Decision support and business intelligence systems:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Upper Saddle River, NJ
Pearson Prentice Hall
2007
|
| Ausgabe: | 8. ed., internat. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Bis 7. Aufl. u.d.T.: Decision support systems and intelligent systems |
| Beschreibung: | XXVIII, 772 S. graph. Darst. |
| ISBN: | 9780131580176 |
Internformat
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Datensatz im Suchindex
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| adam_text | * , * * ONTENTS PREFACE XXIII PART 1 DECISION SUPPORT AND BUSINESS
INTELLIGENCE 1 CHAPTER 1 DECISION SUPPORT SYSTEMS AND BUSINESS
INTELLIGENCE 3 1.1 OPENING VIGNETTE: TOYOTA USES BUSINESS INTELLIGENCE
TO EXCEL 4 1.2 CHANGING BUSINESS ENVIRONMENTS AND COMPUTERIZED DECISION
SUPPORT 6 1.3 MANAGERIAL DECISION MAKING 9 1.4 COMPUTERIZED SUPPORT FOR
DECISION MAKING 11 1.5 AN EARLY FRAMEWORK FOR COMPUTERIZED DECISION
SUPPORT 14 INTELLIGENT PRICE SETTING USING AN ADS 17 DECISION SUPPORT AT
HALLMARK FOR BETTER STRATEGY AND PERFORMANCE 19 1.6 THE CONCEPT OF
DECISION SUPPORT SYSTEMS 20 THE HOUSTON MINERALS CASE 21 HELPING
ATLANTIC ELECTRIC SURVIVE IN THE DEREGULATED MARKETPLACE 23 1.7 A
FRAMEWORK FOR BUSINESS INTELLIGENCE 24 PREDICTIVE ANALYTICS HELPS
COLLECT TAXES 29 1.8 A WORK SYSTEM VIEW OF DECISION SUPPORT 30 1.9 THE
MAJOR TOOLS AND TECHNIQUES OF MANAGERIAL DECISION SUPPORT 31 UNITED
SUGARS CORPORATION OPTIMIZES PRODUCTION, DISTRIBUTION, AND INVENTORY
CAPACITY WITH DIFFERENT DECISION SUPPORT TOOLS 33 1.10 IMPLEMENTING
COMPUTER-BASED MANAGERIAL DECISION SUPPORT SYSTEMS 34 1.11 PLAN OF THE
BOOK 35 1.12 RESOURCES, LINKS, AND THE TERADATA UNIVERSITY NETWORK
CONNECTION 37 END OF CHAPTER APPLICATION CASE DECISION SUPPORT AT A
DIGITAL HOSPITAL 41 REFERENCES 42 PART II COMPUTERIZED DECISION SUPPORT
43 CHAPTER 2 DECISION MAKING, SYSTEMS, MODELING, AND SUPPORT 45 2.1
OPENING VIGNETTE: DECISION MAKING AT THE U.S. FEDERAL RESERVE 46 2.2
DECISION MAKING: INTRODUCTORY AND DEFINITIONS 47 2.3 MODELS 51 2.4
PHASES OF THE DECISION-MAKING PROCESS 53 2.5 DECISION MAKING: THE
INTELLIGENCE PHASE 55 2.6 DECISION MAKING: THE DESIGN PHASE 57 IX
CONTENTS DECISION MAKING BETWEEN A ROCK AND A HARD PLACE; OR WHAT CAN
YOU DO WHEN THERE ARE NO GOOD OR EVEN FEASIBLE ALTERNATIVES? 60 DECISION
MAKING FROM THE GUT: WHEN INTUITION CAN FAIL 64 TOO MANY ALTERNATIVES
SPOILS THE BROTH 66 2.7 DECISION MAKING: THE CHOICE PHASE 68 2.8
DECISION MAKING: THE IMPLEMENTATION PHASE 69 2.9 HOW DECISIONS ARE
SUPPORTED 70 UNION PACIFIC RAILROAD: IF YOU RE COLLECTING DATA, USE IT
PROFITABLY! 72 ADVANCED TECHNOLOGY FOR MUSEUMS: RFID MAKES ART COME AUVE
75 2.10 RESOURCES, LINKS, AND THE TERADATA UNIVERSITY NETWORK CONNECTION
76 END OF CHAPTER APPLICATION CASE STRATEGIC DECISION MAKING IN THE
PHARMACEUTICAL INDUSTRY: HOW BAYER DECIDES WHETHER OR NOT TO DEVELOP A
NEW DRUG 80 REFERENCES 81 CHAPTER 3 DECISION SUPPORT SYSTEMS CONCEPTS,
METHODOLOGIES, AND TECHNOLOGIES: AN OVERVIEW 84 3.1 OPENING VIGNETTE:
DECISION SUPPORT SYSTEM CURES FOR HEALTH CARE 85 3.2 DECISION SUPPORT
SYSTEMS CONFIGURATIONS 87 3.3 DECISION SUPPORT SYSTEMS DESCRIPTION 88
WEB/GIS-BASED DSSAID IN DISASTER RELIEF AND IDENTIFYING FOOD STAMP FRAUD
89 3.4 DECISION SUPPORT SYSTEMS CHARACTERISTICS AND CAPABILITIES 90 3.5
COMPONENTS OF DSS 92 3.6 THE DATA MANAGEMENT SUBSYSTEM 97 ROADWAY DRIVES
LEGACY APPLICATIONS ONTO THE WEB 98 3.7 THE MODEL MANAGEMENT SUBSYSTEM
104 WEB-BASED CLUSTER ANALYSIS DSS MATCHES UP MOVIES AND CUSTOMERS. 107
3.8 THE USER INTERFACE (DIALOG) SUBSYSTEM 109 CLARISSA: A HANDS-FREE
HELPER FOR ASTRONAUTS 111 3.9 THE KNOWLEDGE-BASED MANAGEMENT SUBSYSTEM
115 IAP SYSTEMS S INTELLIGENT DSS DETERMINES THE SUCCESS OF OVERSEAS
ASSIGNMENTS AND LEARNS FROM THE EXPERIENCE 116 3.10 THE DECISION SUPPORT
SYSTEMS USER 116 3.11 DECISION SUPPORT SYSTEMS HARDWARE 117 3.12
DECISION SUPPORT SYSTEMS CLASSIFICATION 118 CONTENTS XI #*
DATABASE-ORIENTED DSS: GLAXO WELLCOME ACCESSES LIFE-SAVING DATA 119 3.13
RESOURCES, LINKS, AND THE TERADATA UNIVERSITY NETWORK CONNECTION 124 END
OF CHAPTER APPLICATION CASE FEDEX TRACKS CUSTOMERS ALONG WITH PACKAGES
127 REFERENCES 129 CHAPTER 4 MODELING AND ANALYSIS 131 4.1 OPENING
VIGNETTE: WINNING ISN T EVERYTHING... BUT LOSING ISN T ANYTHING:
PROFESSIONAL SPORTS MODELING FOR DECISION MAKING 132 4.2 MANAGEMENT
SUPPORT SYSTEMS MODELING 135 UNITED AIRLINES MODEL-BASED DSS FLIES THE
FRIENDLY SKIES 137 FORECASTING/PREDICTIVE ANALYTICS BOOSTS SALES FOR COX
COMMUNICATIONS 139 4.3 STATIC AND DYNAMIC MODELS 142 4.4 CERTAINTY,
UNCERTAINTY, AND RISK 143 4.5 MANAGEMENT SUPPORT SYSTEMS MODELING WITH
SPREADSHEETS 145 4.6 DECISION ANALYSIS WITH DECISION TABLES AND DECISION
TREES 147 JOHNSON & JOHNSON DECIDES ABOUT NEW PHARMACEUTICALS BY USING
TREES 150 4.7 TH E STRUCTURE OF MATHEMATICAL MODELS FOR DECISION SUPPORT
151 4.8 MATHEMATICAL PROGRAMMING OPTIMIZATION 153 COMPLEX TEACHER
SELECTION IS A BREEZE IN FLANDERS 154 4.9 MULTIPLE GOALS, SENSITIVITY
ANALYSIS, WHAT-IF ANALYSIS, AND GOAL SEEKING 158 4.10 PROBLEM-SOLVING
SEARCH METHODS 162 HEURISTIC-BASED DSS MOVES MILK IN NEW ZEALAND 164
4.11 SIMULATION 165 PRATT & WHITNEY CANADA GETS REAL SAVINGS THROUGH
VIRTUAL MANUFACTURING 165 SIMULATION APPLICATIONS 170 4.12 VISUAL
INTERACTIVE SIMULATION 171 4.13 QUANTITATIVE SOFTWARE PACKAGES AND MODEL
BASE MANAGEMENT 173 4.14 RESOURCES, LINKS, AND THE TERADATA UNIVERSITY
NETWORK CONNECTION 174 END OF CHAPTER APPLICATION CASE MAJOR LEAGUE
BASEBALL SCHEDULING: COMPUTERIZED MATHEMATICAL MODELS TAKE US OUT TO THE
BALLGAME 180 REFERENCES 181 * XII CONTENTS PART III BUSINESS
INTELLIGENCE 185 CHAPTERS SPECIAL INTRODUCTORY SECTION: THE ESSENTIALS
OF BUSINESS INTELLIGENCE 187 5.1 A PREVIEW OF THE CONTENT OF CHAPTERS 5
THROUGH 9 187 5.2 THE ORIGINS AND DRIVERS OF BUSINESS INTELLIGENCE (BI)
188 5.3 THE GENERAL PROCESS OF INTELLIGENCE CREATION AND USE 189 5.4 THE
MAJOR CHARACTERISTICS OF BUSINESS INTELLIGENCE 192 5.5 TOWARD
COMPETITIVE INTELLIGENCE AND ADVANTAGE 195 5.6 THE TYPICAL DATA
WAREHOUSE AND BUSINESS INTELLIGENCE USER COMMUNITY 197 5.7 SUCCESSFUL
BUSINESS INTELLIGENCE IMPLEMENTATION 198 FRANCE TELECOM BUSINESS
INTELLIGENCE 199 5.8 STRUCTURE AND COMPONENTS OF BUSINESS INTELLIGENCE
201 5.9 CONCLUSION: BUSINESS INTELLIGENCE TODAY AND TOMORROW 203 S.10
RESOURCES, LINKS AND THE TERADATA UNIVERSITY NETWORK CONNECTION 203
REFERENCES 205 CHAPTER 5 DATA WAREHOUSING 206 5.1 OPENING VIGNETTE:
CONTINENTAL AIRLINES FLIES HIGH WITH ITS REAL-TIME DATA WAREHOUSE 206
5.2 DATA WAREHOUSING DEFINITIONS AND CONCEPTS 209 5.3 DATA WAREHOUSING
PROCESS OVERVIEW 212 DATA WAREHOUSING SUPPORTS FIRST AMERICAN
CORPORATION S CORPORATE STRATEGY 212 5.4 DATA WAREHOUSING ARCHITECTURES
214 5.5 DATA INTEGRATION AND THE EXTRACTION, TRANSFORMATION, AND LOAD
(ETL) PROCESSES 222 BANK OF AMERICA S AWARD-WINNING INTEGRATED DATA
WAREHOUSE 223 5.6 DATA WAREHOUSE DEVELOPMENT 226 THINGS GO BETTER WITH
COKE S DATA WAREHOUSE 227 HP CONSOLIDATES HUNDREDS OF DATA MARTS INTO A
SINGLE EDW 231 5.7 REAL-TIME DATA WAREHOUSING 238 EGG PIC FRIES THE
COMPETITION IN NEAR-REAL- TIME 239 5.8 DATA WAREHOUSE ADMINISTRATION AND
SECURITY ISSUES 243 5.9 RESOURCES, LINKS, AND THE TERADATA UNIVERSITY
NETWORK CONNECTION 244 END OF CHAPTER APPLICATION CASE REAL-TIME DATA
WAREHOUSING AT OVERSTOCK.COM 249 REFERENCES 250 CONTENTS XIII CHAPTER 6
BUSINESS ANALYTICS AND DATA VISUALIZATION 253 6.1 OPENING VIGNETTE:
LEXMARK INTERNATIONAL IMPROVES OPERATIONS WITH BUSINESS INTELLIGENCE 254
6.2 THE BUSINESS ANALYTICS (BA) FIELD: AN OVERVIEW 256 BEN & JERRY S
EXCELS WITH BA 257 6.3 ONLINE ANALYTICAL PROCESSING (OLAP) 261
TCFFINANCIAL CORP.: CONDUCTING OLAP, REPORTING, AND DATA MINING 266 6.4
REPORTS AND QUERIES 266 6.5 MULTIDIMENSIONALITY 269 6.6 ADVANCED
BUSINESS ANALYTICS 273 PREDICTIVE ANALYSIS CAN HELP YOU AVOID TRAFFIC
JAMS 274 6.7 DATA VISUALIZATION 276 FINANCIAL DATA VISUALIZATION AT
MERRILL LYNCH 279 6.8 GEOGRAPHIC INFORMATION SYSTEMS (GIS) 280 GIS AND
GPS TRACK WHERE YOU ARE AND HELP YOU WITH WHAT YOU DO 282 6.9 REAL-TIME
BUSINESS INTELLIGENCE AUTOMATED DECISION SUPPORT (ADS), AND COMPETITIVE
INTELLIGENCE 284 6.10 BUSINESS ANALYTICS AND THE WEB: WEB INTELLIGENCE
AND WEB ANALYTICS 289 WEB ANALYTICS IMPROVES PERFORMANCE FOR ONLINE
MERCHANTS 291 6.11 USAGE, BENEFITS, AND SUCCESS OF BUSINESS ANALYTICS
292 RETAILERS MAKE STEADY BI PROGRESS 294 END OF CHAPTER APPLICATION
CASE STATE GOVERNMENTS SHARE GEOSPATIAL INFORMATION 298 REFERENCES 299
CHAPTER 7 DATA, TEXT, AND WEB MINING 302 7.1 OPENING VIGNETTE: HIGHMARK,
INC., EMPLOYS DATA MINING TO MANAGE INSURANCE COSTS 302 7.2 DATA MINING
CONCEPTS AND APPLICATIONS 304 DATA HELP FORETELL CUSTOMER NEEDS 306
MOTOR VEHICLE ACCIDENTS AND DRIVER DISTRACTIONS 309 DATA MINING TO
IDENTIFY CUSTOMER BEHAVIOR 310 CUSTOMIZING MEDICINE 311 A MINE ON
TERRORIST FUNDING 312 7.3 DATA MINING TECHNIQUES AND TOOLS 313 XIV
CONTENTS 7.4 DATA MINING PROJECT PROCESSES 325 DHS DATA MINING SPINOFFS
AND ADVANCES IN LAW ENFORCEMENT 328 7.5 TEXT MINING 329 FLYING THROUGH
TEXT 330 7.6 WEB MINING 333 CAUGHT IN A WEB 334 END OF CHAPTER
APPLICATION CASE HEWLETT-PACKARD AND TEXT MINING 340 REFERENCES 341
CHAPTER 8 NEURAL NETWORKS FOR DATA MINING 343 8.1 OPENING VIGNETTE:
USING NEURAL NETWORKS TO PREDICT BEER FLAVORS WITH CHEMICAL ANALYSIS 343
8.2 BASIC CONCEPTS OF NEURAL NETWORKS 346 NEURAL NETWORKS HELP REDUCE
TELECOMMUNICATIONS FRAUD 349 8.3 LEARNING IN ARTIFICIAL NEURAL NETWORKS
(ANN) 355 NEURAL NETWORKS HELP DELIVER MICROSOFT S MAIL TO THE INTENDED
AUDIENCE 356 8.4 DEVELOPING NEURAL NETWORK-BASED SYSTEMS 362 8.5 A
SAMPLE NEURAL NETWORK PROJECT 367 8.6 OTHER NEURAL NETWORK PARADIGMS 370
8.7 APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS 372 NEURAL NETWORKS FOR
BREAST CANCER DIAGNOSIS 373 8.8 A NEURAL NETWORK SOFTWARE DEMONSTRATION
374 END OF CHAPTER APPLICATION CASE SOVEREIGN CREDIT RATINGS USING
NEURAL NETWORKS 380 REFERENCES 381 CHAPTER 9 BUSINESS PERFORMANCE
MANAGEMENT 383 9.1 OPENING VIGNETTE: CISCO AND THE VIRTUAL CLOSE 384 9.2
BUSINESS PERFORMANCE MANAGEMENT (BPM) OVERVIEW 386 9.3 STRATEGIZE: WHERE
DO WE WANT TO GO? 388 9.4 PLAN: HOW DO WE GET THERE? 390 9.5 MONITOR:
HOW ARE WE DOING? 392 DISCOVERY-DRIVEN PLANNING: THE CASE OF EURO DISNEY
394 9.6 ACT AND ADJUST: WHAT DO WE NEED TO DO DIFFERENTLY 395 9.7
PERFORMANCE MEASUREMENT 39 8 INTERNATIONAL TRUCK AND ENGINE CORPORATION
400 9.8 BUSINESS PERFORMANCE MANAGEMENT METHODOLOGIES 402 9.9 BUSINESS
PERFORMANCE MANAGEMENT ARCHITECTURE AND APPLICATIONS 409 CONTENTS XV #
9.10 PERFORMANCE DASHBOARDS 417 DASHBOARDS FOR DOCTORS 419 9.11 BUSINESS
ACTIVITY MONITORING (BAM) 421 CITY OF ALBUQUERQUE GOES REAL-TIME 422 END
OF CHAPTER APPLICATION CASE VIGILANT INFORMATION SYSTEMS AT WESTERN
DIGITAL 428 REFERENCES 429 PART IV COLLABORATION, COMMUNICATION. GROUP
SUPPORT SYSTEMS, AND KNOWLEDGE MANAGEMENT 431 CHAPTER 10 COLLABORATIVE
COMPUTER-SUPPORTED TECHNOLOGIES AND GROUP SUPPORT SYSTEMS 433 10.1
OPENING VIGNETTE: COLLABORATIVE DESIGN AT BOEING-ROCKETDYNE 434 10.2
MAKING DECISIONS IN GROUPS: CHARACTERISTICS, PROCESS, BENEFITS, AND
DYSFUNCTIONS 436 10.3 SUPPORTING GROUPWORK WITH COMPUTERIZED SYSTEMS 439
HOW GENERAL MOTORS IS COLLABORATING ONLINE 440 10.4 TOOLS FOR INDIRECT
SUPPORT OF DECISION MAKING 443 VIDEOCONFERENCING IS READY FOR PRIME TIME
446 10.5 INTEGRATED GROUPWARE SUITES 448 NETMEETING PROVIDES A REAL-TIME
ADVANTAGE 449 10.6 DIRECT COMPUTERIZED SUPPORT FOR DECISION MAKING: FROM
GROUP DECISION SUPPORT SYSTEMS (GDSS) TO GROUP SUPPORT SYSTEMS (GSS) 452
EASTMAN CHEMICAL BOOSTS CREATIVE PROCESSES AND SAVES $500,000 WITH
GROUPWARE 456 10.7 PRODUCTS AND TOOLS FOR GDSS/GSS AND SUCCESSFUL
IMPLEMENTATION 458 10.8 EMERGING COLLABORATION TOOLS: FROM VOIP TO WIKIS
462 10.9 COLLABORATIVE IN PLANNING, DESIGN, AND PROJECT MANAGEMENT 465
CPFR INITIATIVES AT ACE HARDWARE AND SEARS 468 10.10 CREATIVITY, IDEA
GENERATION, AND COMPUTERIZED SUPPORT 469 END OF CHAPTER APPLICATION CASE
DRESDNER KLEINWORT WASSERSTEIN USES WIKIFOR COLLABORATION 475 REFERENCES
476 CHAPTER 11 KNOWLEDGE MANAGEMENT 478 11.1 OPENING VIGNETTE: SIEMENS
KNOWS WHAT IT KNOWS THROUGH KNOWLEDGE MANAGEMENT 479 11.2 INTRODUCTION
TO KNOWLEDGE MANAGEMENT 481 CINGULAR CALLS ON KNOWLEDGE 485 11.3
ORGANIZATIONAL LEARNING AND TRANSFORMATION 486 XVI CONTENT S 11.4
KNOWLEDGE MANAGEMENT ACTIVITIES 488 11.5 APPROACHES TO KNOWLEDGE
MANAGEMENT 490 TEXACO DRILLS FOR KNOWLEDGE 492 11.6 INFORMATION
TECHNOLOGY (IT) IN KNOWLEDGE MANAGEMENT 495 11.7 KNOWLEDGE MANAGEMENT
SYSTEM (KMS) IMPLEMENTATION 500 PORTAL OPENS THE DOOR TO LEGAL KNOWLEDGE
502 KNOWLEDGE MANAGEMENT: YOU CAN BANK ON IT AT COMMERCE BANK 504 11.8
ROLES OF PEOPLE IN KNOWLEDGE MANAGEMENT 507 ONLINE KNOWLEDGE SHARING AT
XEROX 510 11.9 ENSURING THE SUCCESS OF KNOWLEDGE MANAGEMENT EFFORTS 513
THE BRITISH BROADCASTING CORPORATION KNOWLEDGE MANAGEMENT SUCCESS 514
HOW THE U.S. DEPARTMENT OF COMMERCE USES AN EXPERT LOCATION SYSTEM 515
WHEN KMS FAIL, THEY CAN FAIL IN A BIG WAY 518 END OF CHAPTER APPLICATION
CASE DAIMLER CHRYSLER EBOKS WITH KNOWLEDGE MANAGEMENT 524 REFERENCES 526
PART V INTELLIGENT SYSTEMS 531 CHAPTER 12 ARTIFICIAL INTELLIGENCE AND
EXPERT SYSTEMS 533 12.1 OPENING VIGNETTE: CIGNA USES BUSINESS RULES TO
SUPPORT TREATMENT REQUEST APPROVAL 534 12.2 CONCEPTS AND DEFINITIONS OF
ARTIFICIAL INTELLIGENCE 53 5 INTELLIGENT SYSTEMS BEAT CHESS GRAND MASTER
535 12.3 THE ARTIFICIAL INTELLIGENCE FIELD 537 AUTOMATIC SPEECH
RECOGNITION IN CALL CENTERS 542 AGENTS FOR TRAVEL PLANNING AT USC 544
12.4 BASIC CONCEPTS OF EXPERT SYSTEMS (ES) 545 12.5 APPLICATIONS OF
EXPERT SYSTEMS 549 SAMPLE APPLICATIONS OF EXPERT SYSTEMS 549 12.6
STRUCTURE OF EXPERT SYSTEMS 552 12.7 HOW EXPERT SYSTEMS WORK: INFERENCE
MECHANISMS 555 12.8 PROBLEM AREAS SUITABLE FOR EXPERT SYSTEMS 558 12.9
DEVELOPMENT OF EXPERT SYSTEMS 560 12.10 BENEFITS, LIMITATIONS, AND
SUCCESS FACTORS OF EXPERT SYSTEMS 563 12.11 EXPERT SYSTEMS ON THE WEB
566 BANNER WITH BRAINS: WEB-BASED ES FOR RESTAURANT SELECTION 567
RULE-BASED SYSTEM FOR ONLINE STUDENT CONSULATION 567 CONTENTS XVII END
OF CHAPTER APPLICATION CASE BUSINESS RULE AUTOMATION AT FARM BUREAU
FINANCIAL SERVICES 572 REFERENCES 573 CHAPTER 13 ADVANCED INTELLIGENT
SYSTEM S 575 13.1 OPENING VIGNETTE: IMPROVING URBAN INFRASTRUCTURE
MANAGEMENT IN THE CITY OF VERDUN 576 13.2 MACHINE-LEARNING TECHNIQUES
577 13.3 CASE-BASED REASONING (CBR) 580 CBR IMPROVES JET ENGINE
MAINTENANCE, REDUCES COSTS 585 13.4 GENETIC ALGORITHM FUNDAMENTALS 587
13.5 DEVELOPING GENETIC ALGORITHM APPLICATIONS 592 GENETIC ALGORITHMS
SCHEDULE ASSEMBLY LINES AT VOLVO TRUCKS NORTH AMERICA 593 13.6 FUZZY
LOGIC FUNDAMENTALS 595 13.7 NATURAL LANGUAGE PROCESSING (NLP) 598 13.8
VOICE TECHNOLOGIES 601 13.9 DEVELOPING INTEGRATED ADVANCED SYSTEMS 605
HYBRID ES AND FUZZY LOGIC SYSTEM DISPATCHES TRAINS 607 END OF CHAPTER
APPLICATION CASE BARCLAYS USES VOICE TECHNOLOGY TO EXCEL 611 REFERENCES
612 CHAPTER 14 INTELLIGENT SYSTEMS OVER THE INTERNET 614 14.1 OPENING
VIGNETTE: NETFLIX GAINS HIGH CUSTOMER SATISFACTION 14.2 14.3 14.4 14.5
14.6 14.7 14.8 14.9 14.10 FROM DVD RECOMMENDATION 615 WEB-BASED
INTELLIGENT SYSTEMS 617 INTELLIGENT AGENTS: AN OVERVIEW 629
CHARACTERISTICS OF INTELLIGENT AGENTS 622 WHY USE INTELLIGENT AGENTS?
624 CLASSIFICATION AND TYPES OF INTELLIGENT AGENTS 626 INTERNET-BASED
SOFTWARE AGENTS 629 FUJITSU(JAPAN) USES AGENTS FOR TARGETED ADVERTISING
WYNDHAM USES INTELLIGENT AGENTS IN ITS CALL CENTER AGENTS AND
MULTIAGENTS 637 THE SEMANTIC WEB: REPRESENTING KNOWLEDGE FOR THE
INTELLIGENT AGENTS 641 WEB-BASED RECOMMENDATION SYSTEMS 647 635 637
14.11 AMAZON.COM USES COLLABORATIVE FILTERING TO RECOMMEND PRODUCTS 648
CONTENT-BASED FILTERING AT EUROVACATIONS.COM MANAGERIAL ISSUES OF
INTELLIGENT AGENTS 654 653 XVIII CONTENTS END OF CHAPTER APPLICATION
CASE SPARTAN USES INTELLIGENT SYSTEMS TO FIND THE RIGHT PERSON AND
REDUCE TURNOVER 659 REFERENCES 660 PART VI IMPLEMENTING DECISION SUPPORT
SYSTEMS 663 CHAPTER 15 15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8 15.9
SYSTEM DEVELOPMENT AND ACQUISITION 665 OPENING VIGNETTE: OSRAM SYLVANIA
THINKS SMALL, STRATEGIZES BIG TO DEVELOP THE HR INFONET PORTAL SYSTEM
666 WHAT TYPES OF SUPPORT SYSTEMS SHOULD YOU BUILD? 668 THE LANDSCAPE
AND FRAMEWORK OF MANAGEMENT SUPPORT SYSTEMS APPLICATION DEVELOPMENT 670
DEVELOPMENT OPTIONS FOR MANAGEMENT SUPPORT SYSTEM APPLICATIONS
PROTOTYPING: A PRACTICAL MANAGEMENT SUPPORT SYSTEM DEVELOPMENT
METHODOLOGY 681 CRITERIA FOR SELECTING AN MANAGEMENT SUPPORT SYSTEM
DEVELOPMENT APPROACH 687 THIRD-PARTY PROVIDERS OF MANAGEMENT SUPPORT
SYSTEM SOFTWARE PACKAGES AND SUITES 689 FLORICULTURE PARTNERSHIP
STREAMLINES REAL-TIME ORDERING 692 CONNECTING TO DATABASES AND OTHER
ENTERPRISE SYSTEMS 693 THE RISE OF WEB SERVICES, XML, AND THE
SERVICE-ORIENTED ARCHITECTURE 695 673 LINCOLN FINANCIAL EXCELS BY USING
WEB SERVICES 696 15.10 USER-DEVELOPED MANAGEMENT SUPPORT SYSTEM 697
END-USER DEVELOPMENT USING WIKIS 697 15.11 MANAGEMENT SUPPORT SYSTEM
VENDOR AND SOFTWARE SELECTION 70 0 15.12 PUTTING TOGETHER AN MANAGEMENT
SUPPORT SYSTEM 701 END OF CHAPTER APPLICATION CASE A FULLY INTEGRATED
MSS FOR STERNGOLD: AN OLD DENTAL MANUFACTURER ADOPTS NEW IT TRICKS 705
REFERENCES 706 CHAPTER 16 INTEGRATION, IMPACTS AND THE FUTURE OF
MANAGEMENT SUPPORT SYSTEMS 708 16.1 OPENING VIGNETTE: ELITE CARE
SUPPORTED BY INTELLIGENT SYSTEMS 709 16.2 SYSTEMS INTEGRATION: AN
OVERVIEW 711 16.3 TYPES OF MANAGEMENT SUPPORT SYSTEM INTEGRATION 715
16.4 INTEGRATION WITH ENTERPRISE SYSTEMS AND KNOWLEDGE MANAGEMENT 720
THE IMPACTS OF MANAGEMENT SUPPORT SYSTEMS: AN OVERVIEW 725 MANAGEMENT
SUPPORT SYSTEMS IMPACTS ON ORGANIZATIONS 726 16.5 16.6 16.7 MANAGEMENT
SUPPORT SYSTEMS IMPACTS ON INDIVIDUALS 730 CONTENTS XIX ^ 16.8
AUTOMATING DECISION MAKING AND THE MANAGER S JOB 731 16.9 ISSUES OF
LEGALITY, PRIVACY, AND ETHICS 733 16.10 INTELLIGENT AND AUTOMATED
SYSTEMS AND EMPLOYMENT LEVELS 737 ROBOTS 738 16.11 OTHER SOCIETAL
IMPACTS OF MANAGEMENT SUPPORT SYSTEMS AND THE DIGITAL DIVIDE 739 16.12
THE FUTURE OF MANAGEMENT SUPPORT SYSTEMS 742 END OF CHAPTER APPLICATION
CASE AN INTELLIGENT LOGISTICS SUPPORT SYSTEM 747 REFERENCES 748 ONLINE
MATERIAL CHAPTER 17 ENTERPRISE SYSTEMS 751 CHAPTER 18 KNOWLEDGE
ACQUISITION, REPRESENTATION, AND REASONING 752 CHAPTER 1 ONLINE FILES
WL.L REPRESENTATIVE DECISION SUPPORT TOOLS W1.2 DECISION SUPPORT
TECHNOLOGIES AND THE WEB W1.3 EMERGING TECHNOLOGIES THAT MAY BENEFIT
DECISION SUPPORT W1.4 ADDITIONAL REFERENCES W1.5 TERADATA UNIVERSITY
NETWORK CHAPTER 2 ONLINE FILES W2.1: THE MMS RUNNING CASE W2.2: WEB
SOURCES FOR DECISION-MAKING SUPPORT SAMPLER W2.3: FURTHER READING
CHAPTER 3 ONLINE FILES W3.1: DATABASES W3.2: MAJOR CAPABILITIES OF THE
UIMS W3.3: AD HOC VISUAL BASIC DSS EXAMPLE W3.4: FURTHER READING ON DSS
CHAPTER 4 ONLINE FILES W4.1: INFLUENCE DIAGRAMS W4.2: LINKS TO
SPREADSHEET-BASED DSS EXCEL FILES IN CHAPTER 4 W4.3: SPREADSHEET-BASED
ECONOMIC ORDER QUANTITY SIMULATION MODEL W4.4: WAITING LINE MODELING
(QUEUEING) IN A SPREADSHEET W4.5: LINEAR PROGRAMMING OPTIMIZATION: THE
BLENDING PROBLEM W4.6: LINDO EXAMPLE: THE PRODUCT-MIX MODEL W4.7: LINGO
EXAMPLE: THE PRODUCT-MIX MODEL W4.8: THE GOAL PROGRAMMING MBI MODEL
W4.9: LINKS TO EXCEL FILES OF SECTION 4.9 W4.10: TABLE OF MODELS AND WEB
IMPACTS W4.LL: MODEL BASE MANAGEMENT W4.12: ADDITIONAL REFERENCES XX
CONTENTS BI PREVIEW CHAPTER ONLINE FILES WS.L: THE GENERAL PROCESS OF
INTELLIGENCE CREATION AND USE AS REFLECTED IN CONTINENTAL AIRLINE CASE
WS.2: BI GOVERNANCE WS.3: THE BI USER COMMUNITY WS.4: AN ACTION PLAN FOR
THE INFORMATION SYSTEMS ORGANIZATION CHAPTER 6 ONLINE FILES W6.1
CAPABILITIES OF EIS W6.2 SAP ANALYTICS W6.3 TRENDS IN VISUALIZATION
PRODUCTS FOR DECISION SUPPORT W6.4 VIRTUAL REALTY VISUALIZATION W6.5
COMPETITIVE INTELLIGENCE ON THE INTERNET W6.6 CABELA S CHAPTER 7 ONLINE
FILES W7.1 DATA MINING CHAPTER 8 ONLINE FILES W8.1 HEARTDISEASE.STA W8.2
CREDITRISK.XLS W8.3 MOVIETRAIN.XLS W8.4 MOVIETEST.XLS W8.5 STATISTICA
COUPON CHAPTER 9 ONLINE FILES W9.1 PORTFOLIO OF OPTIONS W9.2 ROLLING
FORECASTS AND REAL-TIME DATA W9.3 EFFECTIVE PERFORMANCE MEASUREMENT W9.4
SIX SIGMA ROLES W9.5 PROBLEMS WITH DASHBOARD DISPLAYS CHAPTER 10 ONLINE
FILES W10.1 SEVEN SINS OF DEADLY MEETINGS AND SEVEN STEPS TO SALVATION
W10.2 WHITEBOARDS W10.3 INTERNET VOTING W10.4 GROUPSYSTEMS TOOLS FOR
SUPPORT OF GROUP PROCESSES W10.5 COLLABORATION IN DESIGNING STORES
CHAPTER 11 ONLINE FILES WLL.L LEVERAGING KNOWLEDGE THROUGH KNOWLEDGE
MANAGEMENT SYSTEMS CHAPTER 12 ONLINE FILES W12.1 INTELLIGENT SYSTEMS
W12.2 INTERNET-BASED INTELLIGENT TUTORING SYSTEMS W12.3 AUTOMATING THE
HELP DESK W12.4 ASSIGNMENT ES CHAPTER 13 ONLINE FILES W13.1: STEPS IN
THE CBR PROCESS W13.2: AUTOMATING A HELP DESK WITH CASE-BASED REASONING
W13.3: AUTOMATIC TRANSLATION OF WEB PAGES CONTENTS XXI CHAPTER 15 ONLINE
FILES W15.1 GUIDELINES FOR A THINK SMALL, STRATEGIZE BIG
IMPLEMENTATION W15.2 PROJECT MANAGEMENT SOFTWARE W15.3 UTILITY COMPUTING
W15.4 AGILE DEVELOPMENT AND EXTREME PROGRAMMING (XP) W15.5 A PROTOTYPING
APPROACH TO DSS DEVELOPMENT W15.6 IBM S WEBSPHERE COMMERCE SUITE W15.7
XML, WEB SERVICES AND SERVICE-ORIENTED ARCHITECTURE W15.8 THE PROCESS OF
SELECTING A SOFTWARE VENDOR AND AN MSS PACKAGE CHAPTER 16 ONLINE FILES
W16.1 AN ACTIVE AND SELF-EVOLVING MODEL OF INTELLIGENT DSS W16.2 COOKIES
AND SPYWARE W16.3 A FRAMEWORK FOR ETHICAL ISSUES W16.4 A HYBRID
INTELLIGENT SYSTEM ONLINE TUTORIALS TUTORIAL 1: SYSTEMS TUTORIAL 2:
FORECASTING TUTORIAL 3: TEXT MINING PROJECT TUTORIAL 4: STATISTICA
SOFTWARE PROJECT REFERENCES 748 GLOSSARY 751 INDEX 763
|
| any_adam_object | 1 |
| author_GND | (DE-588)13563024X |
| building | Verbundindex |
| bvnumber | BV035347232 |
| callnumber-first | H - Social Science |
| callnumber-label | HD30 |
| callnumber-raw | HD30.2 |
| callnumber-search | HD30.2 |
| callnumber-sort | HD 230.2 |
| callnumber-subject | HD - Industries, Land Use, Labor |
| classification_rvk | ST 302 ST 515 |
| ctrlnum | (OCoLC)317458794 (DE-599)BVBBV035347232 |
| dewey-full | 658.4/038011 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.4/038011 |
| dewey-search | 658.4/038011 |
| dewey-sort | 3658.4 538011 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Informatik Wirtschaftswissenschaften |
| edition | 8. ed., internat. ed. |
| format | Book |
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| id | DE-604.BV035347232 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:31:49Z |
| institution | BVB |
| isbn | 9780131580176 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017151443 |
| oclc_num | 317458794 |
| open_access_boolean | |
| owner | DE-20 DE-945 DE-Aug4 |
| owner_facet | DE-20 DE-945 DE-Aug4 |
| physical | XXVIII, 772 S. graph. Darst. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Pearson Prentice Hall |
| record_format | marc |
| spelling | Decision support and business intelligence systems Efraim Turban ... 8. ed., internat. ed. Upper Saddle River, NJ Pearson Prentice Hall 2007 XXVIII, 772 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Bis 7. Aufl. u.d.T.: Decision support systems and intelligent systems Ciência da computação larpcal Gestion - Informatique Gestion - Informatique ram Inteligência empresarial larpcal Systèmes d'aide à la décision Systèmes d'aide à la décision ram Systèmes experts (Informatique) Systèmes experts (informatique) ram Unternehmen (DE-588)4061963-1 gnd rswk-swf Entscheidungsunterstützung (DE-588)4202171-6 gnd rswk-swf Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd rswk-swf Expertensystem (DE-588)4113491-6 gnd rswk-swf Managementinformationssystem (DE-588)4074518-1 gnd rswk-swf Expertensystem (DE-588)4113491-6 s Entscheidungsunterstützung (DE-588)4202171-6 s Managementinformationssystem (DE-588)4074518-1 s Unternehmen (DE-588)4061963-1 s 1\p DE-604 Entscheidungsunterstützungssystem (DE-588)4191815-0 s 2\p DE-604 DE-604 Turban, Efraim 1930- Sonstige (DE-588)13563024X oth SWBplus Fremddatenuebernahme application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017151443&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Decision support and business intelligence systems Ciência da computação larpcal Gestion - Informatique Gestion - Informatique ram Inteligência empresarial larpcal Systèmes d'aide à la décision Systèmes d'aide à la décision ram Systèmes experts (Informatique) Systèmes experts (informatique) ram Unternehmen (DE-588)4061963-1 gnd Entscheidungsunterstützung (DE-588)4202171-6 gnd Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd Expertensystem (DE-588)4113491-6 gnd Managementinformationssystem (DE-588)4074518-1 gnd |
| subject_GND | (DE-588)4061963-1 (DE-588)4202171-6 (DE-588)4191815-0 (DE-588)4113491-6 (DE-588)4074518-1 |
| title | Decision support and business intelligence systems |
| title_auth | Decision support and business intelligence systems |
| title_exact_search | Decision support and business intelligence systems |
| title_full | Decision support and business intelligence systems Efraim Turban ... |
| title_fullStr | Decision support and business intelligence systems Efraim Turban ... |
| title_full_unstemmed | Decision support and business intelligence systems Efraim Turban ... |
| title_short | Decision support and business intelligence systems |
| title_sort | decision support and business intelligence systems |
| topic | Ciência da computação larpcal Gestion - Informatique Gestion - Informatique ram Inteligência empresarial larpcal Systèmes d'aide à la décision Systèmes d'aide à la décision ram Systèmes experts (Informatique) Systèmes experts (informatique) ram Unternehmen (DE-588)4061963-1 gnd Entscheidungsunterstützung (DE-588)4202171-6 gnd Entscheidungsunterstützungssystem (DE-588)4191815-0 gnd Expertensystem (DE-588)4113491-6 gnd Managementinformationssystem (DE-588)4074518-1 gnd |
| topic_facet | Ciência da computação Gestion - Informatique Inteligência empresarial Systèmes d'aide à la décision Systèmes experts (Informatique) Systèmes experts (informatique) Unternehmen Entscheidungsunterstützung Entscheidungsunterstützungssystem Expertensystem Managementinformationssystem |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017151443&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT turbanefraim decisionsupportandbusinessintelligencesystems |