دانلود کتاب Big Data Optimization: Recent Developments and Challenges

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Preface......Page 6
Acknowledgments......Page 12
Con„tents......Page 13
About the Editor......Page 15
1 Introduction......Page 16
2 Methodology......Page 17
3 Data and Basic Statistics......Page 19
4.1 Mapping the Cognitive Space......Page 20
4.2 Mapping the Social Space......Page 22
References......Page 28
Abstract......Page 32
1 How to Set Up a Big Data Project......Page 33
2 Big Data Management Technologies......Page 41
2.1 NoSQL Systems......Page 43
2.2 NewSQL Systems......Page 46
2.4 Analytical Platforms......Page 47
2.5 Hadoop Based Solutions......Page 48
2.6 Big Data Streaming Systems......Page 49
3.2 Big Data Benchmarking Challenges......Page 50
3.3 Big Data Benchmarking Comparison......Page 51
References......Page 56
Abstract......Page 63
1 Introduction......Page 64
2 Data Manipulation Challenges......Page 65
2.1 Spatial and Temporal Databases......Page 66
2.2 Key-Value Stores and NoSQL......Page 67
2.3 Data Handling and Data Cleaning......Page 68
2.4 Big Data Processing Stack......Page 69
2.5 Processing in Big Data Platforms......Page 71
3.1 Intelligent Reduction Techniques......Page 72
3.3 Predictive Analytics......Page 74
3.4 Prescriptive Analytics......Page 76
4 CyberWater Case Study......Page 77
Acknowledgments......Page 79
References......Page 80
1 What Performance Tool the Users Really Need for Big Data Optimization?......Page 85
2.1 Data Collection......Page 87
2.2 Data Presentation......Page 88
2.3 Data Analysis......Page 89
3.2 Design Considerations......Page 91
3.3 Overall Architecture......Page 93
3.4 Implementation Details......Page 94
3.5 User Cases of Performance Analysis......Page 96
3.6 Other Performance Analysis Tools......Page 100
4.1 Target Users......Page 101
4.3 Overall Architecture......Page 102
4.4 Implementation Details......Page 103
4.5 Industrial Use Cases......Page 105
4.6 Other Auto-Tuning Tools......Page 107
References......Page 108
1 Introduction: Big Image Processing Tasks......Page 111
1.1 Types of Image Processing Tasks......Page 113
2 Regularisation of Inverse Problems......Page 116
3 Non-smooth Geometric Regularisers for Imaging......Page 117
4.1 Remarks About Notation and Discretisation......Page 120
4.2 Primal: FISTA, NESTA, etc.......Page 121
4.3 Primal-Dual: PDHGM, ADMM, and Other Variants on a Theme......Page 123
4.4 When the Proximal Mapping is Difficult......Page 126
5 Second-Order Optimisation Methods for Imaging......Page 127
5.1 Huber-Regularisation......Page 128
5.2 A Primal-Dual Semi-smooth Newton Approach......Page 129
6 Non-linear Operators and Methods for Iterative Regularisation......Page 131
6.1 Inverse Problems with Non-linear Operators......Page 132
6.2 Iterative Regularisation......Page 133
7.1 Convex Relaxation......Page 134
7.2 Decomposition and Preconditioning Techniques......Page 136
8 Conclusions......Page 137
References......Page 138
Abstract......Page 146
1 Introduction......Page 147
2 Interlinking Tools......Page 148
2.2 LIMES......Page 150
2.3 LODRefine......Page 151
3 Interlinking Process......Page 152
4 A Case Study for Interlinking......Page 153
5 Conclusions and Future Directions......Page 156
References......Page 157
1 Introduction......Page 159
2 Topology......Page 161
3 Persistence......Page 164
3.1 Persistence Diagrams as Features......Page 166
3.2 Cohomology and Circular Coordinates......Page 169
4 Mapper......Page 170
5 Optimization......Page 171
6.1 Local to Global......Page 172
6.2 Nonlinear Dimensionality Reduction......Page 175
6.3 Dynamics......Page 177
6.4 Visualization......Page 178
7 Software and Limitations......Page 180
8 Conclusions......Page 181
References......Page 182
Abstract......Page 189
1 Introduction......Page 190
2.2 Big Data Analytics......Page 191
2.2.1 Principal Component Analysis......Page 192
2.2.3 Fuzzy C-Means Clustering......Page 193
2.2.4 Traditional Artificial Neural Networks......Page 194
2.2.5 Traditional Genetic Algorithms......Page 195
3.1 Solar Energy Forecasting......Page 196
3.2 Wind Energy Forecasting......Page 200
3.3 Financial Data Analytics......Page 202
3.3.1 Training Process......Page 203
3.3.2 Biological Data Analytics......Page 205
4 Conclusions......Page 207
References......Page 208
1 Introduction......Page 212
2 Information Commons and Latency Variables......Page 213
3 Evolution and Design of the Genome Commons......Page 215
4 Latency Analysis and the Genome Commons......Page 221
5 Conclusion......Page 224
References......Page 225
Abstract......Page 227
1.2 DC Evolution: Limitations and Strategies......Page 228
1.3 Vision on Future DC......Page 231
2.1 HTC-DC Overview......Page 232
2.3 Pooled Resource Access Protocol (PRAP)......Page 233
2.4 Many-Core Data Processing Unit......Page 235
2.6 Optical Interconnects......Page 236
3 Optimization of Big Data......Page 237
References......Page 238
1 Introduction......Page 240
2.1 General Definition......Page 241
2.2 Dike Monitoring Example......Page 243
3.1.1 Abstraction Level of Interpreted Information......Page 245
3.1.2 Temporal Issues......Page 247
3.2 Performance......Page 248
3.3 Availability......Page 249
4.1 Approaches in Optimization......Page 250
4.1.1 Data Oriented Optimization......Page 251
4.1.3 System Architecture Oriented Optimization......Page 253
4.2.1 Impact on Other Constraints......Page 256
References......Page 258
Smart Sampling and Optimal Dimensionality Reduction of Big Data Using Compressed Sensing......Page 260
1 Introduction......Page 261
2 Background......Page 263
2.1 Notation and Fundamental Knowledge......Page 264
2.2 Compressed Sensing and Sparse Reconstruction......Page 265
3.2 Smart Sampling......Page 269
3.3 Classification and Evaluation......Page 272
4 Experiments......Page 273
4.2 Sensitivity of Compressed Sensing to Randomness......Page 274
4.3 Compressibility Estimation and Optimal Dimensionality Investigation......Page 275
4.4 Investigating the Robustness of Hierarchical Compressed Sensing......Page 277
4.5 Dependance of Random Projections to Data Sparsity......Page 278
4.6 Comparison with Principal Component Analysis......Page 280
5.1 Dimensionality Reduction and Data Compression......Page 281
5.2 Pattern Analysis Using Compressed Sensing......Page 283
6 Conclusions......Page 284
References......Page 285
Optimized Management of BIG Data Produced in Brain Disorder Rehabilitation......Page 290
1 Introduction......Page 291
2 Scientific Dataspace Model......Page 295
2.1 e-Science Life Cycle Activities......Page 297
2.2 The Environment of Dataspaces in e-Science---a BIG Data Challenge......Page 298
2.3 Relationships in the Scientific Dataspace......Page 300
2.4 The e-Science Life Cycle Ontology and Towards Conceptualization in the Brain Damage Restoration Domain......Page 302
3 Data Capture and Processing Model......Page 305
3.1 Use-Cases......Page 306
3.2 Indoor Treatment......Page 307
3.3 Outdoor Treatment......Page 308
3.4 Data Model......Page 309
3.5 Event-Based Data......Page 310
4.1 Motivation and System Usage......Page 311
4.2 System Design......Page 312
4.3 Architecture---a Service-Level View......Page 314
5 Data Analysis and Visualization Services......Page 315
5.1 Sequential Pattern Mining......Page 316
5.2 Association Rule Mining......Page 318
6 Conclusions......Page 320
References......Page 322
1 Introduction......Page 327
2 Liner Shipping Network Design......Page 330
2.1 Container Routing......Page 331
3 Mat-Heuristic for Liner Shipping Network Design......Page 334
4 Computational Results Using LINER-LIB......Page 336
5 Empty Container Repositioning......Page 337
5.1 Path Flow Formulation......Page 339
6 Container Vessel Stowage Plans......Page 340
6.1 Mathematical Model......Page 341
7 Bunker Purchasing......Page 342
7.1 Bunker Purchasing with Contracts......Page 344
8.1 Definitions......Page 346
8.2 Mathematical Model......Page 347
9 Conclusion and Future Challenges......Page 348
References......Page 350
1 Introduction......Page 353
2.2 Eminent Properties of Network......Page 354
2.3 Graph Based Network Notation......Page 356
3.1 What is Optimization......Page 357
3.2 How to Tackle Optimization Problems......Page 358
4.2 Qualitative Community Definition......Page 360
5.1 Single Objective Optimization Model......Page 362
5.2 Multi-objective Optimization Model......Page 365
6.1 Artificial Generated Benchmark Networks......Page 369
6.3 Famous Websites......Page 371
7 Experimental Exhibition......Page 372
8 Concluding Remarks......Page 373
References......Page 374
Abstract......Page 382
1 Introduction......Page 383
2.1 Problem Formulation......Page 384
2.2 Dual Decomposition Method......Page 386
3 Hadoop MapReduce Programming Model......Page 388
4 Numeric Results......Page 391
5 Conclusion......Page 394
References......Page 395
1 Introduction......Page 397
2 Basic Unconstrained Optimization Algorithms......Page 399
2.3 Conjugate Direction Methods......Page 400
2.4 Quasi-Newton Methods......Page 402
3 Conjugate Gradient Methods......Page 407
3.1 The Hestenes-Stiefel Method......Page 408
3.3 The Polak-Ribière-Polyak Method......Page 410
3.5 The Dai-Liao Method......Page 411
3.6 The CG-Descent Algorithm......Page 413
3.7 Hybrid Conjugate Gradient Methods......Page 414
3.8 Spectral Conjugate Gradient Methods......Page 416
4 Limited-Memory Quasi-Newton Methods......Page 417
References......Page 419
1 Introduction......Page 424
2 Notations and Background......Page 425
3.1 Method......Page 426
3.2 Global Convergence......Page 430
4.1 Method......Page 431
5.1 Solvers......Page 434
5.2 Test Problems and Parameters......Page 435
5.3 Results......Page 436
6 Conclusions......Page 438
References......Page 439
1 Introduction......Page 442
2 Differential Evolution and Applications to LSGO......Page 443
2.1 The jDElscop Algorithm......Page 445
2.3 The jDEsps Algorithm......Page 446
3.1 The MA with LS Chains Approach......Page 447
3.2 The MOS-Based Algorithms......Page 449
4 Cooperative Coevolution......Page 450
4.1 Random Grouping......Page 452
4.2 Adaptive Decomposition......Page 454
4.3 Automatic Grouping......Page 456
4.4 Dealing with Unbalanced Subcomponents......Page 458
4.5 Successful Algorithms Based on CC......Page 459
5 Conclusions......Page 460
References......Page 461
1 Introduction......Page 466
2 Smooth Problems with Separable Feasible Sets......Page 468
3 Non-smooth Problems with Separable Feasible Sets......Page 471
4 Problems with Non-separable Feasible Sets......Page 475
References......Page 478
Index......Page 480