Business Intelligence and Analytics For these Global Editions, the editorial team at Pearson has collaborated with educators across the world to address a wide range of subjects and requirements, equipping students with the best possible learning tools This Global Edition preserves the cutting-edge approach and pedagogy of the original, but also features alterations, customization and adaptation from the North American version TENTH EDITION Pearson Global Edition Sharda • Delen • Turban This is a special edition of an established title widely used by colleges and universities throughout the world Pearson published this exclusive edition for the benefit of students outside the United States and Canada If you purchased this book within the United States or Canada you should be aware that it has been imported without the approval of the Publisher or Author GLOBAL EDITION GLOBAL EDITION GLOBAL EDITION Business Intelligence and Analytics Systems for Decision Support TENTH EDITION Ramesh Sharda • Dursun Delen • Efraim Turban Tenth Edition Business Intelligence and Analytics: Systems for Decision Support Global Edition Ramesh Sharda Oklahoma State University Dursun Delen Oklahoma State University Efraim Turban University of Hawaii With contributions by J E Aronson The University of Georgia Ting-Peng Liang National Sun Yat-sen University David King JDA Software Group, Inc Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montréal Toronto Delhi Mexico City São Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo A01_SHAR9209_10_PIE_FM.indd 1/25/14 7:47 AM Editor in Chief: Stephanie Wall Executive Editor: Bob Horan Publisher, Global Edition: Laura Dent Senior Acquisitions Editor, Global Edition: Steven Jackson Program Manager Team Lead: Ashley Santora Program Manager: Denise Vaughn Marketing Manager, International: Kristin Schneider Project Manager Team Lead: Judy Leale Project Manager: Tom 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published as part of the services for any purpose All such documents and related graphics are provided “as is” without warranty of any kind Microsoft and/or its respective suppliers hereby disclaim all warranties and conditions with regard to this information, including all warranties and conditions of merchantability, whether express, implied or statutory, fitness for a particular purpose, title and non-infringement In no event shall Microsoft and/or its respective suppliers be liable for any special, indirect or consequential damages or any damages whatsoever resulting from loss of use, data or profits, whether in an action of contract, negligence or other tortious action, arising out of or in connection with the use or performance of information available from the services The documents and related graphics contained herein could include technical inaccuracies or typographical errors Changes are periodically added to the information herein Microsoft and/or its respective suppliers may make improvements and/or changes in the product(s) and/or the program(s) described herein at any time Partial screen shots may be viewed in full within the software version specified Microsoft® and Windows® are registered trademarks of the Microsoft Corporation in the U.S.A and other countries This book is not sponsored or endorsed by or affiliated with the Microsoft Corporation ISBN 10: 1-292-00920-9 ISBN 13: 978-1-292-00920-9 British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library 987654321 14 13 12 11 10 Typeset in ITC Garamond Std Integra Software Solutions Printed and bound by Courier Kendalville in The United States of America A01_SHAR9209_10_PIE_FM.indd 1/25/14 7:47 AM BRIEF Contents Preface 21 About the Authors 29 Part I Decision Making and Analytics: An Overview 31 Chapter Chapter An Overview of Business Intelligence, Analytics, and Decision Support 32 Foundations and Technologies for Decision Making 67 Part II Descriptive Analytics 107 Chapter Chapter Data Warehousing 108 Business Reporting, Visual Analytics, and Business Performance Management 165 Part III Predictive Analytics 215 Chapter Chapter Chapter Chapter Data Mining 216 Techniques for Predictive Modeling 273 Text Analytics, Text Mining, and Sentiment Analysis 318 Web Analytics, Web Mining, and Social Analytics 368 Part IV Prescriptive Analytics 421 Chapter Model-Based Decision Making: Optimization and MultiCriteria Systems 422 Chapter 10 Modeling and Analysis: Heuristic Search Methods and Simulation 465 Chapter 11 Automated Decision Systems and Expert Systems 499 Chapter 12 Knowledge Management and Collaborative Systems 537 Part V Big Data and Future Directions for Business Analytics 571 Chapter 13 Big Data and Analytics 572 Chapter 14 Business Analytics: Emerging Trends and Future Impacts 622 Glossary 664 Index 678 A01_SHAR9209_10_PIE_FM.indd 1/25/14 7:47 AM Contents Preface 21 About the Authors 29 Part I Decision Making and Analytics: An Overview 31 Chapter An Overview of Business Intelligence, Analytics, and Decision Support 32 1.1 Opening Vignette: Magpie Sensing Employs Analytics to Manage a Vaccine Supply Chain Effectively and Safely 33 1.2 Changing Business Environments and Computerized Decision Support 35 The Business Pressures–Responses–Support Model 35 1.3 Managerial Decision Making 37 The Nature of Managers’ Work 37 The Decision-Making Process 38 1.4 Information Systems Support for Decision Making 39 1.5 An Early Framework for Computerized Decision Support 41 The Gorry and Scott-Morton Classical Framework 41 Computer Support for Structured Decisions 42 Computer Support for Unstructured Decisions 43 Computer Support for Semistructured Problems 43 1.6 The Concept of Decision Support Systems (DSS) 43 DSS as an Umbrella Term 43 Evolution of DSS into Business Intelligence 44 1.7 A Framework for Business Intelligence (BI) 44 Definitions of BI 44 A Brief History of BI 44 The Architecture of BI 45 Styles of BI 45 The Origins and Drivers of BI 46 A Multimedia Exercise in Business Intelligence 46 ▶ Application Case 1.1 Sabre Helps Its Clients Through Dashboards and Analytics 47 The DSS–BI Connection 48 1.8 Business Analytics Overview 49 Descriptive Analytics 50 ▶ Application Case 1.2 Eliminating Inefficiencies at Seattle Children’s Hospital 51 ▶ Application Case 1.3 Analysis at the Speed of Thought 52 Predictive Analytics 52 A01_SHAR9209_10_PIE_FM.indd 1/25/14 7:47 AM Contents ▶ Application Case 1.4 Moneyball: Analytics in Sports and Movies 53 ▶ Application Case 1.5 Analyzing Athletic Injuries 54 Prescriptive Analytics 54 ▶ Application Case 1.6 Industrial and Commercial Bank of China (ICBC) Employs Models to Reconfigure Its Branch Network 55 Analytics Applied to Different Domains 56 Analytics or Data Science? 56 1.9 Brief Introduction to Big Data Analytics 57 What Is Big Data? 57 ▶ Application Case 1.7 Gilt Groupe’s Flash Sales Streamlined by Big Data Analytics 59 1.10 Plan of the Book 59 Part I: Business Analytics: An Overview 59 Part II: Descriptive Analytics 60 Part III: Predictive Analytics 60 Part IV: Prescriptive Analytics 61 Part V: Big Data and Future Directions for Business Analytics 61 1.11 Resources, Links, and the Teradata University Network Connection 61 Resources and Links 61 Vendors, Products, and Demos 61 Periodicals 61 The Teradata University Network Connection 62 The Book’s Web Site 62 Chapter Highlights 62 • Key Terms 63 Questions for Discussion 63 • Exercises 63 ▶ End-of-Chapter Application Case Nationwide Insurance Used BI to Enhance Customer Service 64 References 65 Chapter Foundations and Technologies for Decision Making 67 2.1 Opening Vignette: Decision Modeling at HP Using Spreadsheets 68 2.2 Decision Making: Introduction and Definitions 70 Characteristics of Decision Making 70 A Working Definition of Decision Making 71 Decision-Making Disciplines 71 Decision Style and Decision Makers 71 2.3 Phases of the Decision-Making Process 72 2.4 Decision Making: The Intelligence Phase 74 Problem (or Opportunity) Identification 75 ▶ Application Case 2.1 Making Elevators Go Faster! 75 Problem Classification 76 Problem Decomposition 76 Problem Ownership 76 A01_SHAR9209_10_PIE_FM.indd 1/25/14 7:47 AM 6 Contents 2.5 Decision Making: The Design Phase 77 Models 77 Mathematical (Quantitative) Models 77 The Benefits of Models 77 Selection of a Principle of Choice 78 Normative Models 79 Suboptimization 79 Descriptive Models 80 Good Enough, or Satisficing 81 Developing (Generating) Alternatives 82 Measuring Outcomes 83 Risk 83 Scenarios 84 Possible Scenarios 84 Errors in Decision Making 84 2.6 Decision Making: The Choice Phase 85 2.7 Decision Making: The Implementation Phase 85 2.8 How Decisions Are Supported 86 Support for the Intelligence Phase 86 Support for the Design Phase 87 Support for the Choice Phase 88 Support for the Implementation Phase 88 2.9 Decision Support Systems: Capabilities 89 A DSS Application 89 2.10 DSS Classifications 91 The AIS SIGDSS Classification for DSS 91 Other DSS Categories 93 Custom-Made Systems Versus Ready-Made Systems 93 2.11 Components of Decision Support Systems 94 The Data Management Subsystem 95 The Model Management Subsystem 95 ▶ Application Case 2.2 Station Casinos Wins by Building Customer Relationships Using Its Data 96 ▶ Application Case 2.3 SNAP DSS Helps OneNet Make Telecommunications Rate Decisions 98 The User Interface Subsystem 98 The Knowledge-Based Management Subsystem 99 ▶ Application Case 2.4 From a Game Winner to a Doctor! 100 Chapter Highlights 102 • Key Terms 103 Questions for Discussion 103 • Exercises 104 ▶ End-of-Chapter Application Case Logistics Optimization in a Major Shipping Company (CSAV) 104 References 105 A01_SHAR9209_10_PIE_FM.indd 1/25/14 7:47 AM Contents Part II Descriptive Analytics 107 Chapter Data Warehousing 108 3.1 Opening Vignette: Isle of Capri Casinos Is Winning with Enterprise Data Warehouse 109 3.2 Data Warehousing Definitions and Concepts 111 What Is a Data Warehouse? 111 A Historical Perspective to Data Warehousing 111 Characteristics of Data Warehousing 113 Data Marts 114 Operational Data Stores 114 Enterprise Data Warehouses (EDW) 115 Metadata 115 ▶ Application Case 3.1 A Better Data Plan: Well-Established TELCOs Leverage Data Warehousing and Analytics to Stay on Top in a Competitive Industry 115 3.3 Data Warehousing Process Overview 117 ▶ Application Case 3.2 Data Warehousing Helps MultiCare Save More Lives 118 3.4 Data Warehousing Architectures 120 Alternative Data Warehousing Architectures 123 Which Architecture Is the Best? 126 3.5 Data Integration and the Extraction, Transformation, and Load (ETL) Processes 127 Data Integration 128 ▶ Application Case 3.3 BP Lubricants Achieves BIGS Success 128 Extraction, Transformation, and Load 130 3.6 Data Warehouse Development 132 ▶ Application Case 3.4 Things Go Better with Coke’s Data Warehouse 133 Data Warehouse Development Approaches 133 ▶ Application Case 3.5 Starwood Hotels & Resorts Manages Hotel Profitability with Data Warehousing 136 Additional Data Warehouse Development Considerations 137 Representation of Data in Data Warehouse 138 Analysis of Data in the Data Warehouse 139 OLAP Versus OLTP 140 OLAP Operations 140 3.7 Data Warehousing Implementation Issues 143 ▶ Application Case 3.6 EDW Helps Connect State Agencies in Michigan 145 Massive Data Warehouses and Scalability 146 3.8 Real-Time Data Warehousing 147 ▶ Application Case 3.7 Egg Plc Fries the Competition in Near Real Time 148 A01_SHAR9209_10_PIE_FM.indd 1/25/14 7:47 AM 8 Contents 3.9 Data Warehouse Administration, Security Issues, and Future Trends 151 The Future of Data Warehousing 153 3.10 Resources, Links, and the Teradata University Network Connection 156 Resources and Links 156 Cases 156 Vendors, Products, and Demos 157 Periodicals 157 Additional References 157 The Teradata University Network (TUN) Connection 157 Chapter Highlights 158 • Key Terms 158 Questions for Discussion 158 • Exercises 159 ▶ End-of-Chapter Application Case Continental Airlines Flies High with Its Real-Time Data Warehouse 161 References 162 Chapter Business Reporting, Visual Analytics, and Business Performance Management 165 4.1 Opening Vignette:Self-Service Reporting Environment Saves Millions for Corporate Customers 166 4.2 Business Reporting Definitions and Concepts 169 What Is a Business Report? 170 ▶ Application Case 4.1 Delta Lloyd Group Ensures Accuracy and Efficiency in Financial Reporting 171 Components of the Business Reporting System 173 ▶ Application Case 4.2 Flood of Paper Ends at FEMA 174 4.3 Data and Information Visualization 175 ▶ Application Case 4.3 Tableau Saves Blastrac Thousands of Dollars with Simplified Information Sharing 176 A Brief History of Data Visualization 177 ▶ Application Case 4.4 TIBCO Spotfire Provides Dana-Farber Cancer Institute with Unprecedented Insight into Cancer Vaccine Clinical Trials 179 4.4 Different Types of Charts and Graphs 180 Basic Charts and Graphs 180 Specialized Charts and Graphs 181 4.5 The Emergence of Data Visualization and Visual Analytics 184 Visual Analytics 186 High-Powered Visual Analytics Environments 188 4.6 Performance Dashboards 190 ▶ Application Case 4.5 Dallas Cowboys Score Big with Tableau and Teknion 191 A01_SHAR9209_10_PIE_FM.indd 1/25/14 7:47 AM Contents Dashboard Design 192 ▶ Application Case 4.6 Saudi Telecom Company Excels with Information Visualization 193 What to Look For in a Dashboard 194 Best Practices in Dashboard Design 195 Benchmark Key Performance Indicators with Industry Standards 195 Wrap the Dashboard Metrics with Contextual Metadata 195 Validate the Dashboard Design by a Usability Specialist 195 Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard 195 Enrich Dashboard with Business Users’ Comments 195 Present Information in Three Different Levels 196 Pick the Right Visual Construct Using Dashboard Design Principles 196 Provide for Guided Analytics 196 4.7 Business Performance Management 196 Closed-Loop BPM Cycle 197 ▶ Application Case 4.7 IBM Cognos Express Helps Mace for Faster and Better Business Reporting 199 4.8 Performance Measurement 200 Key Performance Indicator (KPI) 201 Performance Measurement System 202 4.9 Balanced Scorecards 202 The Four Perspectives 203 The Meaning of Balance in BSC 204 Dashboards Versus Scorecards 204 4.10 Six Sigma as a Performance Measurement System 205 The DMAIC Performance Model 206 Balanced Scorecard Versus Six Sigma 206 Effective Performance Measurement 207 ▶ Application Case 4.8 Expedia.com’s Customer Satisfaction Scorecard 208 Chapter Highlights 209 • Key Terms 210 Questions for Discussion 211 • Exercises 211 ▶ End-of-Chapter Application Case Smart Business Reporting Helps Healthcare Providers Deliver Better Care 212 References 214 Part III Predictive Analytics 215 Chapter Data Mining 216 5.1 Opening Vignette: Cabela’s Reels in More Customers with Advanced Analytics and Data Mining 217 5.2 Data Mining Concepts and Applications 219 ▶ Application Case 5.1 Smarter Insurance: Infinity P&C Improves Customer Service and Combats Fraud with Predictive Analytics 221 A01_SHAR9209_10_PIE_FM.indd 1/25/14 7:47 AM www.downloadslide.net 674 Glossary risk A probabilistic or stochastic decision situation risk analysis A decision-making method that analyzes the risk (based on assumed known probabilities) associated with different alternatives robot A machine that has the capability of performing manual functions without human intervention rule-based system A system in which knowledge is represented completely in terms of rules (e.g., a system based on production rules) SAS Enterprise Miner A comprehensive, commercial data mining software tool developed by SAS Institute satisficing A process by which one seeks a solution that will satisfy a set of constraints In contrast to optimization, which seeks the best possible solution, satisficing simply seeks a solution that will work well enough scenario A statement of assumptions and configurations concerning the operating environment of a particular system at a particular time scorecard A visual display that is used to chart progress against strategic and tactical goals and targets screen sharing Software that enables group members, even in different locations, to work on the same document, which is shown on the PC screen of each participant search engine A program that finds and lists Web sites or pages (designated by URLs) that match some user-selected criteria search engine optimization (SEO) The intentional activity of affecting the visibility of an e-commerce site or a Web site in a search engine’s natural (unpaid or organic) search results self-organizing A neural network architecture that uses unsupervised learning semantic Web An extension of the current Web, in which information is given well-defined meanings, better enabling computers and people to work in cooperation semantic Web services An XML-based technology that allows semantic information to be represented in Web services semistructured problem A category of decision problems where the decision process has some structure to it but still requires subjective analysis and an iterative approach SentiWordNet An extension of WordNet to be used for sentiment identification See WordNet sequence discovery The identification of associations over time sequence mining A pattern discovery method where relationships among the things are examined in terms of their order of occurrence to identify associations over time sigmoid (logical activation) function An S-shaped transfer function in the range of to simple split Data is partitioned into two mutually exclusive subsets called a training set and a test set (or holdout set) It is common to designate two-thirds of the data as the training set and the remaining one-third as the test set simulation An imitation of reality in computers singular value decomposition (SVD) Closely related to principal components analysis, reduces the overall dimensionality of the input matrix (number of input documents by number of extracted terms) to a lower dimensional space, where each consecutive dimension represents the largest degree of variability (between words and documents) Six Sigma A performance management methodology aimed at reducing the number of defects in a business process to as close to zero defects per million opportunities (DPMO) as possible social analytics The monitoring, analyzing, measuring, and interpreting digital interactions and relationships of people, topics, ideas, and content social media The online platforms and tools that people use to share opinions, experiences, insights, perceptions, and various media, including photos, videos, or music, with each other The enabling technologies of social interactions among people in which they create, share, and exchange information, ideas, and opinions in virtual communities and networks social media analytics The systematic and scientific ways to consume the vast amount of content created by Webbased social media outlets, tools, and techniques for the betterment of an organization’s competitiveness SEMMA An alternative process for data mining projects proposed by the SAS Institute The acronym “SEMMA” stands for “sample, explore, modify, model, and assess.” social network analysis (SNA) The mapping and measuring of relationships and information flows among people, groups, organizations, computers, and other information- or knowledge-processing entities The nodes in the network are the people and groups, whereas the links show relationships or flows between the nodes sensitivity analysis A study of the effect of a change in one or more input variables on a proposed solution software agent A piece of autonomous software that persists to accomplish the task it is designed for (by its owner) sentiment A settled opinion reflective of one’s feelings software-as-a-service instead of sold sentiment analysis The technique used to detect favorable and unfavorable opinions toward specific products and services using a large number of textual data sources (customer feedback in the form of Web postings) Z01_SHAR9209_10_PIE_GLOS.indd 674 (SaaS) Software that is rented speech analytics A growing field of science that allows users to analyze and extract information from both live and recorded conversations 1/25/14 7:17 AM www.downloadslide.net Glossary 675 speech (voice) understanding An area of artificial intelligence research that attempts to allow computers to recognize words or phrases of human speech staff assistant An individual who acts as an assistant to a manager static models Models that describe a single interval of a situation status report A report that provides the most current information on the status of an item (e.g., orders, expenses, production quantity) stemming A process of reducing words to their respective root forms in order to better represent them in a text mining project stop words Words that are filtered out prior to or after processing of natural language data (i.e., text) story A case with rich information and episodes Lessons may be derived from this kind of case in a case base strategic goal A quantified objective that has a designated time period strategic models Models that represent problems for the strategic level (i.e., executive level) of management strategic objective A broad statement or general course of action that prescribes targeted directions for an organization strategic theme A collection of related strategic objectives, used to simplify the construction of a strategic map strategic vision A picture or mental image of what the organization should look like in the future strategy map A visual display that delineates the relationships among the key organizational objectives for all four balanced scorecard perspectives stream analytics A term commonly used for extracting actionable information from continuously flowing/streaming data sources structured problem A decision situation where a specific set of steps can be followed to make a straightforward decision Structured Query Language (SQL) A data definition and management language for relational databases SQL front ends most relational DBMS suboptimization An optimization-based procedure that does not consider all the alternatives for or impacts on an organization summation function A mechanism to add all the inputs coming into a particular neuron support vector machines (SVM) A family of generalized linear models, which achieve a classification or regression decision based on the value of the linear combination of input features synapse The connection (where the weights are) between processing elements in a neural network synchronous (real time) Occurring at the same time system architecture The logical and physical design of a system system development lifecycle (SDLC) A systematic process for the effective construction of large information systems systems dynamics Macro-level simulation models in which aggregate values and trends are considered The objective is to study the overall behavior of a system over time, rather than the behavior of each individual participant or player in the system tacit knowledge Knowledge that is usually in the domain of subjective, cognitive, and experiential learning It is highly personal and difficult to formalize tactical models Models that represent problems for the tactical level (i.e., midlevel) of management teleconferencing The use of electronic communication that allows two or more people at different locations to have a simultaneous conference term–document matrix (TDM) A frequency matrix created from digitized and organized documents (the corpus) where the columns represent the terms while rows represent the individual documents text analytics A broader concept that includes information retrieval (e.g., searching and identifying relevant documents for a given set of key terms) as well as information extraction, data mining, and Web mining text mining The application of data mining to nonstructured or less structured text files It entails the generation of meaningful numeric indices from the unstructured text and then processing those indices using various data mining algorithms theory of certainty factors A theory designed to help incorporate uncertainty into the representation of knowledge (in terms of production rules) for expert systems threshold value A hurdle value for the output of a neuron to trigger the next level of neurons If an output value is smaller than the threshold value, it will not be passed to the next level of neurons supervised learning A method of training artificial neural networks in which sample cases are shown to the network as input, and the weights are adjusted to minimize the error in the outputs tokenizing Categorizing a block of text (token) according to the function it performs support The measure of how often products and/or services appear together in the same transaction; that is, the proportion of transactions in the data set that contain all of the products and/or services mentioned in a specific rule transformation (transfer) function In a neural network, the function that sums and transforms inputs before a neuron fires It shows the relationship between the internal activation level and the output of a neuron Z01_SHAR9209_10_PIE_GLOS.indd 675 topology The way in which neurons are organized in a neural network 1/25/14 7:17 AM www.downloadslide.net 676 Glossary trend analysis The collecting of information and attempting to spot a pattern, or trend, in the information virtual worlds Artificial worlds created by computer systems in which the user has the impression of being immersed Turing test A test designed to measure the “intelligence” of a computer visual analytics The combination of visualization and predictive analytics uncertainty In expert systems, a value that cannot be determined during a consultation Many expert systems can accommodate uncertainty; that is, they allow the user to indicate whether he or she does not know the answer visual interactive modeling (VIM) See visual interactive simulation (VIS) uncontrollable variable (parameter) A factor that affects the result of a decision but is not under the control of the decision maker These variables can be internal (e.g., related to technology or to policies) or external (e.g., related to legal issues or to climate) unstructured data Data that does not have a predetermined format and is stored in the form of textual documents unstructured problem A decision setting where the steps are not entirely fixed or structured, but may require subjective considerations unsupervised learning A method of training artificial neural networks in which only input stimuli are shown to the network, which is self-organizing visual interactive simulation (VIS) A simulation approach used in the decision-making process that shows graphical animation in which systems and processes are presented dynamically to the decision maker It enables visualization of the results of different potential actions visual recognition The addition of some form of computer intelligence and decision making to digitized visual information, received from a machine sensor such as a camera voice of customer (VOC) Applications that focus on “who and how” questions by gathering and reporting direct feedback from site visitors, by benchmarking against other sites and offline channels, and by supporting predictive modeling of future visitor behavior voice (speech) recognition Translation of human voice into individual words and sentences that are understandable by a computer user interface The component of a computer system that allows bidirectional communication between the system and its user Voice over IP (VoIP) Communication systems that transmit voice calls over Internet Protocol (IP)–based networks Also known as Internet telephony user interface management system (UIMS) The DSS component that handles all interaction between users and the system voice portal A Web site, usually a portal, that has an audio interface user-developed MSS An MSS developed by one user or by a few users in one department, including decision makers and professionals (i.e., knowledge workers, e.g., financial analysts, tax analysts, engineers) who build or use computers to solve problems or enhance their productivity utility (on-demand) computing Unlimited computing power and storage capacity that, like electricity, water, and telephone services, can be obtained on demand, used, and reallocated for any application and that are billed on a pay-per-use basis vendor-managed inventory (VMI) The practice of retailers making suppliers responsible for determining when to order and how much to order video teleconferencing (videoconferencing) Virtual meeting in which participants in one location can see participants at other locations on a large screen or a desktop computer virtual (Internet) community A group of people with similar interests who interact with one another using the Internet virtual meeting An online meeting whose members are in different locations, possibly even in different countries virtual team A team whose members are in different places while in a meeting together Z01_SHAR9209_10_PIE_GLOS.indd 676 voice synthesis The technology by which computers convert text to voice (i.e., speak) Web 2.0 The popular term for advanced Internet technology and applications, including blogs, wikis, RSS, and social bookmarking One of the most significant differences between Web 2.0 and the traditional World Wide Web is greater collaboration among Internet users and other users, content providers, and enterprises Web analytics The application of business analytics activities to Web-based processes, including e-commerce Web content mining The extraction of useful information from Web pages Web crawlers An application used to read through the content of a Web site automatically Web mining The discovery and analysis of interesting and useful information from the Web, about the Web, and usually through Web-based tools Web services An architecture that enables assembly of distributed applications from software services and ties them together Web structure mining The development of useful information from the links included in Web documents Web usage mining The extraction of useful information from the data being generated through Web page visits, transactions, and so on 1/25/14 7:17 AM www.downloadslide.net Glossary 677 Weka A popular, free-of-charge, open source suite of machine-learning software written in Java, developed at the University of Waikato what-if analysis A process that involves asking a computer what the effect of changing some of the input data or parameters would be wiki A piece of server software available in a Web site that allows users to freely create and edit Web page content, using any Web browser Z01_SHAR9209_10_PIE_GLOS.indd 677 wikilog A Web log (blog) that allows people to participate as peers; anyone can add, delete, or change content WordNet A popular general-purpose lexicon created at Princeton University work system A system in which humans and/or machines perform a business process, using resources to produce products or services for internal or external customers 1/25/14 7:17 AM www.downloadslide.net index Note: ‘A’, ‘f’, ‘n’ and ‘t’ refer to application cases, figures, notes and tables respectively A Academic providers, 658 Accenture, 607, 608t Accuracy metrics for classification models, 245t, 246t Acxiom, 265 Ad hoc DSS, 93 Agent-based models, 491–492, 493A– 494A Agility support, 641 AI See Artificial intelligence (AI) AIS SIGDS classification (DSS), 91–93 communication driven, 92 compound, 93 data driven, 92 document driven, 92 group, 92 knowledge driven, 92 model driven, 92–93 AJAX, 635 Algorithms analytical technique, 468–469 data mining, 305 decision tree, 452 evolutionary, 307, 471 genetic, 471–476 HITS, 375 kNN algorithm, 307 learning, 228, 290, 291 linear, 302 neighbor, 305–307 popular, 358 proprietary, 382 search, 308 Alter’s DSS classification, 93 Amazon.com analytical decision making, 219–220, 633 apps, for customer support, 99 cloud computing, 637–638 collaborative filtering, 634 SimpleDB at, 640 Web usage, 389 American Airlines, 433A Analytic ecosystem aggregators and distributors, data, 652 data infrastructure providers, 650–651 data warehouse, 651–652 industry clusters, 650 middleware industry, 652 software developers, 652 Analytical techniques, 33, 85, 371, 468 algorithms, 468–469 blind searching, 469 heuristic searching, 469, 469A– 470A See also Big Data analytics Analytic hierarchy process (AHP) application, 453A– 454A alternative ranking, 458f application, 455–459 description, 453–455 diagram, 456f final composite, 459f ranking criteria, 457f subcriteria, 458f Analytics business process restructure, 644 impact in organization, 643 industry analysts and influencers, 657 job satisfaction, 644 legal issues, 646 manager’s activities, impact on, 645–646 mobile user privacy, 647 privacy issues, 647 stress and anxiety, job, 644–645 technology issues, 648–649 user organization, 655 Analytics-as-a-Service (AaaS), 641–642 ANN See Artificial neural network (ANN) Apple, 99, 375 Apriori, 230, 239, 256–257 Area under the ROC curve, 247 Artificial intelligence (AI) advanced analytics, 156 automated decision system, 502, 503 BI systems, 45 data-mining, 220, 222–223 ES feature, symbolic reasoning, 508 field applications, 505–507 genetic algorithm, 471 knowledge-driven DSS, 92, 499 knowledge-based management subsystem, 100 knowledge engineering tools, 517 in natural language processing (NLP), 327 rule-based system, 505, 521 text-analytics, 322 visual interactive models (VIM), 487 Artificial neural network (ANN) application, 294A– 295A architectures, 284–285 backpropagation, 290–291 biological and artificial, 278–280 developing (See Neural network-based systems, developing) elements, 281–284 Hopfield networks, 285–286 Kohonen’s self-organizing feature maps (SOM), 285 learning process, 289–290 neural computing, 277 predictive models, 274 result predictions, 276f simple split, exception to, 245, 246f Artificial neurons, 278–281 Association rule mining, 230, 239, 254–257 Associations in data mining, 227, 230 defined, 230 See also Association rule mining Asynchronous communication, 557 Asynchronous products, 558 Attensity360, 414 Attributes, 248 Auctions, e-commerce network, 390 Audio, acoustic approach, 359 Authoritative pages, 375 Automated decision-making (ADM), 92 Automated decision system (ADS), 92, 501 application, 502A architecture, 502f concept, 501–505 for revenue management system, 504 rule based system, 505 vignette, 500–501 Automated help desks, 513 Automatic programming in AI field, 506f, 528 Automatic sensitivity analysis, 448 Automatic summarization, 330 AWStats (awstats.org), 399 Axons, 278 B Back-error propagation, 290, 291f Backpropagation, 281, 285, 290–291, 291f Backtracking, 234, 523 Backward chaining, 521–523, 523f Bag-of-words used in text mining, 326–327 Balanced scorecards application, 208A– 209A concept, 202 dashboard vs, 204–205 DMAIC performance model, 206 four perspectives, 203–204 meaning of balance, 204 Six Sigma vs, 206 Basic Information, 636 Black box testing by using sensitivity analysis, 292–293 Banking, 231 Bayesian classifiers, 248 Best Buy, 99 BI See Business intelligence (BI) Big Data analytics applications, 580A– 581A, 585A– 586A, 593A– 594A, 598A– 599A, 606A– 607A, 610A– 611A business problems, addressing, 584–586 data scientist’s role in, 595–599 data warehousing and, 599–602 defined, 57, 576–577 fundamentals, 581–584 gray areas, 602 Hadoop, 588–592, 600–604 industry testaments, 609–610 MapReduce, 587–588 NoSQL, 592–594 Stream analytics, 611–618 value proposition, 580 variability, 579 variety, 578–579 velocity, 579 vendors, 604, 608t veracity, 579 volume, 577–578 vignette, 573–576 Bing, 99, 385 678 Z02_SHAR9209_10_PIE_INDX.indd 678 1/25/14 7:15 AM www.downloadslide.net Index 679 Biomedical text mining applications, 334 Blackboard (workplace), 514–516 Black-box syndrome, 293 Blending problem, 445 bluecava.com, 648 Blind searching, 469 Blogs, 350, 407, 552 Bootstrapping, 247 Bounded rationality, 82 Brainstorming, electronic, 87–88, 564 Branch, 248 Bridge, 406–407 Break-even point, goal seeking analysis used to compute, 449 Budgeting, 201 Building, process of, 198 Business activity monitoring (BAM), 87, 115 Business analytics (BA) application, 626A– 627A, 629A, 631A– 632A athletic injuries, 54A by Seattle Children’s Hospital, 51A consumer applications, 630 data science vs, 56 descriptive analytics, 50 for smart energy use, 623 geo-spatial analytics, 624–628 Industrial and Commercial Bank of China’s network, 55A legal and ethical issues, 646 location-based analytics, 624 moneyball in sports and movies, 53A organizations, impact on, 643 overview, 49 recommendation engines, 633–634 speed of thought, 52A vignette, 623–624 See also Analytic ecosystem Business Intelligence (BI) architecture, 45 brief history, 44 definitions, 44 DSS and, 48 multimedia exercise, 46 origin and drivers, 46 Sabre’s dashboard and analytical technique, 47A styles, 45 Business intelligence service provider (BISP), 138 Business Objects, 142 Business Performance Improvement Resource, 156 Business performance management (BPM) application, 199A– 200A closed loop cycle, 197–199 defined, 196 key performance indicator (KPI), 201 measurement of performance, 200–202 Business Pressures–Responses– Support model, 35–37, 36t Business process management (BPM), 87, 88, 99 Business process reengineering (BPR), 86, 644 Business reporting analytic reporting, 652 Z02_SHAR9209_10_PIE_INDX.indd 679 application, 171A– 173A, 174A– 175A, 176A, 179A– 180A, 191A, 193A– 194A, 199A– 200A, 208A– 209A charts and graphs, 180–183 components, 173–174 data visualization, 175, 176A, 177, 177A, 179A, 184–186 definitions and concept, 166–170 vignette, 166 BuzzLogic, 415 C Candidate generation, 256 Capacities, 82 Capital One, 220 Carnegie Mellon University, 631 CART, 249, 258, 259, 274 Case-based reasoning (CBR), 248 Causal loops, 489 Catalog design, 255 Catalyst, data warehousing, 147 Categorical data, 224, 236 Categorization in text mining applications, 323 Cell phones mobile social networking and, 636 mobile user privacy and, 648 Centrality, 406 Centroid, 253 Certainty, decision making under, 431, 432 Certainty factors (CF), 452 Certification agencies, 658 Certified Analytics Professional (CAP), 658 Channel optimization, 47t Chief executive officer (CEO), 88, 657 Chief information officer (CIO), 655, 657 Chief operating officer (COO), 369 Chi-squared automatic interaction detector (CHAID), 249 Choice phase of decision-making process, 85, 88 Chromosome, 232, 473 Cisco, 608t Classification, 231–232 AIS SIGDSS Classification for DSS, 91–92 in data mining, 229, 244–249 in decision support system (DSS), 91 non-linear, 302 N-P Polarity, 356 of problem (intelligent phase), 76 in text mining, 342 Class label, 248 Clementine, 258, 262, 292 Clickstream analysis, 388 Cliques, social circles, 407 Cloud computing, 153, 637–638 Cluster analysis for data mining, 250–251 Clustering in data mining, 230 defined, 230 K-Means, 253 optimal number, 252 in text mining, 323, 342–343 See also Cluster analysis for data mining Clustering coefficient, 407 Clusters, 227 Coca-Cola, 132, 133A Cognitive limits, 40 Cognos, 94, 109, 113 Cohesion, 407 Collaborative networks, 561 Collaborative planning, forecasting, and replenishment (CPFR) See CPFR Collaborative planning along supply chain, 561 Collaborative workflow, 560 Collective Intellect, 414–415 Collective intelligence (CI), 553, 635 Communication networks, 404 Community networks, 404 Community of practice (COP), 551, 568 Complete enumeration, 469 Complexity in simulation, 471 Compound DSS, 92, 93 Comprehensive database in data warehousing process, 119 Computer-based information system (CBIS), 89, 507 Computer hardware and software See Hardware; Software Computerized decision support system decision making and analytics, 35 Gorry and Scott-Morton classical framework, 41–42 reasons for using, 41–43 for semistructured problems, 43 for structured decisions, 42 (See also Automated decision system (ADS)) for unstructured decisions, 43 Computer-supported collaboration tools collaborative networks, 561 collaborative workflow, 560 corporate (enterprise) portals, 556 for decision making (See Computerized decision support system) virtual meeting systems, 559 Voice over IP (VoIP), 483–484 Web 2.0, 560–561 wikis, 561 Computer-supported collaborative work (CSCW), 562 Computer vision, 506f Concepts, defined, 320 Conceptual methodology, 43 Condition-based maintenance, 232 Confidence gap, 483 Confidence metric, 255–256 Confusion matrix, 245, 245f Connection weights, 283 Constraints, 126, 241, 285 Consultation environment used in ES, 514, 514f Continental Airlines, 150, 157–158, 161A Contingency table, 245, 245f Continuous probability distributions, 482t Control system, 528 Converseon, 415 Corporate intranets and extranets, 561 Corporate performance management (CPM), 196 Corporate portals, 559t Corpus, 324, 338–339 CPFR, 561 Creativity, 78 Credibility assessment (deception detection), 332 1/25/14 7:15 AM www.downloadslide.net 680 Index Credit analysis system, 512–513 Crimson Hexagon, 415 CRISP-DM, 234–236, 242–243, 243f, 337 Cross-Industry Standard Process for Data Mining See CRISP-DM Crossover, genetic algorithm, 473 Cross-marketing, 255 Cross-selling, 255 Cross-validation accuracy (CVA), 247 Cube analysis, 45 Customer attrition, 47t, 231 Customer experience management (CEM), 353 Customer profitability, 47t Customer relationship management (CRM), 64, 72, 134t, 219, 231, 328, 331, 343, 350, 359, 513 Customer segmentation, 47t Custom-made DSS system, 93–94 D Dashboards, 190 application, 191A, 193A– 194A vs balanced scorecards, 204 best practices, 195 characterstics, 194 design, 192, 196 guided analytics, 196 information level, 196 key performance indicators, 195 metrics, 195 rank alerts, 195 user comments, 195 validation methods, 195 Data as a service (DaaS), 605, 638–639 Database management system (DBMS), 116, 118t, 122, 367, W3.1.46 architecture, 122 column-oriented, 153 data storage, 174, 551 defined, 95 NoSQL, 639 relational, 113, 154–155 Teradata, 112 Data directory, 95 Data dredging, 222 Data extraction, 119 Data integration application, 128A description, 128 extraction, transformation and load (ETL) processes, 127 Data loading in data warehousing process, 119 Data management subsystem, 95 Data marts, 114 Data migration tools, 123 Data mining applications, 221A– 222A, 226A– 227A, 231–234, 233A– 234A, 251A– 252A, 261A– 264A, 265A– 266A artificial neural network (ANN) for (See Artificial neural network (ANN)) associations used in, 227 as blend of multiple disciplines, 223f in cancer research, 240A– 241A characteristics of, 222–227, 225f classification of tasks used in, 225, 228f, 229, 244 Z02_SHAR9209_10_PIE_INDX.indd 680 clustering used in, 229–230 commercial uses of, 258 concepts and application, 219–222 data extraction capabilities of, 119 data in, 223–224, 223f definitions of, 222 DSS templates provided by, 94 law enforcement’s use of, 226A– 227A methods, 244–257 myths and blunders, 264–267 names associated with, 222 patterns identified by, 228 prediction used in, 223, 228 process, 234–242 recent popularity of, 229 standardized process and methodologies, 242–243 vs statistics, 230–231 software tools, 258–264 term origin, 222 time-series forecasting used in, 230 using predictive analytics tools, 53 vignette, 217–219 visualization used in, 233 working, 227–230 Data modeling vs analytical models, 77n Data organization, 579 Data-oriented DSS, 49 Data preparation, 236–238, 237f, 239t Data processing, 58, 112, 220, 227–228, 513, 591, 653 Data quality, 130–131 Data sources in data warehousing process, 46f Data visualization, 175, 177A, 179A Data warehouse (DW) defined, 111 development, 136–138 drill down in, 141 hosted, 138 See also Data warehousing Data warehouse administrator (DWA), 152 Data Warehouse Institute, 396 Data warehouse vendors, 133, 154 Data warehousing administration, 151 application, 109A, 115A, 118A, 133A, 136A, 145A, 148A architecture, 120–126 characteristics, 113 data analysis, 139 data representation, 138 definition and concept, 111 development, 132–133, 137 future trends, 151, 153 historical perspective, 111 implementation issues, 143 real-time, 147 scalability, 146 security issues, 151 The Data Warehousing Institute (TDWI), 46f, 61, 133, 143, 657 DB2 Information Integrator, 122 Debugging system, 528 Deception detection, 331–333, 332A– 333A, 333f Decisional managerial roles, 38–39 Decision analysis, 450–452 Decision automation system (DAS), 501 Decision makers, 79 Decision making characteristics, 70 disciplines, 71 ethical issues, 649 at HP, 68 implementation phase, 85, 88 phases, 72 style and makers, 71 working definition, 71 Decision making and analytics business pressures–responses–support model, 37 computerized decision support, 35 information systems support, 39 overview, 31 vignette, 33 Decision-making models components of, 96–97 defined, 77 design variables in, 77–78 Kepner-Tregoe method, 74 mathematical (quantitative), 77 Simon’s four-phase model, 72–74, 74t Decision-making process, phases of, 72–74, 74f Decision modeling, using spreadsheets, 68–69 Decision rooms, 564 Decision style, 71–72 Decision support in decision making, 86–89, 86f Decision support system (DSS) AIS SIGDSS, 91 applications, 89–91, 96A, 98A, 100A, 104A, 425A– 426A in business intelligence, 44 capabilities, 89 characteristics and capabilities of, 89–91, 90f classifications of, 91–94 components of, 94–102 custom-made system vs ready-made system, 93 definition and concept of, 43 description of, 89–91 DSS-BI connection, 46 (See also DSS/BI) resources and links, 61 singular and plural of, 32n spreadsheet-based (See Spreadsheets) as umbrella term, 43–44 vendors, products, and demos, 61 See also Computerized decision support system Decision tables, 450–452 Decision trees, 249, 450–452 Decision variables, 228, 429, 430 Deep Blue (chess program), 319 Dell, 134t, 608t, 650 DeltaMaster, 259t Density, in network distribution, 407 DENDRAL, 511 Dendrites, 279 Dependent data mart, 114 Dependent variables, 430 Descriptive models, 80–81 Design phase of decision-making process, 73, 74t, 77–84 alternatives, developing (generating), 82–83 1/25/14 7:15 AM www.downloadslide.net Index 681 decision support for, 87–88 descriptive models used in, 80–81 design variables used in, 77 errors in decision making, 84 normative models used in, 79 outcomes, measuring, 83 principle of choice, selecting, 78 risk, 83 satisficing, 81–82 scenarios, 84 suboptimization approach to, 79–80 Design system, 528 Development environment used in ES, 514 Diagnostic system, 528 Dictionary, 324 Digital cockpits, 45f Dimensional modeling, 138 Dimensional reduction, 238 Dimension tables, 138 Directory, data, 95 Discrete event simulation, 483 Discrete probability distributions, 482t Disseminator, 38t Distance measure entropy, 253 Distance, in network distribution, 407 Disturbance handler, 38t DNA microarray analysis, 334 Document management systems (DMS), 87, 551 Drill down data warehouse, 141 DSS See Decision support system (DSS) DSS/BI defined, 48 hybrid support system, 93 DSS/ES, 102 DSS Resources, 61, 156 Dynamic model, 435 E ECHELON surveillance system, 331 Eclat algorithm, 230, 256 E-commerce site design, 255 Economic order quantity (EOQ), 80 Electroencephalography, 101 Electronic brainstorming, 82, 551 Electronic document management (EDM), 87, 549, 551 Electronic meeting system (EMS), 560, 562, 564 Electronic teleconferencing, 557 Elitism, 473 E-mail, 326, 353, 402 Embedded knowledge, 546 Emotiv, 101 End-user modeling tool, 434 Enterprise application integration (EAI), 129, 641 Enterprise data warehouse (EDW), 115–117, 118t Enterprise information integration (EII), 117, 129, 641 Enterprise information system (EIS), 45, 72, 645 Enterprise Miner, 258, 259, 259t, 260 Enterprise reporting, 45, 170 Enterprise resource management (ERM), 72 Enterprise resource planning (ERP), 72, 343 Enterprise 2.0, 553 Entertainment industry, 233 Z02_SHAR9209_10_PIE_INDX.indd 681 Entity extraction, 323 Entity-relationship diagrams (ERD), 134, 137t Entrepreneur, 38t Entropy, 249 Environmental scanning and analysis, 426 ERoom server, 560 Ethics in decision making and support, 649 Evolutionary algorithm, 471 Executive information system (EIS), 44 Expert, defined, 507 Expert Choice (EC11), 453, 454–455, 560, 653 Expertise, 507 Expertise Transfer System (ETS), 539 Expert system (ES), 58, 110, 111, 112, 135– 136, 138, 572–576 applications of, 510A, 511A, 512–513, 516A– 517A, 531A– 532A vs conventional system, 509t development of, 528–532 expertise and, 508 experts in, 507–508 features of, 508–509 generic categories of, 527t problem areas suitable for, 527–528 rule-based, 512, 513 structure of, 514–515 used in identifying sport to talent, 510A Expert system (ES) shell, 530 Explanation and justification, 518 Explanation facility (or justifier), 526 Explanations, why and how, 526 Explanation subsystem in ES, 516 Explicit knowledge, 545–546 Exsys, 156, 530 Exsys Corvid, 516–517 EXtensible Markup Language (XML), 125–126, 551 External data, 639f Extraction, transformation, and load (ETL), 119, 120f, 127–132 Extraction of data, 130 Extranets, 121, 556 F Facebook, 58, 350, 353, 402, 589 Facilitate, 558 Facilitators (in GDSS), 562, 564 Fair Isaac Business Science, 259t Feedforward-backpropagation paradigm, 281 See also Backpropagation Figurehead, 38t FireStat (firestats.cc), 398 Folksonomies, 560 FootPath, 628 Forecasting (predictive analytics), 426–427 Foreign language reading/writing, 330 Forward chaining, 521, 523 Forward-thinking companies, 400 FP-Growth algorithm, 230, 256 Frame, 81 Fraud detection/prevention, 47t, 654 Fuzzy logic, 253, 399, 524, 551 G Gambling referenda predicted by using ANN, 427A Game playing in AI field, 506f Gartner Group, 44, 657 GATE, 347 General-purpose development environment, 529–530 Genetic algorithms, 248, 288, 471–476 Geographical information system (GIS), 87, 91, 182, 484 Gini index, 249 Goal seeking analysis, 450f Google Docs & Spreadsheets, 637 Google Web Analytics (google.com/ analytics), 398 Gorry and Scott-Morton classical framework, 41–43 Government and defense, 232 GPS, 61, 87, 577 Graphical user interface (GUI), 98A Group decision support system (GDSS) characteristics of, 562 defined, 562 idea generation methods, 562 groupwork improved by, 562–563 limitations of, 563 support activities in, 563 Group support system (GSS), 58, 73, 85, 86, 90, 92, 449, 472–475 collaboration capabilities of, 132, 135 in crime prevention, 457–458 defined, 563 decision rooms, 564 facilities in, 564 Internet/Intranet based system, 564 support activities, 564 GroupSystems, 560, 564 Groupware defined, 557 products and features, 559t tools, 558 GroupSystems, 467, 469 Lotus Notes (IBM collaboration software), 224, 546 Team Expert Choice (EC11), 560 WebEx.com, 559 Groupwork benefits and limitations of, 554 characteristics of, 553 computerized system used to support, 556–558 defined, 553 difficulties associated with, 554t group decision-making process, 554 overview, 556–557 WEB 32.0, 552–553 H Hadoop pros and cons, 590–591 technical components, 589–590 working 588–589 Hardware for data mart, 135 data mining used in, 232 data warehousing, 153 for DSS, 102 Heuristic programming, 465 Heuristics, defined, 469 Heuristic searching, 469 Hewlett-Packard Company (HP), 68–69, 415, 495–496, 605, 608t, 650 1/25/14 7:15 AM www.downloadslide.net 682 Index Hidden layer, 282, 282f, 284 Hive, in data mining, 230 Holdout set, 245 Homeland security, 233, 513, 648 Homonyms, 324 Homophily, 406 Hopfield networks, 285–286 Hosted data warehouse (DW), 138 How explanations, 526–527 HP See Hewlett-Packard Company (HP) Hub, 375 Hybrid approaches to KMS, 547 Hyperion Solutions, 134t Hyperlink-induced topic search (HITS), 375 Hyperplane, 295 I IBM Cognos, 94, 109, 118 DB2, 122 Deep Blue (chess program), 319 Watson’s story, 319–321 ILOG acquisition, 653 InfoSphere Warehouse, 605 Intelligent Miner, 259t Lotus Notes, 224 546, 560 WebSphere portal, 213 Iconic (scale) model, 271 IData Analyzer, 259t Idea generation, 562, 563 Idea generation in GSS process, 562 ID3, 226, 249 Implementation defined, 85 phase of decision-making process, 73, 73ft, 85, 88–89 Independent data mart, 123 Indices represented in TDM, 339–340 Individual DSS, 103 Individual privacy, 648 Inference, 526 Inference engine, 515, 521 Inferencing, 521 backward chaining, 521 combining two or more rules, 521–522 forward chaining, 521 with uncertainty, 523–524 Influence diagrams, 422, 426 Informational managerial roles, 37, 38t Information-as-a-Service (Information on Demand) (IaaS), 641 Information Builders, 156, 166, 169 Information extraction in text mining applications, 323 Information gain, 251–252 Information harvesting, 222 Information overload, 70 Information retrieval, 322 Information system, integrating with KMS, 546 Information technology (IT) in knowledge management, 550–553 Information warfare, 233 Infrastructure as a service (IaaS), 637 Inmon, Bill, 113, 126, 150 Inmon model (EDW approach), 134 Innovation networks, 404–405 Input/output (technology) coefficients, 280, 438 Z02_SHAR9209_10_PIE_INDX.indd 682 Insightful Miner, 259t Instant messaging (IM), 557, 645 Instant video, 559t Institutional DSS, 93 Instruction system, 528 Insurance, 255 Integrated data warehousing, 145 Intelligent phases, decision making application, 75A classification of problems, 76 decomposition of problems, 76 identification of problems, 75 problem ownership, 76 supporting, 86 Intelligent agents (IA) in AI field, 551 Intelligent decision support system (IDSS), 499 Intelligent DSS, 92 Intelligent Miner, 259t Interactive Financial Planning System (IFPS), 97 Intermediate result variables, 429, 430, 431 Internal data, 95 Internet GDSS facilities, 464 non-work-related use of, 649 virtual communities, 407 Interpersonal managerial roles, 37, 38t Interpretation system, 527 Interval data, 225 Intranets, 224, 546, 551 Inverse document frequency, 341 J Jackknifing, 241 Java, 57, 96, 121, 131, 259, 292 K KDD (knowledge discovery in databases), 243 Key performance indicators, 173, 198 K-fold cross-validation, 246–247 Kimball model (data mart approach), 134–135 K-means clustering algorithm, 253 k-nearest neighbor, 305 Knowledge acquisition, 518–519 characteristics of, 544–545 data, information, and, 543, 544f defined, 543 explicit, 545–546 leaky, 545 tacit, 545–546 taxonomy of, 545t Knowledge acquisition in ES, 514–515 Knowledge and inference rules, 521 Knowledge-based decision support system (KBDSS), 499 Knowledge-based DSS, 100 Knowledge-based economy, 544 Knowledge-based modeling, 426, 428 Knowledge-based system (KBS), 518 Knowledge base in ES, 515 Knowledge discovery in databases (KDD), 243, 551 Knowledge discovery in textual databases See Text mining Knowledge elicitation, 518 Knowledge engineer, 515 Knowledge engineering, 517–527 Knowledge extraction, 222 See also Data mining Knowledge extraction methods, 342 Knowledge harvesting tools, 541 Knowledge management consulting firms, 548 Knowledge management system (KMS), 40, 72, 74t, 87–88, 92, 546 approaches to, 546–548 artificial intelligence (AI) in, 551–552, 552t components, 551 concepts and definitions, 543–545 cycle, 550, 550f explicit, 545–546 harvesting process, 541 information technology, role in, 550 knowledge repository in, 548–549 nuggets, 539–541 organizational culture in, 546 organizational learning in, 543 organizational memory in, 543 overview, 542 successes, 541–554 tacit, 545–546 traps, 548 vignette, 538–539 Web 32.0, 552–553 See also Knowledge Knowledge Miner, 259t Knowledge nuggets (KN), 515 Knowledge-refining system in ES, 516 Knowledge repository, 548, 549f Knowledge representation, 518, 520 KnowledgeSeeker, 258 Knowledge-sharing system, 541 Knowledge validation, 518 Kohonen’s self-organizing feature maps (SOM), 285 KXEN (Knowledge eXtraction ENgines), 259t L Language translation, 330 Laptop computers, 255 Latent semantic indexing, 325 Law enforcement, 404 Leader, 38t Leaf node, 183 Leaky knowledge, 545 Learning algorithms, 228, 278, 290, 305 Learning in artificial neural network (ANN), 283–289 algorithms, 290, 291 backpropagation, 290–291, 291f how a network learns, 288–290 learning rate, 288 process of, 289, 289f supervised and unsupervised, 289–290 Learning organization, 581A Learning rate, 288 Leave-one-out, 247 Left-hand side (LHS), 444 Legal issues in business analytics, 646 Liaison, 38t Lift metric, 255 Lindo Systems, Inc., 434, 444, 653 Linear programming (LP) allocation problems, 442f 1/25/14 7:15 AM www.downloadslide.net Index 683 application, 437A decision modeling with spreadsheets, 434 implementation, 444 mathematical programming and, 444 modeling in, example of, 439–442 modeling system, 439–441 product-mix problem, 441f Link analysis, 230 LinkedIn, 410, 560, 634, 636 Linux, 135t Loan application approvals, 231, 282–283 Location-based analytics for organizations, 624–626 Lockheed aircraft, 161 Logistics, 231 Lotus Notes See IBM Lotus Software See IBM M Machine-learning techniques, 304, 307, 358 Machine translation, 330 Management control, 93 Management information system (MIS), 546 Management science (MS), 658 Management support systems (MSS), 71 Managerial performance, 37 Manual methods, 111 Manufacturing, 232 Market-basket analysis See Association rule mining Marketing text mining applications, 323 MARS, 258, 259t Mashups, 552, 560, 634 Massively parallel processing (MPP), 154 Mathematical (quantitative) model, 77 Mathematical programming, 438, 444, 452 Megaputer, 258, 292, 336A PolyAnalyst, 258, 259t, 336A Text Analyst, 347 WebAnalyst, 399 Mental model, 545t Message feature mining, 332 Metadata in data warehousing, 114, 115– 117, 342 Microsoft Enterprise Consortium, 258 Excel, 181–182, 181f, 182f, 260 (See also Spreadsheets) PowerPoint, 60, 158 SharePoint, 552t, 558 SQL Server, 258 Windows, 135t, 630 Windows-based GUI, 563 Windows XP, 399 See also Groove Networks; SQL MicroStrategy Corp., 45 Middleware tools in data warehousing process, 120 Mind-reading platforms, 101 Mintzberg’s 37 managerial roles, 38t MochiBot (mochibot.com), 399A Mobile social networking, 636 Model base, 96 Model base management system (MBMS), 95–96, 97f, 428 Modeling and analysis certainty, uncertainty, and risk, 431–433 decision analysis, 450–452 Z02_SHAR9209_10_PIE_INDX.indd 683 goal seeking analysis, 448–449, 450f management support system (MSS) modeling, 71 mathematical models for decision support, 429–431 mathematical program optimization, 437–445 (See also Linear programming (LP) model base management, 428 multicriteria decision making with pairwise comparisons, 453–459 of multiple goals, 452t problem-solving search methods, 467–470, 468f sensitivity analysis, 448 simulation, 476–483 with spreadsheets, 434A– 435A (See also under Spreadsheets) what-if analysis, 448 See also individual headings Model libraries, 428 Model management subsystem, 95–98 categories of models, 428t components of, 96–97 languages, 96 model base, 96, 97f model base management system (MBMS), 96 model directory, 96 model execution, integration, and command, 96 model integration, 96 modeling language, 96 Models/modeling issues environmental scanning and analysis, 426 forecasting (predictive analytics), 426–427 knowledge-based modeling, 428 model categories, 428 model management, 428 trends in, current, 428–429 variable identification, 426–427 Monitor, 38t Monitoring system, 527 Morphology, 324 MSN, 647 Multicriteria decision making with pairwise comparisons, 453–455 Multidimensional analysis (modeling), 429 Multidimensional cube presentation, 209A Multiple goals, analysis of, 446–447 Multiple goals, defined, 452 Multiplexity, 406 Multiprocessor clusters, 140t Mutuality/reciprocity, 406 Mutation, 473 MySpace, 409 MySQL query response, 601 N Narrative, 81 NASA, 576 Natural language generation, 330 Natural language processing (NLP) in AI field, 551 aggregation technique, 356 bag-of-words interpretation, 326, 327 defined, 322–323 goal of, 322 morphology, 324 QA technologies, 319 text analytics and text mining, 322, 327, 378 sentiment analysis and, 350, 353, 412 social analytics, 403 stop words, 324 Natural language understanding, 330 Negotiator, 38t NEOS Server for Optimization, 428 NET Framework, 96 Network closure, 406 Network information processing, 282 Networks, 285–286 Neural computing, 277, 279 Neural network application, 280A– 281A, 286A– 287A architectures, 284–285 concepts, 277–278 information processing, 282–284 See also Artificial neural network (ANN) Neural network-based systems backpropagation, 290–291 developing, 288–289 implementation of ANN, 289–290 learning algorithm selection, 290 Neurodes, 279 Neurons, 278, 279 Nominal data, 224 Nonvolatile data warehousing, 114 Normative models, 79 Nucleus, 278 Numeric data, 225 O Objective function, 438 Objective function coefficients, 438 Object-oriented databases, 530 Objects, defined, 131 OLAP See Online analytical processing (OLAP) OLTP system, 129, 140, 149, 609 1-of-N pseudo variables, 226 Online advertising, 255 Online analytical processing (OLAP) business analytics, 49 data driven DSS, 92 data Warehouse, 111, 139 data storage, 174 decision making, 39 DSS templates provided by, 94 mathematical (quantitative) models embedded in, 170 middleware tools, 120 multidimensional analysis, 429 vs OLTP, 140 operations, 140–141 Simon’s four phases (decision making), 74t variations, 141–142 Online collaboration, implementation issues for, 419 Online transaction processes See OLTP system Open Web Analytics (openwebanalytics com), 398 Operational control, 42 Operational data store (ODS), 114–115 Operations research (OR), 69 Optical character recognition, 330 Optimal solution, 438 1/25/14 7:15 AM www.downloadslide.net 684 Index Optimization algorithms, 653 in compound DSS, 93 marketing application, 331 nonlinear, 291 in mathematical programming, 437 normative models, 79 model, spreadsheet-based, 435 of online advertising, 255 quadratic modeling, 295 search engine, 384–386 vignette, 423–424 Web Site, 400–402 Oracle Attensity360, 414 Big Data analytics, 605, 608t, 642 Business Intelligence Suite, 129 Data Mining (ODM), 259t Endeca, 185 enterprise performance management (EPM), 196 Hyperion, 113, 258 RDBMS, 122 Orange Data Mining Tool, 259t Ordinal data, 224 Ordinal multiple logistic regression, 225 Organizational culture in KMS, 546 Organizational knowledge base, 100, 515, 548 Organizational learning in KMS, 543 Organizational memory, 543, 563, 564 Organizational performance, 40 Organizational support, 93 orms-today.org, 653 Overall Analysis System for Intelligence Support (OASIS), 331 Overall classifier accuracy, 245 P Piwik (PIWIK.ORG) Prediction method application, 308A– 309A distance metric, 306–307 k-nearest neighbor algorithm (KNN), 305–306 parameter selection, 307–308 Predictive modeling vignette, 274–276 Processing element (PE), 281 Process losses, 554 Procter & Gamble (P&G), 93, 424, 482 Product design, applications for, 513 Production, 80–81, 132, 232, 438, 520 Product life-cycle management (PLM), 87, 115 Product-mix model formulation, 440 Product pricing, 255 Profitability analysis, 200 Project management, 86, 206, 528 Propensity to buy, 47t Propinquity, 406 Q Qualitative data, 236, 400 Quality, information, 43 Quantitative data, 236, 509t Quantitative models, decision theory, 447 Query facility, 95 Query-specific clustering, 343 Question answering in text mining, applications, 330 Z02_SHAR9209_10_PIE_INDX.indd 684 R Radian6/Salesforce Cloud, 414 Radio frequency identification (RFID) Big Data analytics, 577, 582, 592 business process management(BPM), 99 location-based data, 624–625 simulation-based assesment, 484A– 487A tags, 491, 579 VIM in DSS, 484 Rank order, 225, 377, 379, 382 RapidMiner, 259, 260 rapleaf Com., 648 Ratio data, 225 Ready-made DSS system, 94 Reality mining, 60, 628 Revenue management systems, 427A, 504 Search engine application, 383A– 384A, 387A– 388A optimization methods, 384–386 S Sentiment analysis, 349, 351A– 352A, 361A– 362A September 41, 2001, 233A– 234A, 648 Sequence mining, 230 Sequence pattern discovery, 389 Sequential relationship patterns, 227 Serial analysis of gene expression (SAGE), 334 Service-oriented architectures (SOA), 117 Service-Oriented DSS, components, 638, 640t Sharing in collective intelligence (CI), 553, 635 Short message service (SMS), 99, 556 Sigmoid (logical activation) function, 284 Sigmoid transfer function, 284 Simon’s four phases of decision making See Decision-making process, phases of Simple split, 245–246, 246f Simulation, 84, 138, 201–207 advantages of, 479 applications, 476A– 478A characteristics of, 478–479 defined, 476 disadvantages of, 480 examples of, 484A– 487A inadequacies in, conventional, 483 methodology of, 480–481, 480f software, 487–488 types, 481–482, 482t vignette, 466–467 visual interactive models and DSS, 484 visual interactive simulation (VIS), 483–484 Simultaneous goals, 446 Singular-value decomposition (SVD), 325, 341–342 Site Meter (sitemeter.com), 398 SLATES (Web 32.0 acronym), 561 Slice-and-dice analysis, 45 SMS See Short message service (SMS) Snoop (reinvigorate.net), 399 Snowflake schema, 139 Social media application, 409A– 410A definition and concept, 407–408 users, 408 Social media analytics application, 413A best practices, 411–412 concept, 410–411 tools and vendors, 414–415 Social network analysis (SNA), 404 application, 405A– 406A connections, 406 distributions, 406–407 metrics, 405 segmentation, 407 Social networking, online business enterprises, implication on, 636 defined, 636 Twitter, 636 WEB 2.0, 634–635 Social-networking sites, 560 Sociometricsolutions.com, 649 Software in artificial neural networks (ANN), 292 data mining used in, 231 in text mining, 347 used in data mining, 258–261 Software as a service (SaaS), 153, 637 Solution technique libraries, 428, 429 SOM See Kohonen’s self-organizing feature maps (SOM) Speech recognition, 330 Speech synthesis, 330 Spiders, 378 Spiral16, 415 Spreadsheets data storage, 174 for decision tables, 451 for DSS model, 428 as end-user model, 434–435 as ETL tool, 130 of goal seeking analysis, 440 in LP models, 444 management support system (MSS) modeling with model used to create schedules for medical interns, 437A MSS modeling with, 97 in multi dimensional analysis, 429 in prescriptive analytics, 436f simulation packages, 479, 482 in ThinkTank, 560 user interface subsystem, 98 what-if query, 448 SPRINT, 249 SproutSocial, 415 SPSS Clementine, 258, 262A, 292, 399t PASW Modeler, 258 Static models, 484 Statistics data mining vs, 230–231 predictive analytics, conversion, 394–395 Server, 575 Server Data Mining, 259t Starbucks, 415, 645 Star schema, 138–139, 139f State of nature, 450 Static model, 484 Statistica Data Miner, 155, 258, 292 Statistica Text Mining, 347 StatSoft, Inc., 340, 653 Stemming, 324 Stop terms, 340 Stop words, 324 Store design, 255 Story, 187 1/25/14 7:15 AM www.downloadslide.net Index 685 Strategic planning, 543 Stream analytics application, 615A– 616A critical Event Processing, 612–613 cyber security, 616 data stream mining, 613 defined, 611–612 e-Commerce, 614 financial services, 617 government, 617–618 health sciences, 617 law enforcement, 616 versus perpetual analytics, 612 power industry, 616 telecommunications, 614 Structural holes, 407 Structured problems, 42 Structured processes, 42 Subject matter expert (SME), 539 Subject-oriented data warehousing, 111 Suboptimization, 80 Summation function, 283 Summarization in text mining applications, 323 Sun Microsystems, 148, 637 Supervised learning, 289–290 Supply-chain management (SCM), 43, 72, 86f, 88, 750 Support metric, 255–256 Support vector machines (SVM) applications, 296A– 300A vs artificial neural network (ANN), 304–305 formulations, 300–302 Kernel trick, 302–303 non-linear classification, 302 process-based approach, 303–304 Sybase, 134t, 155, 415 Symbiotic intelligence See Collective intelligence (CI) Symmetric kernel function , 236 Synapse, 279, 557 Synchronous communication, 459 Synchronous products, 558–559 Synonyms, 324 Sysomos, 414 System dynamics modeling, 488–490 T Tablet computers, 98, 188 Tacit knowledge, 545–546 Tags, RFID, 491, 561 Tailored turn-key solutions, 531 Team Expert Choice (EC11), 560 Teamwork See Groupwork Technology insight active data warehousing, 150 Ambeo’s auditing solution, 152 ANN Software, 292 on Big Data, 609–610 biological and artificial neural network, compared, 280 business reporting, stories, 186–187 criterion and a constraint, compared 78 data Scientists, 596 data size, 577–578 Gartner, Inc.’s business intelligence platform, 184–185 group process, dysfunctions, 555–556 Z02_SHAR9209_10_PIE_INDX.indd 685 on Hadoop, 591–592 hosted data warehouses, 138 knowledge acquisition difficulties, 519 linear programming, 439 MicroStrategy’s data warehousing, 142–143 PageRank algorithm, 380–381 popular search engine, 385 taxonomy of data, 224–226 text mining lingo, 324–325 textual data sets, 359 Teleconferencing, 557 Teradata Corp., 125, 148f, 149f Teradata University Network (TUN), 46, 62, 157–158 Term-by-document matrix (occurrence matrix), 324–325 Term dictionary, 324 Term-document matrix (TDM), 339–340, 339f Terms, defined, 324 Test set, 245 Text categorization, 342 Text data mining See Text mining Text mining academic application, 335 applications, 325A– 326A, 328A– 329A, 330–337, 344A– 346A biomedical application, 334–335 bag-of-words used in, 326 commercial software tools, 347 concepts and definitions, 321–325 corpus establishment, 338–339 free software tools, 347 Knowledge extraction, 342–346 marketing applications, 331 natural language processing (NLP) in, 326–330 for patent analysis, 325A– 326A process, 337–347 research literature survey with, 344A– 346A security application, 331–333 term document matrix, 339–342 three-step process, 337–338, 338f tools, 347–349 Text proofing, 330 Text-to-speech, 330 Theory of certainty factors, 524 ThinkTank, 560 Three-tier architecture, 120, 121, 122 Threshold value, 284 Tie strength, 407 Time compression, 479 Time-dependent simulation, 482 Time-independent simulation, 482 Time/place framework, 557–558 Time pressure, 70 Time-series forecasting, 230 Time variant (time series) data warehousing, 114 Tokenizing, 324 Topic tracking in text mining applications, 323 Topologies, 282 Toyota, 415 Training set, 245 Transaction-processing system (TPS), 144 Transformation (transfer) function, 284 Travel industry, 232 Trend analysis in text mining, 343 Trial-and-error sensitivity analysis, 448 Tribes, 187 Turing test, 507 Twitter, 369, 408, 411, 635 Two-tier architecture, 121 U Uncertainty, decision making under, 431, 432, 433A, 451 Uncontrollable variables, 430, 451 U.S Department of Homeland Security (DHS), 233A– 234A, 376, 648 Unstructured data (vs structured data), 324 Unstructured decisions, 42 Unstructured problems, 42, 43 Unstructured processes, 42 Unstructured text data, 331 Unsupervised learning, 228, 244 USA PATRIOT Act, 233A, 647 User-centered design (UCD), 560 User interface in ES, 515 subsystem, 98–99 Utility theory, 447 V Variables decision, 430, 438, 451 dependent, 430 identification of, 426 intermediate result, 431 result (outcome), 430, 451 uncontrollable, 430, 451 Videogames, 567 Video-sharing sites, 560 Virtual communities, 634–635 Virtual meeting system, 559 Virtual reality, 81, 179, 484, 630 VisSim (Visual Solutions, Inc.), 491 Visual analytics high-powered environment, 188–189 story structure, 187 Visual inactive modeling (VIM), 483–484 Visual interactive models and DSS, 484 Visual interactive problem solving, 483 Visual interactive simulation (VIS), 483–484, 484A– 487A Visualization, 44, 175–180, 184 Visual recognition, 308A– 309A Visual simulation, 483 Vivisimo/Clusty, 347 Vodafone New Zealand Ltd., 36 Voice input (speech recognition), 99 Voice of customer (VOC), 396, 401, 402–403 Voice over IP (VoIP), 558 Voice recognition, 295 W Walmart, 40, 99, 146, 254, 415, 643 Web analytics application, 390A– 392A conversion statistics, 394–395 knowledge extraction, 389A maturity model, 396–398 metrics, 392 usability, 392–393 technologies, 389–390 tools, 398–400 1/25/14 7:15 AM www.downloadslide.net 686 Index Web analytics (Continued) traffic sources, 393–394 vignette, 369–371 visitor profiles, 394 Web-based data warehousing, 121, 122f Web conferencing, 558 Web content mining, 374–376, 376A– Web crawlers, 374, 378 WebEx.com, 559, 564 Web-HIPRE application, 455 Webhousing, 145 Webinars, 559 Web mining, 371–373 Web site optimization ecosystem, 400–402, 400f, 401f Web structure mining, 374–376 Webtrends, 415 Z02_SHAR9209_10_PIE_INDX.indd 686 Web 2.0, 552–553, 560–561, 634–635 characteristics of, 635 defined, 552–553 features/techniques in, 560–561 used in online social networking, 634–635 Web usage mining, 371–373, 388–389 What-if analysis, 448, 449f Why explanations, 526 Weka, 258 WiFi hotspot access points, 628 Wikilog, 558, 559, 561 Wikipedia, 408, 491, 561, 635, 637 Wikis, 407, 552–553, 560, 561, 574, 645 Woopra (wopra.com), 399 Word counting, 327 Word frequency, 324 WordStat analysis, 347 Workflow, 560, 574, 590 wsj.com/wtk, 648 X XCON, 511, 512, 512t XLMiner, 259t XML Miner, 399t xplusone.com, 648 Y Yahoo!, 99, 292, 375, 381, 398, 588, 637, 647 Yahoo! Web Analytics (web.analytics.yahoo com), 398 Yield management, 232, 503 YouTube, 46, 408, 410, 560, 578, 627, 635 1/25/14 7:15 AM www.downloadslide.net Z02_SHAR9209_10_PIE_INDX.indd 687 1/25/14 7:15 AM www.downloadslide.net Z02_SHAR9209_10_PIE_INDX.indd 688 1/25/14 7:15 AM ... Direct Computerized Support for Decision Making: From Group Decision Support Systems to Group Support Systems 562 Group Decision Support Systems (GDSS) 562 Group Support Systems 563 How GDSS... United States edition, entitled Business Intelligence and Analytics: Systems for Decision Support, 10th edition, ISBN 978-0-133-05090-5, by Ramesh Sharda, Dursun Delen, and Efraim Turban, published... 29 Part I Decision Making and Analytics: An Overview 31 Chapter Chapter An Overview of Business Intelligence, Analytics, and Decision Support 32 Foundations and Technologies for Decision Making