Foreword |
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iv | |
Preface |
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viii | |
Acknowledgements |
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x | |
List of Figures |
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xvi | |
List of Tables |
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xviii | |
1 Introduction |
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1 | (20) |
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1.1 Motivation and Challenges |
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2 | (1) |
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2 | (7) |
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1.3 Observing and Interpolating Continuous Phenomena |
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9 | (2) |
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1.4 Deterministic Approaches |
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11 | (2) |
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1.5 Geostatistical Approaches |
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13 | (2) |
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15 | (1) |
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16 | (3) |
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19 | (2) |
2 Monitoring Continuous Phenomena |
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21 | (16) |
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22 | (2) |
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24 | (3) |
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2.2.1 (Near) Real-Time Monitoring |
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24 | (1) |
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2.2.2 Persistent Storage and Archiving |
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25 | (1) |
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26 | (1) |
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2.3 Resources and Limitations |
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27 | (6) |
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29 | (1) |
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29 | (2) |
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2.3.3 Computational Power |
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31 | (1) |
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2.3.4 Time (Processing and Transmission) |
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31 | (1) |
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2.3.5 Energy (Processing and Transmission) |
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32 | (1) |
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33 | (4) |
3 Spatio-Temporal Interpolation: Kriging |
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37 | (14) |
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38 | (1) |
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3.2 The Experimental Variogram |
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39 | (1) |
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3.3 The Theoretical Variogram and the Covariance Function |
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40 | (5) |
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3.4 Variants and Parameters |
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45 | (3) |
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48 | (2) |
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50 | (1) |
4 Representation of Continuous Phenomena: Vector and Raster Data |
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51 | (10) |
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52 | (3) |
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4.2 Vector Data Properties |
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55 | (1) |
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4.3 Raster Data Properties |
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56 | (1) |
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4.4 Raster-Vector Interoperability |
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57 | (3) |
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60 | (1) |
5 A Generic System Architecture for Monitoring Continuous Phenomena |
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61 | (58) |
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63 | (1) |
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5.2 Workflow Abstraction Concept |
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64 | (4) |
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5.2.1 Datasets (Input/Source and Output/Sink) |
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66 | (1) |
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5.2.2 Process/Transmission |
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67 | (1) |
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5.3 Monitoring Process Chain |
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68 | (21) |
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5.3.1 Random Field Generation by Variogram Filter |
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70 | (3) |
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5.3.2 Sampling and Sampling Density |
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73 | (6) |
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5.3.3 Experimental Variogram Generation |
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79 | (1) |
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5.3.4 Experimental Variogram Aggregation |
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80 | (5) |
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85 | (3) |
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88 | (1) |
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88 | (1) |
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5.4 Performance Improvements for Data Stream Management |
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89 | (16) |
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90 | (1) |
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5.4.2 Sequential Model Merging Approach |
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91 | (7) |
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91 | (1) |
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92 | (1) |
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92 | (1) |
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93 | (2) |
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5.4.2.5 Partitioning Large Models: Performance Considerations |
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95 | (3) |
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5.4.3 Compression and Progressive Retrieval |
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98 | (7) |
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98 | (1) |
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99 | (1) |
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99 | (1) |
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100 | (1) |
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5.4.3.5 Binary Interval Subdivision |
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100 | (1) |
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5.4.3.6 Supported Data Types |
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101 | (2) |
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5.4.3.7 Compression Features |
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103 | (2) |
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5.5 Generic Toolset for Variation and Evaluation of System Configurations |
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105 | (13) |
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5.5.1 Context and Abstraction |
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106 | (3) |
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5.5.2 Computational Workload |
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109 | (4) |
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5.5.3 Systematic Variation of Methods, Parameters and Configurations |
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113 | (2) |
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5.5.4 Overall Evaluation Concept |
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115 | (3) |
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118 | (1) |
6 A General Concept for Higher Level Queries about Continuous Phenomena |
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119 | (10) |
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120 | (1) |
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121 | (3) |
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124 | (1) |
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125 | (2) |
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127 | (2) |
7 Experimental Evaluation |
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129 | (40) |
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7.1 Minimum Sampling Density Estimator |
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131 | (4) |
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131 | (1) |
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131 | (4) |
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135 | (1) |
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135 | (6) |
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136 | (3) |
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139 | (2) |
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141 | (1) |
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141 | (4) |
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142 | (1) |
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142 | (2) |
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144 | (1) |
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145 | (6) |
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145 | (3) |
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148 | (2) |
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150 | (1) |
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7.5 Prediction of Computational Effort |
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151 | (2) |
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151 | (1) |
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152 | (1) |
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152 | (1) |
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153 | (9) |
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153 | (4) |
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157 | (2) |
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159 | (3) |
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7.7 Case Study: Satellite Temperature Data |
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162 | (7) |
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163 | (2) |
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165 | (2) |
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167 | (2) |
8 Conclusions |
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169 | (8) |
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8.1 Subsuming System Overview |
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170 | (5) |
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175 | (2) |
References |
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177 | (12) |
Index |
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189 | |