Preface |
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xix | |
About the Authors |
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xxv | |
Chapter 1 An Overview of Business Intelligence, Analytics, and Data Science |
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3 | (50) |
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1.1 Opening Vignette: Sports Analytics-An Exciting Frontier for Learning and Understanding Applications of Analytics |
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4 | (7) |
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1.2 Changing Business Environments and Evolving Needs for Decision Support and Analytics |
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11 | (2) |
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1.3 Evolution of Computerized Decision Support to Analytics/Data Science |
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13 | (2) |
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1.4 A Framework for Business Intelligence |
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15 | (7) |
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16 | (1) |
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16 | (1) |
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16 | (1) |
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The Origins and Drivers of BI |
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16 | (3) |
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Application Case 1.1: Sabre Helps Its Clients through Dashboards and Analytics |
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18 | (1) |
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A Multimedia Exercise in Business Intelligence |
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19 | (1) |
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Transaction Processing versus Analytic Processing |
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19 | (1) |
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Appropriate Planning and Alignment with the Business Strategy |
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20 | (1) |
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Real-Time, On-Demand BI Is Attainable |
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21 | (1) |
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Developing or Acquiring BI Systems |
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21 | (1) |
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Justification and Cost-Benefit Analysis |
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22 | (1) |
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Security and Protection of Privacy |
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22 | (1) |
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Integration of Systems and Applications |
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22 | (1) |
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22 | (7) |
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24 | (1) |
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Application Case 1.2: Silvaris Increases Business with Visual Analysis and Real-Time Reporting Capabilities |
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24 | (1) |
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Application Case 1.3: Siemens Reduces Cost with the Use of Data Visualization |
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25 | (1) |
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25 | (1) |
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Application Case 1.4: Analyzing Athletic Injuries |
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26 | (1) |
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26 | (1) |
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Analytics Applied to Different Domains |
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27 | (1) |
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Application Case 1.5: A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates |
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27 | (1) |
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Analytics or Data Science? |
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28 | (1) |
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1.6 Analytics Examples in Selected Domains |
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29 | (6) |
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Analytics Applications in Healthcare-Humana Examples |
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29 | (4) |
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Analytics in the Retail Value Chain |
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33 | (2) |
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1.7 A Brief Introduction to Big Data Analytics |
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35 | (2) |
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35 | (2) |
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Application Case 1.6: CenterPoint Energy Uses Real-Time Big Data Analytics to Improve Customer Service |
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37 | (1) |
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1.8 An Overview of the Analytics Ecosystem |
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37 | (9) |
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Data Generation Infrastructure Providers |
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39 | (1) |
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Data Management Infrastructure Providers |
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39 | (1) |
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40 | (1) |
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40 | (1) |
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40 | (1) |
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Analytics-Focused Software Developers |
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41 | (1) |
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Application Developers: Industry Specific or General |
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42 | (1) |
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Analytics Industry Analysts and Influencers |
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43 | (1) |
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Academic Institutions and Certification Agencies |
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44 | (1) |
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Regulators and Policy Makers |
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45 | (1) |
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Analytics User Organizations |
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45 | (1) |
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46 | (1) |
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1.10 Resources, Links, and the Teradata University Network Connection |
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47 | (6) |
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47 | (1) |
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Vendors, Products, and Demos |
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48 | (1) |
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48 | (1) |
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The Teradata University Network Connection |
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48 | (1) |
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48 | (26) |
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49 | (1) |
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49 | (1) |
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49 | (1) |
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50 | (1) |
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51 | (2) |
Chapter 2 Descriptive Analytics I: Nature of Data, Statistical Modeling, and Visualization |
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53 | (74) |
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2.1 Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing |
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54 | (3) |
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57 | (4) |
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2.3 A Simple Taxonomy of Data |
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61 | (4) |
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Application Case 2.1: Medical Device Company Ensures Product Quality While Saving Money |
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63 | (2) |
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2.4 The Art and Science of Data Preprocessing |
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65 | (9) |
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Application Case 2.2: Improving Student Retention with Data-Driven Analytics |
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68 | (6) |
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2.5 Statistical Modeling for Business Analytics |
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74 | (12) |
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Descriptive Statistics for Descriptive Analytics |
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75 | (1) |
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Measures of Centrality Tendency (May Also Be Called Measures of Location or Centrality) |
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76 | (1) |
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76 | (1) |
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77 | (1) |
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77 | (1) |
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Measures of Dispersion (May Also Be Called Measures of Spread Decentrality) |
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77 | (1) |
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78 | (1) |
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78 | (1) |
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78 | (1) |
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78 | (1) |
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Quartiles and Interquartile Range |
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78 | (1) |
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79 | (1) |
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The Shape of a Distribution |
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80 | (6) |
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Application Case 2.3: Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and Detect Problems |
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84 | (2) |
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2.6 Regression Modeling for Inferential Statistics |
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86 | (12) |
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How Do We Develop the Linear Regression Model? |
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87 | (1) |
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How Do We Know If the Model Is Good Enough? |
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88 | (1) |
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What Are the Most Important Assumptions in Linear Regression? |
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89 | (1) |
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90 | (6) |
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Application Case 2.4: Predicting NCAA Bowl Game Outcomes |
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91 | (5) |
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96 | (2) |
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98 | (3) |
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Application Case 2.5: Flood of Paper Ends at FEMA |
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100 | (1) |
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101 | (5) |
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A Brief History of Data Visualization |
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101 | (5) |
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Application Case 2.6: Macfarlan Smith Improves Operational Performance Insight with Tableau Online |
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103 | (3) |
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2.9 Different Types of Charts and Graphs |
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106 | (4) |
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106 | (1) |
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Specialized Charts and Graphs |
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107 | (1) |
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Which Chart or Graph Should You Use? |
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108 | (2) |
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2.10 The Emergence of Visual Analytics |
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110 | (7) |
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112 | (1) |
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High-Powered Visual Analytics Environments |
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112 | (5) |
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2.11 Information Dashboards |
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117 | (10) |
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Application Case 2.7: Dallas Cowboys Score Big with Tableau and Teknion |
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118 | (1) |
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119 | (2) |
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Application Case 2.8: Visual Analytics Helps Energy Supplier Make Better Connections |
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119 | (2) |
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What to Look for in a Dashboard |
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121 | (1) |
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Best Practices in Dashboard Design |
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121 | (1) |
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Benchmark Key Performance Indicators with Industry Standards |
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121 | (1) |
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Wrap the Dashboard Metrics with Contextual Metadata |
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121 | (1) |
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Validate the Dashboard Design by a Usability Specialist |
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122 | (1) |
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Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard |
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122 | (1) |
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Enrich Dashboard with Business-User Comments |
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122 | (1) |
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Present Information in Three Different Levels |
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122 | (1) |
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Pick the Right Visual Construct Using Dashboard Design Principles |
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122 | (1) |
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Provide for Guided Analytics |
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122 | (1) |
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123 | (1) |
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123 | (1) |
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124 | (1) |
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124 | (2) |
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126 | (1) |
Chapter 3 Descriptive Analytics II: Business Intelligence and Data Warehousing |
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127 | (62) |
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3.1 Opening Vignette: Targeting Tax Fraud with Business Intelligence and Data Warehousing |
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128 | (2) |
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3.2 Business Intelligence and Data Warehousing |
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130 | (7) |
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What Is a Data Warehouse? |
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131 | (1) |
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A Historical Perspective to Data Warehousing |
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132 | (1) |
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Characteristics of Data Warehousing |
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133 | (1) |
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134 | (1) |
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135 | (1) |
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Enterprise Data Warehouses (EDW) |
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135 | (1) |
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135 | (4) |
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Application Case 3.1: A Better Data Plan: Well-Established TELCOs Leverage Data Warehousing and Analytics to Stay on Top in a Competitive Industry |
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135 | (2) |
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3.3 Data Warehousing Process |
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137 | (2) |
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3.4 Data Warehousing Architectures |
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139 | (6) |
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Alternative Data Warehousing Architectures |
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142 | (2) |
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Which Architecture Is the Best? |
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144 | (1) |
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3.5 Data Integration and the Extraction, Transformation, and Load (ETL) Processes |
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145 | (5) |
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146 | (2) |
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Application Case 3.2: BP Lubricants Achieves BIGS Success |
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146 | (2) |
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Extraction, Transformation, and Load |
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148 | (2) |
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3.6 Data Warehouse Development |
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150 | (10) |
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Application Case 3.3: Use of Teradata Analytics for SAP Solutions Accelerates Big Data Delivery |
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151 | (2) |
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Data Warehouse Development Approaches |
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153 | (3) |
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Additional Data Warehouse Development Considerations |
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156 | (1) |
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Representation of Data in Data Warehouse |
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156 | (2) |
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Analysis of Data in Data Warehouse |
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158 | (1) |
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158 | (1) |
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159 | (1) |
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3.7 Data Warehousing Implementation Issues |
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160 | (4) |
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Massive Data Warehouses and Scalability |
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162 | (2) |
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Application Case 3.4: EDW Helps Connect State Agencies in Michigan |
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163 | (1) |
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3.8 Data Warehouse Administration, Security Issues, and Future Trends |
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164 | (6) |
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The Future of Data Warehousing |
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165 | (5) |
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3.9 Business Performance Management |
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170 | (5) |
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171 | (4) |
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Application Case 3.5: AARP Transforms Its BI Infrastructure and Achieves a 347% ROI in Three Years |
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173 | (2) |
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3.10 Performance Measurement |
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175 | (2) |
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Key Performance Indicator (KPI) |
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175 | (1) |
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Performance Measurement System |
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176 | (1) |
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177 | (2) |
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177 | (2) |
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The Meaning of Balance in BSC |
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179 | (1) |
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3.12 Six Sigma as a Performance Measurement System |
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179 | (10) |
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The DMAIC Performance Model |
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180 | (1) |
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Balanced Scorecard versus Six Sigma |
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180 | (1) |
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Effective Performance Measurement |
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181 | (12) |
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Application Case 3.6: Expedia.com's Customer Satisfaction Scorecard |
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182 | (1) |
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183 | (1) |
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184 | (1) |
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184 | (1) |
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185 | (2) |
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187 | (2) |
Chapter 4 Predictive Analytics I: Data Mining Process, Methods, and Algorithms |
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189 | (58) |
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4.1 Opening Vignette: Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime |
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190 | (3) |
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4.2 Data Mining Concepts and Applications |
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193 | (10) |
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Application Case 4.1: Visa Is Enhancing the Customer Experience While Reducing Fraud with Predictive Analytics and Data Mining |
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194 | (2) |
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Definitions, Characteristics, and Benefits |
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196 | (1) |
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197 | (6) |
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Application Case 4.2: Dell Is Staying Agile and Effective with Analytics in the 2Ist Century |
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198 | (5) |
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Data Mining versus Statistics |
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203 | (1) |
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4.3 Data Mining Applications |
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203 | (3) |
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Application Case 4.3: Predictive Analytic and Data Mining Help Stop Terrorist Funding |
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205 | (1) |
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206 | (9) |
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Step I: Business Understanding |
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207 | (1) |
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Step 2: Data Understanding |
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208 | (1) |
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208 | (1) |
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209 | (3) |
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Application Case 4.4: Data Mining Helps in Cancer Research |
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209 | (3) |
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Step 5: Testing and Evaluation |
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212 | (1) |
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212 | (1) |
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Other Data Mining Standardized Processes and Methodologies |
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212 | (3) |
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215 | (16) |
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215 | (1) |
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Estimating the True Accuracy of Classification Models |
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216 | (9) |
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Application Case 4.5: Influence Health Uses Advanced Predictive Analytics to Focus on the Factors That Really Influence People's Healthcare Decisions |
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223 | (2) |
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Cluster Analysis for Data Mining |
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225 | (2) |
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227 | (4) |
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4.6 Data Mining Software Tools |
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231 | (6) |
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Application Case 4.6: Data Mining Goes to Hollywood: Predicting Financial Success of Movies |
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233 | (4) |
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4.7 Data Mining Privacy Issues, Myths, and Blunders |
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237 | (10) |
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Application Case 4.7: Predicting Customer Buying Patterns-The Target Story |
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238 | (1) |
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Data Mining Myths and Blunders |
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238 | (23) |
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241 | (1) |
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242 | (1) |
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242 | (1) |
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243 | (2) |
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245 | (2) |
Chapter 5 Predictive Analytics II: Text, Web, and Social Media Analytics |
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247 | (72) |
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5.1 Opening Vignette: Machine versus Men on Jeopardy!: The Story of Watson |
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248 | (3) |
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5.2 Text Analytics and Text Mining Overview |
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251 | (4) |
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Application Case 5.1: Insurance Group Strengthens Risk Management with Text Mining Solution |
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254 | (1) |
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5.3 Natural Language Processing (NLP) |
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255 | (6) |
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Application Case 5.2: AMC Networks Is Using Analytics to Capture New Viewers, Predict Ratings, and Add Value for Advertisers in a Multichannel World |
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257 | (4) |
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5.4 Text Mining Applications |
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261 | (7) |
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261 | (1) |
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261 | (3) |
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Application Case 5.3: Mining for Lies |
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262 | (2) |
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264 | (2) |
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266 | (2) |
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Application Case 5.4: Bringing the Customer into the Quality Equation: Lenovo Uses Analytics to Rethink Its Redesign |
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266 | (2) |
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268 | (8) |
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Task 1: Establish the Corpus |
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269 | (1) |
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Task 2: Create the Term-Document Matrix |
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269 | (2) |
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Task 3: Extract the Knowledge |
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271 | (5) |
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Application Case 5.5: Research Literature Survey with Text Mining |
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273 | (3) |
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276 | (11) |
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Application Case 5.6: Creating a Unique Digital Experience to Capture the Moments That Matter at Wimbledon |
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277 | (3) |
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Sentiment Analysis Applications |
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280 | (2) |
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Sentiment Analysis Process |
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282 | (2) |
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Methods for Polarity Identification |
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284 | (1) |
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284 | (1) |
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Using a Collection of Training Documents |
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285 | (1) |
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Identifying Semantic Orientation of Sentences and Phrases |
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286 | (1) |
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Identifying Semantic Orientation of Documents |
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286 | (1) |
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287 | (4) |
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Web Content and Web Structure Mining |
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289 | (2) |
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291 | (7) |
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Anatomy of a Search Engine |
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292 | (2) |
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292 | (1) |
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293 | (1) |
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Search Engine Optimization |
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294 | (1) |
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Methods for Search Engine Optimization |
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295 | (3) |
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Application Case 5.7: Understanding Why Customers Abandon Shopping Carts Results in a $10 Million Sales Increase |
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297 | (1) |
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5.9 Web Usage Mining (Web Analytics) |
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298 | (6) |
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Web Analytics Technologies |
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299 | (1) |
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300 | (1) |
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300 | (1) |
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301 | (1) |
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302 | (1) |
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302 | (2) |
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304 | (15) |
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304 | (1) |
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Social Network Analysis Metrics |
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305 | (3) |
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Application Case 5.8: Tito's Vodka Establishes Brand Loyalty with an Authentic Social Strategy |
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305 | (3) |
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308 | (1) |
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308 | (1) |
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309 | (1) |
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309 | (1) |
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How Do People Use Social Media? |
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310 | (1) |
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Measuring the Social Media Impact |
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311 | (1) |
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Best Practices in Social Media Analytics |
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311 | (11) |
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313 | (1) |
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314 | (1) |
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315 | (1) |
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315 | (1) |
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316 | (3) |
Chapter 6 Prescriptive Analytics: Optimization and Simulation |
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319 | (50) |
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6.1 Opening Vignette: School District of Philadelphia Uses Prescriptive Analytics to Find Optimal Solution for Awarding Bus Route Contracts |
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320 | (2) |
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6.2 Model-Based Decision Making |
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322 | (6) |
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Prescriptive Analytics Model Examples |
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322 | (2) |
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Application Case 6.1: Optimal Transport for ExxonMobil Downstream through a DSS |
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323 | (1) |
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Identification of the Problem and Environmental Analysis |
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324 | (1) |
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324 | (4) |
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Application Case 6.2: Ingram Micro Uses Business Intelligence Applications to Make Pricing Decisions |
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325 | (3) |
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6.3 Structure of Mathematical Models for Decision Support |
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328 | (2) |
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The Components of Decision Support Mathematical Models |
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328 | (1) |
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The Structure of Mathematical Models |
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329 | (1) |
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6.4 Certainty, Uncertainty, and Risk |
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330 | (1) |
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Decision Making under Certainty |
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330 | (1) |
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Decision Making under Uncertainty |
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331 | (1) |
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Decision Making under Risk (Risk Analysis) |
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331 | (1) |
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6.5 Decision Modeling with Spreadsheets |
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331 | (5) |
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Application Case 6.3: American Airlines Uses Should-Cost Modeling to Assess the Uncertainty of Bids for Shipment Routes |
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332 | (1) |
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Application Case 6.4: Pennsylvania Adoption Exchange Uses Spreadsheet Model to Better Match Children with Families |
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333 | (1) |
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Application Case 6.5: Metro Meals on Wheels Treasure Valley Uses Excel to Find Optimal Delivery Routes |
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334 | (2) |
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6.6 Mathematical Programming Optimization |
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336 | (10) |
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Application Case 6.6: Mixed-Integer Programming Model Helps the University of Tennessee Medical Center with Scheduling Physicians |
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337 | (1) |
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338 | (1) |
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Modeling in LP: An Example |
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339 | (5) |
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344 | (2) |
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6.7 Multiple Goals, Sensitivity Analysis, What-If Analysis, and Goal Seeking |
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346 | (3) |
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346 | (1) |
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347 | (1) |
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348 | (1) |
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348 | (1) |
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6.8 Decision Analysis with Decision Tables and Decision Trees |
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349 | (3) |
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350 | (1) |
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351 | (1) |
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6.9 Introduction to Simulation |
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352 | (7) |
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Major Characteristics of Simulation |
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352 | (2) |
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Application Case 6.7: Simulating Effects of Hepatitis B Interventions |
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353 | (1) |
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354 | (1) |
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Disadvantages of Simulation |
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355 | (1) |
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The Methodology of Simulation |
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355 | (1) |
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356 | (1) |
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357 | (1) |
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Discrete Event Simulation |
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358 | (1) |
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Application Case 6.8: Cosan Improves Its Renewable Energy Supply Chain Using Simulation |
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358 | (1) |
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6.10 Visual Interactive Simulation |
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359 | (10) |
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Conventional Simulation Inadequacies |
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359 | (1) |
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Visual Interactive Simulation |
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359 | (1) |
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Visual Interactive Models and DSS |
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360 | (1) |
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360 | (13) |
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Application Case 6.9: Improving Job-Shop Scheduling Decisions through RFID: A Simulation-Based Assessment |
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361 | (3) |
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364 | (1) |
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364 | (1) |
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365 | (1) |
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365 | (2) |
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367 | (2) |
Chapter 7 Big Data Concepts and Tools |
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369 | (48) |
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7.1 Opening Vignette: Analyzing Customer Churn in a Telecom Company Using Big Data Methods |
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370 | (3) |
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7.2 Definition of Big Data |
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373 | (5) |
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The "V"s That Define Big Data |
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374 | (4) |
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Application Case 7.1: Alternative Data for Market Analysis or Forecasts |
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377 | (1) |
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7.3 Fundamentals of Big Data Analytics |
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378 | (5) |
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Business Problems Addressed by Big Data Analytics |
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381 | (2) |
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Application Case 7.2: Top Five Investment Bank Achieves Single Source of the Truth |
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382 | (1) |
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7.4 Big Data Technologies |
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383 | (10) |
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383 | (2) |
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385 | (1) |
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385 | (1) |
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385 | (1) |
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Hadoop Technical Components |
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386 | (1) |
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Hadoop: The Pros and Cons |
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387 | (2) |
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389 | (4) |
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Application Case 7.3: eBay's Big Data Solution |
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390 | (2) |
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Application Case 7.4: Understanding Quality and Reliability of Healthcare Support Information on Twitter |
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392 | (1) |
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7.5 Big Data and Data Warehousing |
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393 | (4) |
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393 | (1) |
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Use Cases for Data Warehousing |
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394 | (1) |
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The Gray Areas (Any One of the Two Would Do the Job) |
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395 | (1) |
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Coexistence of Hadoop and Data Warehouse |
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396 | (1) |
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7.6 Big Data Vendors and Platforms |
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397 | (9) |
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IBM InfoSphere Biglnsights |
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398 | (3) |
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Application Case 7.5: Using Social Media for Nowcasting the Flu Activity |
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400 | (1) |
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401 | (5) |
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Application Case 7.6: Analyzing Disease Patterns from an Electronic Medical Records Data Warehouse |
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402 | (4) |
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7.7 Big Data and Stream Analytics |
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406 | (3) |
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Stream Analytics versus Perpetual Analytics |
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408 | (1) |
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Critical Event Processing |
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408 | (1) |
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408 | (1) |
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7.8 Applications of Stream Analytics |
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409 | (8) |
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409 | (1) |
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409 | (2) |
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Application Case 7.7: Salesforce Is Using Streaming Data to Enhance Customer Value |
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410 | (1) |
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Law Enforcement and Cybersecurity |
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411 | (1) |
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411 | (1) |
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411 | (1) |
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411 | (1) |
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412 | (7) |
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412 | (1) |
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413 | (1) |
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413 | (1) |
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413 | (1) |
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414 | (3) |
Chapter 8 Future Trends, Privacy and Managerial Considerations in Analytics |
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417 | (50) |
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8.1 Opening Vignette: Analysis of Sensor Data Helps Siemens Avoid Train Failures |
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418 | (1) |
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419 | (10) |
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Application Case 8.1: SilverHook Powerboats Uses Real-Time Data Analysis to Inform Racers and Fans |
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420 | (1) |
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Application Case 8.2: Rockwell Automation Monitors Expensive Oil and Gas Exploration Assets |
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421 | (1) |
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loT Technology Infrastructure |
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422 | (1) |
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422 | (3) |
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425 | (1) |
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426 | (1) |
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Application Case 8.3: Pitney Bowes Collaborates with General Electric loT Platform to Optimize Production |
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426 | (1) |
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427 | (1) |
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Managerial Considerations in the Internet of Things |
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428 | (1) |
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8.3 Cloud Computing and Business Analytics |
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429 | (12) |
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431 | (1) |
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Software as a Service (SaaS) |
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432 | (1) |
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Platform as a Service (PaaS) |
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432 | (1) |
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Infrastructure as a Service (laaS) |
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432 | (1) |
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Essential Technologies for Cloud Computing |
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433 | (1) |
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433 | (1) |
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Major Cloud Platform Providers in Analytics |
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434 | (1) |
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Analytics as a Service (AaaS) |
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435 | (1) |
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Representative Analytics as a Service Offerings |
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435 | (1) |
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Illustrative Analytics Applications Employing the Cloud Infrastructure |
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436 | (5) |
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MD Anderson Cancer Center Utilizes Cognitive Computing Capabilities of IBM Watson to Give Better Treatment to Cancer Patients |
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436 | (1) |
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Public School Education in Tacoma, Washington, Uses Microsoft Azure Machine Learning to Predict School Dropouts |
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437 | (1) |
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Dartmouth-Hitchcock Medical Center Provides Personalized Proactive Healthcare Using Microsoft Cortana Analytics Suite |
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438 | (1) |
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Mankind Pharma Uses IBM Cloud Infrastructure to Reduce Application Implementation Time by 98% |
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|
438 | (1) |
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Gulf Air Uses Big Data to Get Deeper Customer Insight |
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439 | (1) |
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Chime Enhances Customer Experience Using Snowflake |
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|
440 | (1) |
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8.4 Location-Based Analytics for Organizations |
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441 | (7) |
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441 | (4) |
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Application Case 8.4: Great Clips Employs Spatial Analytics to Shave Time in Location Decisions |
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|
443 | (1) |
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Application Case 8.5: Starbucks Exploits GIS and Analytics to Grow Worldwide |
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|
444 | (1) |
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Real-Time Location Intelligence |
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|
445 | (1) |
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Application Case 8.6: Quiznos Targets Customers for Its Sandwiches |
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446 | (1) |
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Analytics Applications for Consumers |
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446 | (2) |
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8.5 Issues of Legality, Privacy, and Ethics |
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|
448 | (5) |
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448 | (1) |
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449 | (1) |
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Collecting Information about Individuals |
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449 | (1) |
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|
450 | (1) |
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Homeland Security and Individual Privacy |
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|
450 | (1) |
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Recent Technology Issues in Privacy and Analytics |
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|
451 | (1) |
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Who Owns Our Private Data? |
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452 | (1) |
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Ethics in Decision Making and Support |
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452 | (1) |
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8.6 Impacts of Analytics in Organizations: An Overview |
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|
453 | (6) |
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454 | (1) |
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Redesign of an Organization through the Use of Analytics |
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|
455 | (1) |
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Analytics Impact on Managers' Activities, Performance, and Job Satisfaction |
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|
455 | (1) |
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456 | (1) |
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Automation's Impact on Jobs |
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|
457 | (1) |
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Unintended Effects of Analytics |
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|
458 | (1) |
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8.7 Data Scientist as a Profession |
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|
459 | (8) |
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Where Do Data Scientists Come From? |
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|
459 | (8) |
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462 | (1) |
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463 | (1) |
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|
463 | (1) |
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|
463 | (1) |
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|
464 | (3) |
Glossary |
|
467 | (8) |
Index |
|
475 | |