Preface |
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xi | |
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xiii | |
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xxxi | |
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xxxiii | |
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1 | (16) |
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1.1 Tasks, Hypotheses, and Human Observers |
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3 | (4) |
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7 | (4) |
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1.3 Dynamic Visual Analytics |
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11 | (6) |
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17 | (58) |
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19 | (8) |
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2.2 Historical Background |
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27 | (11) |
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2.2.1 Early Forms of Visualizations |
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28 | (2) |
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2.2.2 The Age of Cartographic Maps |
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30 | (2) |
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2.2.3 Visualization During Industrialization |
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32 | (2) |
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2.2.4 After the Invention of the Computer |
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34 | (2) |
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2.2.5 Visualization Today |
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36 | (2) |
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2.3 Data Types and Visual Encodings |
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38 | (15) |
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39 | (3) |
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42 | (6) |
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48 | (2) |
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50 | (2) |
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52 | (1) |
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2.4 Interaction Techniques |
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53 | (9) |
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2.4.1 Interaction Categories |
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54 | (4) |
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58 | (3) |
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61 | (1) |
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62 | (13) |
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2.5.1 Visual Enhancements and Decorations |
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63 | (2) |
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2.5.2 Visual Structuring and Organization |
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65 | (1) |
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2.5.3 General Design Flaws |
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66 | (2) |
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68 | (3) |
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71 | (4) |
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75 | (50) |
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77 | (14) |
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3.1.1 Origin and First Stages |
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78 | (1) |
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3.1.2 Data Handling and Management |
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79 | (7) |
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3.1.3 System Ingredients Around the Data |
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86 | (2) |
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3.1.4 Involved Research Fields and Future Perspectives |
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88 | (3) |
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3.2 Visual Analytics Pipeline |
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91 | (14) |
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3.2.1 Data Basis and Runtimes |
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91 | (2) |
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3.2.2 Patterns, Correlations, and Rules |
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93 | (4) |
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3.2.3 Tasks and Hypotheses |
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97 | (5) |
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3.2.4 Refinements and Adaptations |
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102 | (2) |
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3.2.5 Insights and Knowledge |
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104 | (1) |
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3.3 Challenges of Algorithmic Concepts |
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105 | (11) |
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106 | (4) |
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3.3.2 Parameter Specifications |
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110 | (1) |
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3.3.3 Algorithmic Runtime Complexities |
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111 | (1) |
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3.3.4 Performance Evaluation |
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112 | (2) |
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3.3.5 Insights into the Running Algorithm |
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114 | (2) |
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116 | (9) |
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117 | (1) |
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3.4.2 Digital and Computational Pathology |
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118 | (1) |
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119 | (1) |
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3.4.4 Video Data Analysis |
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120 | (2) |
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122 | (3) |
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125 | (50) |
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127 | (11) |
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4.1.1 Pilot vs. Real Study |
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128 | (1) |
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4.1.2 Quantitative vs. Qualitative |
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129 | (1) |
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4.1.3 Controlled vs. Uncontrolled |
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130 | (2) |
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4.1.4 Expert vs. Non-Expert |
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132 | (2) |
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4.1.5 Short-term vs. Longitudinal |
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134 | (1) |
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4.1.6 Limited-number Population vs. Crowdsourcing |
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135 | (1) |
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136 | (2) |
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4.1.8 With vs. Without Eye Tracking |
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138 | (1) |
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138 | (9) |
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139 | (2) |
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141 | (1) |
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4.2.3 Cultural Differences |
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142 | (2) |
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4.2.4 Vision Deficiencies |
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144 | (1) |
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145 | (2) |
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4.3 Study Design and Ingredients |
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147 | (11) |
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4.3.1 Hypotheses and Research Questions |
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148 | (1) |
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149 | (2) |
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151 | (2) |
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4.3.4 Independent and Dependent Variables |
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153 | (4) |
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157 | (1) |
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4.4 Statistical Evaluation and Visual Results |
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158 | (9) |
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4.4.1 Data Preparation and Descriptive Statistics |
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160 | (1) |
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4.4.2 Statistical Tests and Inferential Statistics |
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161 | (2) |
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4.4.3 Visual Representation of the Study Results |
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163 | (4) |
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4.5 Example User Studies Without Eye Tracking |
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167 | (8) |
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4.5.1 Hierarchy Visualization Studies |
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168 | (1) |
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4.5.2 Graph Visualization Studies |
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169 | (2) |
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4.5.3 Interaction Technique Studies |
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171 | (1) |
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4.5.4 Visual Analytics Studies |
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172 | (3) |
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175 | (54) |
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177 | (8) |
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178 | (1) |
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5.1.2 Eye Movement and Smooth Pursuit |
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179 | (2) |
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5.1.3 Disorders and Diseases Influencing Eye Tracking |
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181 | (2) |
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5.1.4 Corrected-to-Normal Vision |
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183 | (2) |
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185 | (12) |
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186 | (2) |
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5.2.2 Progress in the Field |
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188 | (2) |
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190 | (2) |
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5.2.4 Companies, Technologies, and Devices |
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192 | (1) |
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192 | (5) |
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5.3 Eye Tracking Data Properties |
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197 | (12) |
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199 | (3) |
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5.3.2 Gaze Points, Fixations, Saccades, and Scanpaths |
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202 | (2) |
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5.3.3 Areas of Interest (AOIs) and Transitions |
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204 | (2) |
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5.3.4 Physiological and Additional Measures |
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206 | (2) |
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208 | (1) |
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5.4 Examples of Eye Tracking Studies |
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209 | (20) |
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5.4.1 Eye Tracking for Static Visualizations |
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210 | (5) |
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5.4.2 Eye Tracking for Interaction Techniques |
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215 | (3) |
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5.4.3 Eye Tracking for Text/Label/Code Reading |
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218 | (3) |
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5.4.4 Eye Tracking for User Interfaces |
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221 | (2) |
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5.4.5 Eye Tracking for Visual Analytics |
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223 | (6) |
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6 Eye Tracking Data Analytics |
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229 | (38) |
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230 | (7) |
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6.1.1 Data Collection and Acquisition |
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231 | (1) |
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6.1.2 Organization and Relevance |
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232 | (2) |
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6.1.3 Data Annotation and Anonymization |
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234 | (1) |
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6.1.4 Data Interpretation |
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235 | (1) |
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236 | (1) |
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6.2 Data Storage, Adaptation, and Transformation |
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237 | (6) |
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238 | (2) |
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6.2.2 Validation, Verification, and Cleaning |
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240 | (1) |
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6.2.3 Data Enhancement and Enrichment |
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241 | (1) |
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6.2.4 Data Transformation |
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242 | (1) |
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243 | (11) |
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6.3.1 Ordering and Sorting |
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244 | (1) |
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245 | (2) |
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6.3.3 Summarization, Classing, and Classification |
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247 | (1) |
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6.3.4 Normalization and Aggregation |
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248 | (1) |
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6.3.5 Projection and Dimensionality Reduction |
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249 | (1) |
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6.3.6 Correlation and Trend Analysis |
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250 | (2) |
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6.3.7 Pairwise or Multiple Sequence Alignment |
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252 | (1) |
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6.3.8 Artificial Intelligence-Related Approaches |
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253 | (1) |
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6.4 Visualization Techniques and Visual Analytics |
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254 | (13) |
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256 | (1) |
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6.4.2 Point-based Visualization Techniques |
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257 | (4) |
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6.4.3 AOI-based Visualization Techniques |
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261 | (2) |
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6.4.4 Eye Tracking Visual Analytics |
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263 | (4) |
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7 Open Challenges, Problems, and Difficulties |
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267 | (6) |
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7.1 Eye Tracking Challenges |
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267 | (2) |
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7.2 Eye Tracking Visual Analytics Challenges |
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269 | (4) |
References |
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273 | (62) |
Index |
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335 | (12) |
About the Author |
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347 | |