conversational English
  • 58. 5-58 Database MaintenanceDatabase Maintenance • Accomplished by transaction processing systems and other applications, with the support of the DBMS • Done to reflect new business transactions and other events • Updating and correcting data, such as customer addresses
  • 59. 5-59 Application DevelopmentApplication Development • Use DBMS software development tools to develop custom application programs • Not necessary to develop detailed data-handling procedures using conventional programming languages • Can include data manipulation language (DML) statements that call on the DBMS to perform necessary data handling