Computational Intelligence for big data analysis: frontier advances and applications

✍ Scribed by Acharjya, D P(Editor);Dehuri, Satchidananda(Editor);Sanyal, Sugata(Editor)

• Publisher: Springer International Publishing
• Year: 2015
• Tongue: English
• Leaves: 276
• Series: Adaptation learning and optimization (Print) 19
• Category: Library

✦ Synopsis

The work presented in this book is a combination of theoretical advancements of big data analysis, cloud computing, and their potential applications in scientific computing. The theoretical advancements are supported with illustrative examples and its applications in handling real life problems. The applications are mostly undertaken from real life situations. The book discusses major issues pertaining to big data analysis using computational intelligence techniques and some issues of cloud computing. An elaborate bibliography is provided at the end of each chapter. The material in this book includes concepts, figures, graphs, and tables to guide researchers in the area of big data analysis and cloud computing.

✦ Table of Contents

Preface......Page 6
Acknowledgment......Page 11
Contents......Page 12
Part I:Theoretical Foundation of Big DataAnalysis......Page 19
“Atrain Distributed System” (ADS): An Infinitely Scalable Architecture for Processing Big Data of Any 4Vs......Page 20
1 Introduction......Page 21
2 “r-Train” (train) and “r-Atrain” (atrain): The Data Structures for Big Data......Page 22
2.1 Larray......Page 23
2.2 Homogeneous Data Structure “r-Train” (train) for Homogeneous Big Data......Page 24
2.3 r-Atrain (Atrain): A Powerful Heterogeneous Data Structure for Big Data......Page 32
3.1 Solid Matrix and Solid Latrix......Page 40
3.2 3-D Solid Matrix (3-SM) and Some Characterizations......Page 41
4 Algebra of Solid Matrices (Whose Elements Are Numbers)......Page 43
5.1 Implementation of a 3-SM (3-SL)......Page 46
6 Hematrix and Helatrix: Storage Model for Heterogeneous Big Data......Page 52
7.1 Atrain Distributed System (ADS)......Page 53
7.2 ‘Multi-horse Cart Topology’ and ‘Cycle Topology’ for ADS......Page 54
8 The Heterogeneous Data Structure ‘r-Atrain’ in an Atrain Distributed System (ADS)......Page 56
8.1 Coach of a r-Atrain in an ADS......Page 57
8.2 Circular Train and Circular Atrain......Page 62
8.3 Fundamental Operations on ‘r-Atrain’ in an ADS for Big Data......Page 63
9 Heterogeneous Data Structures ‘MA’ for Solid Helatrix of Big Data......Page 66
10 Conclusion......Page 68
References......Page 70
1 Introduction......Page 72
2 Foundations of Fuzzy Set......Page 74
3.1 Artificial Neural Network: An Overview......Page 75
3.2 Fuzzy-Neuro Hybridized Approach: A New Paradigm for the Big Data Time Series Forecasting......Page 77
4 Description of Data Set......Page 78
5.1 EIBD Approach......Page 79
6 Fuzzy-Neuro Forecasting Model for Big Data: Detail Explanation......Page 80
7 Performance Analysis Parameters......Page 84
8.1 Forecasting with the M-factors......Page 85
8.3 Forecasting with Three-factors......Page 86
8.4 Statistical Significance......Page 87
References......Page 88
Learning Using Hybrid Intelligence Techniques......Page 90
1 Introduction......Page 91
2 Gene Selection Using Intelligent Hybrid PSO and Quick-Reduct Algorithm......Page 93
2.1 Particle Swarm Optimization......Page 95
2.2 Proposed Algorithm......Page 96
2.3 Implementation and Results......Page 98
3.1 Rough Set......Page 100
3.2 Gene Selection Based on Rough Set Method......Page 101
3.4 Implementation and Results......Page 102
4 Hybrid Data Mining Technique (CFS + PLS) for Improving Classification Accuracy of Microarray Data......Page 104
4.1 SIMPLS and Dimension Reduction in the Classification Framework......Page 105
4.2 Partial Least Squares Regression......Page 106
4.3 Implementation and Results......Page 108
5 Conclusion......Page 110
References......Page 111
1 Introduction......Page 114
2 Single Valued Neutrosophic Multisets......Page 116
3.1 Distance between Two Neutrosophic Sets......Page 118
3.2 Similarity Measure between Two Single Valued Neutrosophic Sets......Page 120
4 Interval Valued Neutrosophic Soft Sets......Page 124
4.2 Interval Valued Neutrosophic Soft Sets......Page 125
4.3 An Application of IVNSS in Decision Making......Page 130
References......Page 131
Part II:Architecture for Big Data Analysis......Page 133
An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis......Page 134
1 Introduction......Page 135
2 Problem Definition......Page 137
3 The Proposed Algorithm......Page 139
3.1 Encoding......Page 141
3.2 Fitness Function......Page 142
3.4 Crossover Operator......Page 144
3.5 Mutation Operators......Page 146
3.7 Local Search......Page 148
4 Validation of Clustering......Page 149
5.1 Data Sets......Page 150
5.2 Results......Page 152
References......Page 155
1 Introduction......Page 158
2 Self Organizing Migrating Algorithm......Page 160
3 Proposed NMSOMA-M Algorithm......Page 161
3.1 Nelder Mead (NM) Crossover Operator......Page 162
3.2 Log Logistic Mutation Operator......Page 163
4 Benchmark Functions......Page 164
5 Numerical Results on Benchmark Problems......Page 167
References......Page 178
1 Introduction......Page 180
2 Big Data in Clinical Domain......Page 182
3 Framework for Big Data Analytics......Page 183
3.2 Data Preprocessing......Page 184
3.4 Data Mining Techniques......Page 185
3.5 Description and Visualization......Page 186
4 Results and Implementation......Page 187
5 Conclusion......Page 191
References......Page 192
1 Introduction......Page 195
2.1 Spontaneous EEG Waves......Page 196
2.2 Event-Related Potential (ERP)......Page 197
2.3 Components of EEG Based Systems......Page 200
3 Generation of Visual Stimuli......Page 201
4.1 Preprocessing......Page 203
4.2 Feature Extraction......Page 204
4.3 Feature Selection and Reduction......Page 206
4.4 Classification......Page 207
5 Conclusion......Page 208
References......Page 209
Part III:Big Data Analysis and Cloud Computing......Page 212
1 Introduction......Page 213
2 Cloud Computing and Big Data......Page 214
2.1 Benefits for Big Data on Cloud Adoption [21]......Page 215
3 Big Data Processing Challenges in Cloud Computing......Page 216
3.1 Data Capture and Storage......Page 217
3.3 Data Curation......Page 218
3.4 Data Analysis......Page 220
3.5 Data Visualization......Page 221
4.1 Processing Big Data with MapReduce......Page 222
4.2 Processing Big Data with Haloop......Page 224
4.7 Rackspace......Page 226
4.12 EnterpriseDB......Page 227
References......Page 228
1 Introduction......Page 230
2.1 Cloud Computing......Page 231
2.3 Necessity for Using Multiple Clouds......Page 234
2.4 Challenges for Migration......Page 235
3 Techniques for Modernization of Application to Cloud......Page 237
3.1 Existing Technologies......Page 238
4 Portability Issues in Cloud Applications......Page 241
5 Proposed Approach......Page 242
References......Page 245
1 Introduction......Page 247
2 WAN Optimization......Page 249
3 WAN Optimization Techniques......Page 250
3.1 WAN Optimization for Video Surveillance......Page 251
4.1 Blue Coat Application Delivery Network......Page 252
6.1 Complementing WAN Optimization Controller Investment for Big Data and Bulk Data Transfer......Page 253
6.2 WAN Optimization Controller Comparison: Evaluating Vendors and Products......Page 254
7 WAN Optimization for Big Data Analytics......Page 255
8.1 Infineta Sytems and Qfabric......Page 256
8.2 BIG-IP WAN Optimization Manager......Page 257
8.4 EMC Isilon and Silver Peak WAN Optimization......Page 258
8.7 F5 WAN Optimization for Oracle Database Replication Services Faster Replication across the WAN (Can Title be Short)......Page 260
9 Future Trends and Research Potentials......Page 261
9.2 Limitations of WAN Optimization Products......Page 262
References......Page 263
1 Introduction......Page 265
2 ACME Development Authorities Management System......Page 266
3 The Cloud Solution......Page 269
3.1 Technical Solution Architecture......Page 270
3.2 The Modular aDAMS Solution......Page 271
References......Page 274
Author Index......Page 276