Transcript 8. MANAGING DATA RESOURCES

8.1
LEARNING OBJECTIVES
• COMPARE TRADITIONAL FILE
ORGANIZATION & MANAGEMENT
TECHNIQUES
• EXPLAIN PROBLEMS OF TRADITIONAL
FILE ENVIRONMENT
• DESCRIBE HOW DATABASE
MANAGEMENT SYSTEM
ORGANIZES DATA
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8.2
LEARNING OBJECTIVES
• IDENTIFY 3 DATABASE MODELS,
PRINCIPLES OF DATABASE DESIGN
• DISCUSS DATABASE TRENDS
• ANALYZE MANAGERIAL,
ORGANIZATIONAL REQUIREMENTS FOR
CREATING DATABASE ENVIRONMENT
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8.3
MANAGEMENT CHALLENGES
• TRADITIONAL DATA FILE
ENVIRONMENT
• DATABASE ENVIRONMENT
• DESIGNING DATABASES
• DATABASE TRENDS
• MANAGEMENT REQUIREMENTS
FOR DATABASE SYSTEMS
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8.4
FILE ORGANIZATION
• BIT: Binary Digit (0,1;Y,N;On, Off)
• BYTE: Combination of BITS which
represent a CHARACTER
• FIELD: Collection of BYTES which
represent a DATUM or Fact
• RECORD: Collection of FIELDS
which reflect a TRANSACTION
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8.5
FILE ORGANIZATION
• FILE: A Collection of Similar
RECORDS
• DATABASE: An Organization’s
Electronic Library of FILES
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8.5
FILE ORGANIZATION
• ENTITY: Person, Place, Thing, Event
about Which Data Must be Kept
• ATTRIBUTE: Description of a
Particular ENTITY
• KEY FIELD: Field Used to Retrieve,
Update, Sort RECORD
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8.7
KEY FIELD
Field in Each Record
Uniquely Identifies THIS Record
For RETRIEVAL
UPDATING
SORTING
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8.8
SEQUENTIAL VS. DIRECT
FILE ORGANIZATION
• SEQUENTIAL: Tape Oriented; One
File Follows another; Follows
Physical Sequence
• DIRECT: Disk Oriented; Can be
Accessed Without Regard to
Physical Sequence
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8.9
FILING METHODS
• INDEXED SEQUENTIAL ACCESS METHOD (ISAM) :
– EACH RECORD IDENTIFIED BY KEY
– GROUPED IN BLOCKS AND CYLINDERS
– KEYS IN INDEX
• VIRTUAL STORAGE ACCESS METHOD (VSAM) :
– MEMORY DIVIDED INTO AREAS & INTERVALS
– DYNAMIC FILE SPACE
VSAM WIDELY USED FOR RELATIONAL
DATABASES
• DIRECT FILE ACCESS METHOD
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8.10
DIRECT FILE ACCESS METHOD
• EACH RECORD HAS KEY FIELD
• KEY FIELD FED INTO TRANSFORM
ALGORITHM
• ALGORITHM GENERATES PHYSICAL
STORAGE LOCATION OF RECORD
(RECORD ADDRESS)
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8.11
TRADITIONAL FILE
ENVIRONMENT (FLAT FILE)
•
•
•
•
•
DATA REDUNDANCY
PROGRAM / DATA DEPENDENCY
LACK OF FLEXIBILITY
POOR SECURITY
LACK OF DATA
SHARING &
AVAILABILITY
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8.12
DATABASE
ORGANIZATION’S ELECTRONIC
LIBRARY
STORES & MANAGES DATA
IN A CONVENIENT FORM
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8.13
DATABASE MANAGEMENT
SYSTEM (DBMS)
SOFTWARE TO CREATE & MAINTAIN
DATA
ENABLES BUSINESS APPLICATIONS
TO EXTRACT DATA
INDEPENDENT OF SPECIFIC
COMPUTER PROGRAMS
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8.14
DBMS
COMPONENTS OF DBMS:
• DATA DEFINITION LANGUAGE:
– Defines Data Elements in Database
• DATA MANIPULATION LANGUAGE:
– Manipulates Data for Applications
• DATA DICTIONARY:
– Formal Definitions of all Variables in
Database; Controls Variety of Database
Contents
8.15
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DBMS
STRUCTURED QUERY
LANGUAGE (SQL)
EMERGING STANDARD
DATA MANIPULATION LANGUAGE
FOR RELATIONAL DATABASES
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8.16
DBMS
TWO VIEWS OF DATA
• PHYSICAL VIEW: WHERE IS DATA PHYSICALLY?
– DRIVE, DISK, SURFACE, TRACK, SECTOR
(BLOCK), RECORD
– TAPE, BLOCK, RECORD NUMBER (KEY)
• LOGICAL VIEW: WHAT DATA IS NEEDED BY
APPLICATION?
– SUCCESSION OF FACTS NEEDED BY
APPLICATION
– NAME, TYPE, LENGTH OF FIELD
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8.17
DBMS
ADVANTAGES OF DBMS:
• REDUCES COMPLEXITY
• REDUCES DATA REDUNDANCY /
INCONSISTENCY
• CENTRAL CONTROL OF DATA
CREATION / DEFINITIONS
• REDUCES PROGRAM / DATA
DEPENDENCE
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8.18
DBMS
ADVANTAGES OF DBMS:
• REDUCES DEVELOPMENT /
MAINTENANCE COSTS
• ENHANCES SYSTEM FLEXIBILITY
• INCREASES ACCESS / AVAILABILITY
OF INFORMATION
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8.19
DBMS
HIERARCHICAL DATA MODEL
ROOT
FIRST
CHILD
2nd
CHILD
8.20
Employer
Compensation
Ratings
Salary
Job
Assignments
Pension
Benefits
Insurance
Health
POINTER
• FIELD IN ONE RECORD IS ADDRESS
OF NEXT RECORD IN SEQUENCE
*
POINTER
RECORD 1
RECORD 2
RECORD 3
8.21
POINTER
POINTER
TYPES OR RELATIONS
ONE-TO-ONE:
STUDENT
CLASS
ONE-TO-MANY:
STUDENT
A
MANY-TO-MANY:
8.22
ID
STUDENT
B
CLASS
1
STUDENT
A
STUDENT
C
CLASS
2
STUDENT
B
STUDENT
C
NETWORK DATA MODEL
• VARIATION OF HIERARCHICAL
MODEL
• USEFUL FOR MANY-TO-MANY
RELATIONSHIPS
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NETWORK
1
8.23
NETWORK
A
NETWORK
2
NETWORK
B
NETWORK
C
RELATIONAL DATA MODEL
•
•
•
•
8.24
DATA IN TABLE FORMAT
RELATION: TABLE
TUPLE: ROW (RECORD) IN TABLE
FIELD: COLUMN (ATTRIBUTE) IN TABLE
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HOURS
RATE
TOTAL
ABLE
$ 40.50 $ 10.35 $ 419.18
BAXTER $ 38.00 $ 8.75 $ 332.50
CHEN
$ 42.70 $ 9.25 $ 394.98
DENVER $ 35.90 $ 9.50 $ 341.05
COMPARISON OF DATABASE
ALTERNATIVES
HIERARCHICAL:
PROCESSING EFFICIENCY: HIGH
FLEXIBILITY: LOW
USER FRIENDLY: LOW
PROGRAM COMPLEXITY: HIGH
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8.25
COMPARISON OF DATABASE
ALTERNATIVES
NETWORK:
PROCESSING EFFICIENCY: MEDIUM /
HIGH
FLEXIBILITY: LOW / MEDIUM
USER FRIENDLY: LOW / MODERATE
PROGRAM COMPLEXITY: HIGH
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8.26
COMPARISON OF DATABASE
ALTERNATIVES
RELATIONAL:
PROCESSING EFFICIENCY:
LOW
BUT IMPROVING
FLEXIBILITY: HIGH
USER FRIENDLY: HIGH
PROGRAM COMPLEXITY: LOW
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8.27
CREATING A DATABASE
• CONCEPTUAL DESIGN
• PHYSICAL DESIGN
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8.28
CREATING A DATABASE
CONCEPTUAL DESIGN:
• ABSTRACT MODEL, BUSINESS
PERSPECTIVE
• HOW WILL DATA BE GROUPED?
• RELATIONSHIPS AMONG
ELEMENTS
• ESTABLISH END-USER
NEEDS
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8.29
CREATING A DATABASE
PHYSICAL DESIGN:
• DETAILED MODEL BY DATABASE
SPECIALISTS
• ENTITY-RELATIONSHIP DIAGRAM
• NORMALIZATION
• HARDWARE /
SOFTWARE
SPECIFIC
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8.30
ENTITY- RELATIONSHIP
DIAGRAM
ORDER
ORDER: #, DATE, PART #, QUANTITY
1
CAN
HAVE
1
PART: #, DESCRIPTION, UNIT PRICE,
SUPPLIER #
PART
M
CAN
HAVE
1
8.31
SUPPLIER
SUPPLIER: #, NAME,
ADDRESS
NORMALIZATION
PROCESS OF CREATING SMALL DATA
STRUCTURES FROM COMPLEX
GROUPS OF DATA
EXAMPLES:
• ACCOUNTS RECEIVABLE
• PERSONNEL RECORDS
• PAYROLL
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8.32
DATABASE TRENDS
• DISTRIBUTED PROCESSING:
Multiple Geographical / Functional
Systems Connected with Network
• DISTRIBUTED DATABASE: Data
Physically Stored in more than one
Location
– PARTITIONED
– DUPLICATE
8.33
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DISTRIBUTED DATABASES
• PARTITIONED: remote CPUs
(connected to host) have files unique
to that site, e.g., records on local
customers
• DUPLICATE: each remote CPU has
copies of common files,
e.g., layouts for standard
reports and forms
8.34
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DATABASE TRENDS
• OBJECT- ORIENTED: Data and
Procedures Stored Together; can be
Retrieved, Shared
• HYPERMEDIA: Nodes Contain Text,
Graphics, Sound, Video, Programs.
Organizes Data as Nodes.
• MULTIDIMENSIONAL: 3D
(or higher) Groupings to
Store Complex Data
8.35
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DATABASE TRENDS
• DATA WAREHOUSE: Organization’s
Electronic Library Stores
Consolidated Current & Historic Data
for Management Reporting &
Analysis
• DATA MART: small data warehouse
for special function, e.g.,
focused marketing based
on customer info
8.36
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COMPONENTS OF DATA WAREHOUSE
OPERATIONAL,
HISTORICAL DATA
INTERNAL
DATA
SOURCES
DATA WAREHOUSE
DATA
ACCESS &
ANALYSIS
EXTRACT,
TRANSFORM
INFORMATION
DIRECTORY
EXTERNAL
DATA
SOURCES
8.37
QUERIES &
REPORTS
OLAP
DATA MINING
DATABASE TRENDS
• ON-LINE ANALYTICAL PROCESSING
(OLAP): ability to manipulate,
analyze large volumes of data from
multiple perspectives
• LINKING DATABASES TO THE WEB
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8.38
ELEMENTS OF DATABASE
ENVIRONMENT
DATA
DATABASE
TECHNO
LOGY &
MANAGEMENT
ADMINISTRATION
DATABASE
MANAGEMENT
SYSTEM
DATA PLANNING
& MODELING
METHODOLOGY
8.39
USERS
DATABASE
ADMINISTRATION
• DEFINES & ORGANIZES DATABASE
STRUCTURE AND CONTENT
• DEVELOPS SECURITY PROCEDURES
• DEVELOPS DATABASE DOCUMENTATION
• MAINTAINS DBMS
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8.40
Connect to the INTERNET
PRESS LEFT MOUSE BUTTON ON ICON TO
CONNECT TO THE LAUDON & LAUDON
WEB SITE FOR MORE INFORMATION ON
THIS CHAPTER
8.41
8.42