Big data and smart digital environment

"This book reviews the state of the art of big data analysis and smart city. It includes issues which pertain to signal processing, probability models, machine learning, data mining, database, data engineering, pattern recognition, visualisation, predictive analytics, data warehousing, data compression, computer programming, smart city, etc. Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. Data science inspires novel techniques and theories drawn from mathematics, statistics, information theory, computer science, and social science. Papers in this book were the outcome of research conducted in this field of study. The latter makes use of applications and techniques related to data analysis in general and big data and smart city in particular. The book appeals to advanced undergraduate and graduate students, postdoctoral researchers, lecturers and industrial researchers, as well as anyone interested in big data analysis and smart city." -- Springer Contents......Page 6 Introduction......Page 10 1 Introduction......Page 11 2.2 Queuing Systems for the Parking Service......Page 12 3.2 Simulation System by ARENA Rockwell......Page 13 4 Result and Discuss......Page 14 References......Page 15 2 The Advantages of the Smart Water Management......Page 17 3.1 The Human Pillar......Page 18 3.3 The Economic Pillar......Page 19 4.1 The Components of the Smart Water Grid......Page 20 4.2 The Keys of the Smart Water Grid......Page 21 References......Page 23 1 Introduction......Page 25 2.1 Architecture of the System......Page 26 2.2 The Proposed Process of Adaptation......Page 27 3 Conclusion......Page 28 References......Page 29 1 Introduction......Page 30 3 Modified Torque Control Strategy......Page 31 3.2 Flux Control......Page 33 5 Results and Discussion......Page 34 References......Page 36 Abstract......Page 37 1 Introduction......Page 38 2 Versioning of Temporal Logical and Physical Characteristics......Page 39 3.2 Design Choices......Page 40 3.3 Operations Acting on the Whole Temporal Characteristics Document......Page 41 3.5 Operations Specific to the Temporal Logical Characteristics......Page 42 3.6 Operations Specific to the Temporal Physical Characteristics......Page 43 4 Application Example......Page 44 References......Page 48 1 Introduction......Page 50 2.1 ISysQ Model Background......Page 51 2.2 Aggregating Variables Questions into Variables Indicators......Page 53 3 Results......Page 54 4 Discussion......Page 56 Appendix: ISysQ Model Abbreviations......Page 57 References......Page 58 1 Introduction......Page 60 2.1 Line Segment Localization......Page 61 2.2 Clustering of Line Segments......Page 62 2.3 Segments Filtering......Page 63 3 Results......Page 64 3.2 Comparison with Existing Methods......Page 66 References......Page 68 1 Introduction......Page 70 2 Chipless RFID Tag Design......Page 71 3 Ultra Wide Band Antenna Design......Page 74 4 Conclusion......Page 75 References......Page 76 1 Introduction......Page 77 3 Review of Ye et al. Authentication Protocol......Page 79 4.2 Generating of Session Key......Page 81 4.4 Problem in Step 5......Page 82 References......Page 83 1.1 Bioinformatics......Page 85 1.3 DNA Methylation Datasets......Page 86 1.5 Formal Description of the Clustering Problematic......Page 87 2.2 Algorithmic Description......Page 88 2.4 Outline of the Proposed Method......Page 89 3.1.1 DNA-Methylation Datasets......Page 90 3.2.1 F-Measure......Page 91 3.3 Empirical Results and Comparison......Page 92 4 Conclusions......Page 94 References......Page 95 1 Introduction......Page 96 2.2 Variable Neighborhood Descent Method (VND)......Page 97 2.3 VNPSO Components of MSA Problem......Page 98 3 Simulation and Results......Page 99 References......Page 101 Abstract......Page 102 2.1 Adaptivity in E-learning Systems......Page 103 2.2 Recommender Systems......Page 104 2.3 Adaptivity and Personalization in MOOCs/SPOCs......Page 105 3.2 The Proposed Recommendation Approach......Page 106 4 Conclusion and Future Work......Page 107 References......Page 108 1 Introduction......Page 112 2.1 Intelligent Big Data Analysis......Page 115 2.2 Rapid Automatic Keyword Extraction (Rake)......Page 116 2.3 Latent Dirichlet Allocation (LDA)......Page 117 3 Hybrid Miner-Network Analyzer (HMNA)......Page 118 4.1 Collection Dataset......Page 120 4.3 Applying Pragmatic Method to Text Preprocessing......Page 121 4.4 Classify Document by Find Topic Distribution......Page 122 4.5 Building Multi-level Network......Page 124 4.6 Compute the Characteristic Measures......Page 125 5 Discussions and Conclusions......Page 126 References......Page 127 1 Introduction......Page 129 2.1 Measurement Results and Channel Models......Page 130 2.2 Challenges to Overcome......Page 132 3 5G NR Interface......Page 133 3.1 New Waveform and Optimized Modulation......Page 134 3.2 Beamforming......Page 135 References......Page 138 1 Introduction......Page 142 2.2 Battery Models......Page 144 3 Battery Management System (BMS) and the Mathematical Methods Used in SOC Estimating......Page 145 3.1 Direct Measurement......Page 146 3.3 Adaptive Systems......Page 147 4.1 State of Charge Modeling......Page 148 4.2 Power Consumption Modeling......Page 149 References......Page 152 Abstract......Page 154 1 Introduction......Page 155 2 Related Work......Page 156 4 Approach......Page 157 5.1.2 Pre-processing of the MTT Data......Page 158 5.2.2 Region Based Methods: Region Growing......Page 159 6 Evaluation and Discuss......Page 161 7 Conclusion......Page 162 References......Page 163 1 Introduction......Page 165 2 The Configuration Studied......Page 166 3.2 Hysteresis Control......Page 169 3.3 Direct Power Control Study of SAPF......Page 170 4 Simulation Results......Page 171 5 Conclusion......Page 177 References......Page 178 1 Introduction......Page 179 2 Previous Related Works......Page 180 3.1 Pseudo-random Key Stream Generator......Page 181 4.2 Key Space Analysis......Page 182 4.4 Entropy Correlation Coefficient Analysis......Page 183 5 Conclusion......Page 184 References......Page 185 1 Introduction......Page 186 2 OFDM Basis and OOB Emission......Page 187 3 Results and Discussion......Page 190 4 Conclusion......Page 194 References......Page 195 2 Experimental Results......Page 196 2.3 Comparison......Page 197 References......Page 199 1 Introduction......Page 200 2 Preliminaries......Page 201 3.1 TCP + AQM Dynamics......Page 202 3.2 Multi-class Model of TCP + AQM Dynamics......Page 203 4 Main Results......Page 206 5 Numerical Example......Page 208 References......Page 212 1 Introduction......Page 214 2 Related Works......Page 215 2.2 Divide and Conquer Approach......Page 216 2.3 Bitmap Approach......Page 217 2.5 Nearest Neighbor Approach......Page 218 3 Our Map Reduce Nearest Neighbor Approach (MR-NN)......Page 219 3.1 Comparison Between MR-NNS and NNS......Page 221 3.2 Example......Page 222 References......Page 223 1 Introduction......Page 225 2 Related Works......Page 226 3.1 Data Set and Attributes......Page 227 3.2 Classification Task......Page 228 3.3 Particle Swarm Optimization (PSO)......Page 229 4 Experiments and Results......Page 230 4.1 Effectiveness......Page 231 4.2 Accuracy Results......Page 232 References......Page 233 1 Introduction......Page 235 2 Related Categorical Clustering Approaches Using the k-Modes......Page 236 3.1 The Mode of a Categorical Cluster......Page 239 3.2 The Rough Set Theory......Page 240 3.3 The Rough Modes......Page 241 4 Experiments......Page 244 References......Page 245 1 Introduction......Page 247 2 Requirements......Page 248 3 Architecture Challenges......Page 249 3.1 Master and Slave Domain Challenges......Page 250 3.2 Network Domain Challenges......Page 251 References......Page 254 Abstract......Page 256 2 Literature View......Page 257 3.1 Intelligent Data Analysis (IDA)......Page 260 3.3 Prediction Techniques......Page 261 3.4 Churn Prediction......Page 263 4 Smart Customer Predictor (SCP)......Page 264 4.1 Main Stages of Design (SCP)......Page 267 5 Experiments and Results......Page 273 6 Conclusion and Future Work......Page 280 References......Page 281 1 Introduction......Page 283 2.2 The Pedagogical Object at the Heart of the Main Three Tensions......Page 284 2.4 How the Use of Pedagogical Objects in the Pedagogical Conception of an Adaptive cMOOC Can Be Optimized?......Page 286 3 Result......Page 287 5 Conclusion......Page 288 References......Page 289 1 Introduction......Page 290 2.1 Architecture and Operation of IoT......Page 291 2.2 IoT Platform......Page 292 3.3 Architecture of Cassandra......Page 293 4 Proposed Method......Page 294 5.1 Function of the Platform......Page 295 References......Page 296 1 Introduction......Page 298 2.2.1 Presentation......Page 299 2.3.2 Application Domain€......Page 300 3.1.2 Application Domain......Page 301 3.2.2 Application Domain......Page 302 3.3.4 Disadvantages......Page 303 References......Page 304 1 Introduction......Page 306 2.1 Definition of KM......Page 307 2.2 KM Process......Page 308 2.4 KM Methods and Tools......Page 310 2.6 KM Issues......Page 312 3.1 Current Situation Analysis......Page 313 4.1 Selection of the KM Strategy......Page 314 5 Conclusion......Page 315 References......Page 316 1 Introduction......Page 317 2 Inverter Topology......Page 318 3 The Control Strategies of the Inverter......Page 319 4 Simulation and Results......Page 320 References......Page 322 Abstract......Page 324 2.1 Dataset......Page 325 3 Results......Page 326 4 Conclusion and Discussion......Page 330 References......Page 331 1 Introduction......Page 332 2.1 Frequent Itemset Mining......Page 333 2.2 Prepost Algorithm......Page 334 3 The Proposed Algorithm......Page 336 4 Experiments......Page 339 References......Page 341 1 Introduction......Page 343 2.2 Word Cloud......Page 344 3.1 Grouped Data......Page 345 3.2 Measuring Parameters......Page 346 4.1 Word2vec......Page 347 4.2 Clustering......Page 348 5.2 Choose Number of Topics......Page 349 5.3 Topic Probabilities......Page 350 References......Page 351 2.1.1 Definition......Page 352 2.2 Scalogram......Page 353 3.2 Algorithm......Page 354 3.3.2 Heart Rate Based on RR Interval......Page 355 References......Page 356 1 Introduction......Page 357 2 Related Works......Page 358 3 Our Proposed Approach......Page 359 3.3 The Architecture of Our System......Page 360 3.3.4 Health Agent......Page 362 3.3.8 Geo-Location......Page 363 3.4 A Conceptual Model for Big Data and Social Media......Page 364 4 Experimentation and Results......Page 365 5 Conclusion and Future Works......Page 367 References......Page 368 1 Introduction......Page 369 2 Fundamental Concepts......Page 370 3.2 Risk Assessment Standard......Page 371 3.5 Trust Matrix Risk Assessment......Page 372 3.8 Risk Assessment as Service......Page 373 4 Synthesis and Discussion......Page 374 References......Page 376 1 Introduction......Page 379 2 Captologie......Page 380 3 Related Work......Page 381 4.1 Fogg’s Seven Principles of Persuasion......Page 382 4.2 Cialdini’s Six Principles of Persuasion......Page 383 5 Persuasive Technologies for Urban Mobility......Page 384 6 Comparison Study......Page 386 7 Conclusion et Perspectives......Page 387 Reference......Page 388 1 Introduction......Page 390 2 Fixed Monitoring Stations......Page 391 3 Crowdsourcing and Low-Cost Sensors......Page 392 4 Air Quality Remote Sensing......Page 393 5 Statistical and Machine Learning Based Models......Page 395 References......Page 396 2 Towards Conceptual Representations......Page 398 References......Page 399 1 Introduction......Page 401 1.1 Literature Review on the Marketing Information System......Page 403 2.1 The Need not to Negatize the Risk of Non-use of Computerized Applications Induced by the Marketing Information System......Page 406 3.1 Methodology of Quantitative Research......Page 407 4.1 Analysis of Quantitative Results......Page 409 5 Conclusion......Page 412 References......Page 413 Author Index......Page 414