7.1 © 2002 by Prentice Hall c h a p t e r 7 7 MANAGING DATA DATARESOURCES

7.2 © 2002 by Prentice Hall LEARNING OBJECTIVES COMPARE TRADITIONAL FILE ORGANIZATION & MANAGEMENT TECHNIQUESCOMPARE TRADITIONAL FILE ORGANIZATION & MANAGEMENT TECHNIQUES DESCRIBE HOW DATABASE MANAGEMENT SYSTEM ORGANIZES INFORMATIONDESCRIBE HOW DATABASE MANAGEMENT SYSTEM ORGANIZES INFORMATION* © 2002 by Prentice Hall

7.3 LEARNING OBJECTIVES IDENTIFY TYPES OF DATABASE, PRINCIPLES OF DATABASE DESIGNIDENTIFY TYPES OF DATABASE, PRINCIPLES OF DATABASE DESIGN DISCUSS DATABASE TRENDSDISCUSS DATABASE TRENDS* © 2002 by Prentice Hall

7.4 MANAGEMENT CHALLENGES TRADITIONAL DATA FILE ENVIRONMENTTRADITIONAL DATA FILE ENVIRONMENT DATABASE APPROACH TO DATA MANAGEMENTDATABASE APPROACH TO DATA MANAGEMENT CREATING DATABASE ENVIRONMENTCREATING DATABASE ENVIRONMENT DATABASE TRENDSDATABASE TRENDS*

7.5 © 2002 by Prentice Hall MANAGEMENT CHALLENGES 1. ORGANIZATIONAL OBSTACLES: Challenges existing power structure, requires organizational restructure 2. COST / BENEFIT CONSIDERATIONS: Large initial costs, delayed benefits, tangible, intangible *

7.6 © 2002 by Prentice Hall DATA REDUNDANCYDATA REDUNDANCY PROGRAM / DATA DEPENDENCYPROGRAM / DATA DEPENDENCY LACK OF FLEXIBILITYLACK OF FLEXIBILITY POOR SECURITYPOOR SECURITY LACK OF DATA SHARING & AVAILABILITYLACK OF DATA SHARING & AVAILABILITY* PROBLEMS WITH TRADITIONAL FILE ENVIRONMENT Flat File

7.7 © 2002 by Prentice Hall FILING METHODS INDEXED SEQUENTIAL ACCESS METHOD (ISAM) :INDEXED SEQUENTIAL ACCESS METHOD (ISAM) : –EACH RECORD IDENTIFIED BY KEY –GROUPED IN BLOCKS AND CYLINDERS –KEYS IN INDEX VIRTUAL STORAGE ACCESS METHOD (VSAM) :VIRTUAL STORAGE ACCESS METHOD (VSAM) : –MEMORY DIVIDED INTO AREAS & INTERVALS –DYNAMIC FILE SPACE VSAM WIDELY USED FOR RELATIONAL DATABASES VSAM WIDELY USED FOR RELATIONAL DATABASES DIRECT FILE ACCESS METHODDIRECT FILE ACCESS METHOD*

7.8 © 2002 by Prentice Hall DIRECT FILE ACCESS METHOD EACH RECORD HAS KEY FIELDEACH RECORD HAS KEY FIELD KEY FIELD FED INTO TRANSFORM ALGORITHMKEY FIELD FED INTO TRANSFORM ALGORITHM ALGORITHM GENERATES PHYSICAL STORAGE LOCATION OF RECORD (RECORD ADDRESS)ALGORITHM GENERATES PHYSICAL STORAGE LOCATION OF RECORD (RECORD ADDRESS)*

7.9 © 2002 by Prentice Hall DATABASE MANAGEMENT SYSTEM (DBMS) SOFTWARE TO CREATE & MAINTAIN DATA ENABLES BUSINESS APPLICATIONS TO EXTRACT DATA ENABLES BUSINESS APPLICATIONS TO EXTRACT DATA INDEPENDENT OF SPECIFIC COMPUTER PROGRAMS INDEPENDENT OF SPECIFIC COMPUTER PROGRAMS* DBMS

7.10 © 2002 by Prentice Hall COMPONENTS OF DBMS: DATA DEFINITION LANGUAGE:DATA DEFINITION LANGUAGE: –Defines data elements in database DATA MANIPULATION LANGUAGE:DATA MANIPULATION LANGUAGE: –Manipulates data for applications DATA DICTIONARY:DATA DICTIONARY: –Formal definitions of all variables in database, controls variety of database contents, data elements * DBMS

7.11 © 2002 by Prentice Hall STRUCTURED QUERY LANGUAGE (SQL) EMERGING STANDARD DATA MANIPULATION LANGUAGE FOR RELATIONAL DATABASES * DBMS

7.12 © 2002 by Prentice Hall ELEMENTS OF SQL SELECT: List of columns from tables desiredSELECT: List of columns from tables desired FROM: Identifies tables from which columns will be selectedFROM: Identifies tables from which columns will be selected WHERE: Includes conditions for selecting specific rows, conditions for joining multiple tablesWHERE: Includes conditions for selecting specific rows, conditions for joining multiple tables* DBMS

7.13 © 2002 by Prentice Hall TWO VIEWS OF DATA BIT BYTE FIELD RECORD FILE DATABASE PHYSICAL VIEW: Where is data physically?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?LOGICAL VIEW: What data is needed by application? –SUCCESSION OF FACTS NEEDED BY APPLICATION –NAME, TYPE, LENGTH OF FIELD * DBMS

7.14 © 2002 by Prentice Hall RELATIONAL DATA MODEL DATA IN TABLE FORMATDATA IN TABLE FORMAT RELATION: TABLERELATION: TABLE TUPLE: ROW (RECORD) IN TABLETUPLE: ROW (RECORD) IN TABLE FIELD: COLUMN (ATTRIBUTE) IN TABLEFIELD: COLUMN (ATTRIBUTE) IN TABLE*

7.15 © 2002 by Prentice Hall TYPES OR RELATIONS ONE-TO-ONE: STUDENT ID ONE-TO-MANY: CLASS STUDENT A STUDENT B STUDENT C MANY-TO-MANY: STUDENT A STUDENT B STUDENT C CLASS 1 CLASS 2

7.16 © 2002 by Prentice Hall ROOT FIRST CHILD 2nd CHILD RatingsSalary Compensation Job Assignments PensionInsuranceHealth Benefits Employer HIERARCHICAL DATA MODEL

7.17 © 2002 by Prentice Hall NETWORK DATA MODEL VARIATION OF HIERARCHICAL MODELVARIATION OF HIERARCHICAL MODEL USEFUL FOR MANY-TO-MANY RELATIONSHIPSUSEFUL FOR MANY-TO-MANY RELATIONSHIPS* NETWORK A NETWORK B NETWORK C NETWORK 1 NETWORK 2

7.18 © 2002 by Prentice Hall OTHER SYSTEMS LEGACY SYSTEM: older systemLEGACY SYSTEM: older system OBJECT - ORIENTED DBMS: stores data & procedures as objectsOBJECT - ORIENTED DBMS: stores data & procedures as objects OBJECT - RELATIONAL DBMS: hybridOBJECT - RELATIONAL DBMS: hybrid*

7.19 © 2002 by Prentice Hall CREATING A DATABASE CONCEPTUAL DESIGNCONCEPTUAL DESIGN PHYSICAL DESIGNPHYSICAL DESIGN*

7.20 © 2002 by Prentice Hall CREATING A DATABASE CONCEPTUAL DESIGN: ABSTRACT MODEL, BUSINESS PERSPECTIVEABSTRACT MODEL, BUSINESS PERSPECTIVE HOW WILL DATA BE GROUPED?HOW WILL DATA BE GROUPED? RELATIONSHIPS AMONG ELEMENTSRELATIONSHIPS AMONG ELEMENTS ESTABLISH END-USER NEEDSESTABLISH END-USER NEEDS*

7.21 © 2002 by Prentice Hall DETAILED MODEL BY DATABASE SPECIALISTSDETAILED MODEL BY DATABASE SPECIALISTS ENTITY-RELATIONSHIP DIAGRAMENTITY-RELATIONSHIP DIAGRAM NORMALIZATIONNORMALIZATION HARDWARE / SOFTWARE SPECIFICHARDWARE / SOFTWARE SPECIFIC* CREATING A DATABASE PHYSICAL DESIGN:

7.22 © 2002 by Prentice Hall ELEMENTS OF DATABASE ENVIRONMENT DATABASE MANAGEMENT SYSTEM DATA ADMINISTRATION DATABASE TECHNO LOGY & MANAGEMENT USERS DATA PLANNING & MODELING METHODOLOGY

7.23 © 2002 by Prentice Hall ENTITY- RELATIONSHIP DIAGRAM 1 1 M 1 ORDER CAN HAVE PART SUPPLIER CAN HAVE ORDER: #, DATE, PART #, QUANTITY PART: #, DESCRIPTION, UNIT PRICE, SUPPLIER # SUPPLIER: #, NAME, ADDRESS

7.24 © 2002 by Prentice Hall NORMALIZATION PROCESS OF CREATING SMALL DATA STRUCTURES FROM COMPLEX GROUPS OF DATA EXAMPLES: ACCOUNTS RECEIVABLEACCOUNTS RECEIVABLE PERSONNEL RECORDSPERSONNEL RECORDS PAYROLLPAYROLL*

7.25 © 2002 by Prentice Hall DISTRIBUTED DATABASES PARTITIONED: remote CPUs (connected to host) have files unique to that site, e.g., records on local customersPARTITIONED: 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 formsDUPLICATE: each remote CPU has copies of common files, e.g., layouts for standard reports and forms*

7.26 © 2002 by Prentice Hall DATABASE ADMINISTRATION DEFINES & ORGANIZES DATABASE STRUCTURE AND CONTENTDEFINES & ORGANIZES DATABASE STRUCTURE AND CONTENT DEVELOPS SECURITY PROCEDURESDEVELOPS SECURITY PROCEDURES DEVELOPS DATABASE DOCUMENTATIONDEVELOPS DATABASE DOCUMENTATION MAINTAINS DBMSMAINTAINS DBMS*

7.27 © 2002 by Prentice Hall DATABASE TRENDS MULTIDIMENSIONAL DATA ANALYSIS: 3D (or higher) groupings to store complex dataMULTIDIMENSIONAL DATA ANALYSIS: 3D (or higher) groupings to store complex data HYPERMEDIA: Nodes contain text, graphics, sound, video, programs. organizes data as nodes.HYPERMEDIA: Nodes contain text, graphics, sound, video, programs. organizes data as nodes.*

7.28 © 2002 by Prentice Hall DATABASE TRENDS DATA WAREHOUSE: Organization’s electronic library stores consolidated current & historic data for management reporting & analysisDATA WAREHOUSE: Organization’s electronic library stores consolidated current & historic data for management reporting & analysis ON-LINE ANALYTICAL PROCESSING (OLAP): Tools for multi- dimensional data analysisON-LINE ANALYTICAL PROCESSING (OLAP): Tools for multi- dimensional data analysis*

7.29 © 2002 by Prentice Hall COMPONENTS OF DATA WAREHOUSE

7.30 © 2002 by Prentice Hall DATABASE TRENDS DATA MART: Small data warehouse for special function, e.g., Focused marketing based on customer infoDATA MART: Small data warehouse for special function, e.g., Focused marketing based on customer info DATAMINING: Tools for finding hidden patterns, relation- ships, for predicting trendsDATAMINING: Tools for finding hidden patterns, relation- ships, for predicting trends*

7.31 © 2002 by Prentice Hall DATABASE TRENDS LINKING DATABASES TO THE WEB: WEB USER CONNECTS TO VENDOR DATABASEWEB USER CONNECTS TO VENDOR DATABASE SPECIAL SOFTWARE CONVERTS HTML TO SQLSPECIAL SOFTWARE CONVERTS HTML TO SQL SQL FINDS DATA, SERVER CONVERTS RESULT TO HTMLSQL FINDS DATA, SERVER CONVERTS RESULT TO HTML*

7.32 © 2002 by Prentice Hall c h a p t e r 7 7 MANAGING DATA DATARESOURCES