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Computational Intelligence for Big Data Analysis : Frontier Advances and Applications.

By: Acharjya, D. P.
Contributor(s): Dehuri, Satchidananda | Sanyal, Sugata.
Material type: TextTextSeries: eBooks on Demand.Adaptation, Learning, and Optimization Ser: Publisher: Cham : Springer, 2015Copyright date: ©2015Description: 1 online resource (276 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9783319165981.Subject(s): Computational intelligence.;Electronic data processing -- Data entry.;Big dataGenre/Form: Electronic books.Additional physical formats: Print version:: Computational Intelligence for Big Data Analysis : Frontier Advances and ApplicationsDDC classification: 620.00151 LOC classification: TA1-2040Online resources: Click here to view this ebook.
Contents:
Intro -- Preface -- Acknowledgment -- Contents -- Part I: Theoretical Foundation of Big Data Analysis -- "Atrain Distributed System" (ADS): An Infinitely Scalable Architecture for Processing Big Data of Any 4Vs -- 1 Introduction -- 2 "r-Train" (train) and "r-Atrain" (atrain): The Data Structures for Big Data -- 2.1 Larray -- 2.2 Homogeneous Data Structure "r-Train" (train) for Homogeneous Big Data -- 2.3 r-Atrain (Atrain): A Powerful Heterogeneous Data Structure for Big Data -- 3 Solid Matrix and Solid Latrix (for Big Data and Temporal Big Data) -- 3.1 Solid Matrix and Solid Latrix -- 3.2 3-D Solid Matrix (3-SM) and Some Characterizations -- 4 Algebra of Solid Matrices (Whose Elements Are Numbers) -- 5 Homogeneous Data Structure 'MT' for Solid Matrix/Latrix -- 5.1 Implementation of a 3-SM (3-SL) -- 6 Hematrix and Helatrix: Storage Model for Heterogeneous Big Data -- 7 Atrain Distributed System (ADS) for Big Data -- 7.1 Atrain Distributed System (ADS) -- 7.2 'Multi-horse Cart Topology' and 'Cycle Topology' for ADS -- 8 The Heterogeneous Data Structure 'r-Atrain' in an Atrain Distributed System (ADS) -- 8.1 Coach of a r-Atrain in an ADS -- 8.2 Circular Train and Circular Atrain -- 8.3 Fundamental Operations on 'r-Atrain' in an ADS for Big Data -- 9 Heterogeneous Data Structures 'MA' for Solid Helatrix of Big Data -- 10 Conclusion -- References -- Big Data Time Series Forecasting Model: A Fuzzy-Neuro Hybridize Approach -- 1 Introduction -- 2 Foundations of Fuzzy Set -- 3 Fuzzy-Neuro Hybridization and Big Data Time Series -- 3.1 Artificial Neural Network: An Overview -- 3.2 Fuzzy-Neuro Hybridized Approach: A New Paradigm for the Big Data Time Series Forecasting -- 4 Description of Data Set -- 5 Proposed Approach and Algorithm -- 5.1 EIBD Approach -- 5.2 Algorithm for the Big Data Time Series Forecasting Model.
6 Fuzzy-Neuro Forecasting Model for Big Data: Detail Explanation -- 7 Performance Analysis Parameters -- 8 Empirical Analysis -- 8.1 Forecasting with the M-factors -- 8.2 Forecasting with Two-factors -- 8.3 Forecasting with Three-factors -- 8.4 Statistical Significance -- 9 Conclusion and Discussion -- References -- Learning Using Hybrid Intelligence Techniques -- 1 Introduction -- 2 Gene Selection Using Intelligent Hybrid PSO and Quick-Reduct Algorithm -- 2.1 Particle Swarm Optimization -- 2.2 Proposed Algorithm -- 2.3 Implementation and Results -- 3 Rough Set Aided Hybrid Gene Selection for Cancer Classification -- 3.1 Rough Set -- 3.2 Gene Selection Based on Rough Set Method -- 3.3 Supervised Correlation Based Reduct Algorithm (CFS-RST) -- 3.4 Implementation and Results -- 4 Hybrid Data Mining Technique (CFS + PLS) for Improving Classification Accuracy of Microarray Data -- 4.1 SIMPLS and Dimension Reduction in the Classification Framework -- 4.2 Partial Least Squares Regression -- 4.3 Implementation and Results -- 5 Conclusion -- 6 Scope for Future Work -- References -- Neutrosophic Sets and Its Applications to Decision Making -- 1 Introduction -- 2 Single Valued Neutrosophic Multisets -- 3 Distance, Similarity and Entropy of Single Valued Neutrosophic Multisets -- 3.1 Distance between Two Neutrosophic Sets -- 3.2 Similarity Measure between Two Single Valued Neutrosophic Sets -- 4 Interval Valued Neutrosophic Soft Sets -- 4.1 Soft Set -- 4.2 Interval Valued Neutrosophic Soft Sets -- 4.3 An Application of IVNSS in Decision Making -- 5 Conclusion -- References -- Part II: Architecture for Big Data Analysis -- An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis -- 1 Introduction -- 2 Problem Definition -- 3 The Proposed Algorithm -- 3.1 Encoding -- 3.2 Fitness Function -- 3.3 Selection Operator -- 3.4 Crossover Operator.
3.5 Mutation Operators -- 3.6 Replacement and Elitism -- 3.7 Local Search -- 4 Validation of Clustering -- 5 Experiments and Evaluation -- 5.1 Data Sets -- 5.2 Results -- 6 Conclusions -- References -- Self Organizing Migrating Algorithm with Nelder Mead Crossover and Log-Logistic Mutation for Large Scale Optimization -- 1 Introduction -- 2 Self Organizing Migrating Algorithm -- 3 Proposed NMSOMA-M Algorithm -- 3.1 Nelder Mead (NM) Crossover Operator -- 3.2 Log Logistic Mutation Operator -- 3.3 Methodology of the Proposed Algorithm NMSOMA-M -- 4 Benchmark Functions -- 5 Numerical Results on Benchmark Problems -- 6 Conclusion -- References -- A Spectrum of Big Data Applications for Data Analytics -- 1 Introduction -- 2 Big Data in Clinical Domain -- 3 Framework for Big Data Analytics -- 3.1 Big Data -- 3.2 Data Preprocessing -- 3.3 Training Set -- 3.4 Data Mining Techniques -- 3.5 Description and Visualization -- 4 Results and Implementation -- 5 Conclusion -- References -- Fundamentals of Brain Signals and Its Medical Application Using Data Analysis Techniques -- 1 Introduction -- 2 BrainWaves -- 2.1 Spontaneous EEG Waves -- 2.2 Event-Related Potential (ERP) -- 2.3 Components of EEG Based Systems -- 3 Generation of Visual Stimuli -- 4 Processing of Brain Signals -- 4.1 Preprocessing -- 4.2 Feature Extraction -- 4.3 Feature Selection and Reduction -- 4.4 Classification -- 5 Conclusion -- 6 Future Work -- References -- Part III: Big Data Analysis and Cloud Computing -- BigData: Processing of Data Intensive Applications on Cloud -- 1 Introduction -- 2 Cloud Computing and Big Data -- 2.1 Benefits for Big Data on Cloud Adoption [21] -- 3 Big Data Processing Challenges in Cloud Computing -- 3.1 Data Capture and Storage -- 3.2 Data Transmission -- 3.3 Data Curation -- 3.4 Data Analysis -- 3.5 Data Visualization.
4 Big Data Cloud Tools: Techniques and Technologies -- 4.1 Processing Big Data with MapReduce -- 4.2 Processing Big Data with Haloop -- 4.3 Cloudant -- 4.4 Xeround -- 4.5 StormDB -- 4.6 SAP -- 4.7 Rackspace -- 4.8 MongoLab -- 4.9 Microsoft Azure -- 4.10 Google Cloud SQL -- 4.11 Garantia Data -- 4.12 EnterpriseDB -- 4.13 Amazon Web Services -- 5 Conclusion -- References -- Framework for Supporting Heterogenous Clouds Using Model Driven Approach -- 1 Introduction -- 2 Background -- 2.1 Cloud Computing -- 2.2 Model Driven Engineering -- 2.3 Necessity for Using Multiple Clouds -- 2.4 Challenges for Migration -- 3 Techniques for Modernization of Application to Cloud -- 3.1 Existing Technologies -- 4 Portability Issues in Cloud Applications -- 5 Proposed Approach -- 6 Conclusion -- References -- Cloud Based Big Data Analytics: WAN Optimization Techniques and Solutions -- 1 Introduction -- 2 WAN Optimization -- 2.1 Issues and Challenges -- 3 WAN Optimization Techniques -- 3.1 WAN Optimization for Video Surveillance -- 4 Tools to Improve Application Performance -- 4.1 Blue Coat Application Delivery Network -- 5 WAN Optimization Appliances -- 6 WAN Optimization Controllers -- 6.1 Complementing WAN Optimization Controller Investment for Big Data and Bulk Data Transfer -- 6.2 WAN Optimization Controller Comparison: Evaluating Vendors and Products -- 7 WAN Optimization for Big Data Analytics -- 7.1 Key Trends in WAN Optimization for Big Data Analytics -- 7.2 Drivers of WAN Optimization for Big Data -- 8 WAN Optimization Solutions -- 8.1 Infineta Sytems and Qfabric -- 8.2 BIG-IP WAN Optimization Manager -- 8.3 Edge Virtual Server Infrastructure -- 8.4 EMC Isilon and Silver Peak WAN Optimization -- 8.5 F5 WAN OptimizationModule (WOM) -- 8.6 BIG-IP WAN Optimization Module.
8.7 F5 WAN Optimization for Oracle Database Replication Services Faster Replication across the WAN (Can Title be Short) -- 9 Future Trends and Research Potentials -- 9.1 WAN Optimization in Virtual Data Environments and Cloud Services -- 9.2 Limitations of WAN Optimization Products -- 9.3 Accelerating Data Migration with WAN Optimization -- 10 Conclusion -- References -- Cloud Based E-Governance Solution: A Case Study -- 1 Introduction -- 2 ACME Development Authorities Management System -- 3 The Cloud Solution -- 3.1 Technical Solution Architecture -- 3.2 The Modular aDAMS Solution -- 4 Conclusion -- References -- Author Index.
Summary: 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.
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Intro -- Preface -- Acknowledgment -- Contents -- Part I: Theoretical Foundation of Big Data Analysis -- "Atrain Distributed System" (ADS): An Infinitely Scalable Architecture for Processing Big Data of Any 4Vs -- 1 Introduction -- 2 "r-Train" (train) and "r-Atrain" (atrain): The Data Structures for Big Data -- 2.1 Larray -- 2.2 Homogeneous Data Structure "r-Train" (train) for Homogeneous Big Data -- 2.3 r-Atrain (Atrain): A Powerful Heterogeneous Data Structure for Big Data -- 3 Solid Matrix and Solid Latrix (for Big Data and Temporal Big Data) -- 3.1 Solid Matrix and Solid Latrix -- 3.2 3-D Solid Matrix (3-SM) and Some Characterizations -- 4 Algebra of Solid Matrices (Whose Elements Are Numbers) -- 5 Homogeneous Data Structure 'MT' for Solid Matrix/Latrix -- 5.1 Implementation of a 3-SM (3-SL) -- 6 Hematrix and Helatrix: Storage Model for Heterogeneous Big Data -- 7 Atrain Distributed System (ADS) for Big Data -- 7.1 Atrain Distributed System (ADS) -- 7.2 'Multi-horse Cart Topology' and 'Cycle Topology' for ADS -- 8 The Heterogeneous Data Structure 'r-Atrain' in an Atrain Distributed System (ADS) -- 8.1 Coach of a r-Atrain in an ADS -- 8.2 Circular Train and Circular Atrain -- 8.3 Fundamental Operations on 'r-Atrain' in an ADS for Big Data -- 9 Heterogeneous Data Structures 'MA' for Solid Helatrix of Big Data -- 10 Conclusion -- References -- Big Data Time Series Forecasting Model: A Fuzzy-Neuro Hybridize Approach -- 1 Introduction -- 2 Foundations of Fuzzy Set -- 3 Fuzzy-Neuro Hybridization and Big Data Time Series -- 3.1 Artificial Neural Network: An Overview -- 3.2 Fuzzy-Neuro Hybridized Approach: A New Paradigm for the Big Data Time Series Forecasting -- 4 Description of Data Set -- 5 Proposed Approach and Algorithm -- 5.1 EIBD Approach -- 5.2 Algorithm for the Big Data Time Series Forecasting Model.

6 Fuzzy-Neuro Forecasting Model for Big Data: Detail Explanation -- 7 Performance Analysis Parameters -- 8 Empirical Analysis -- 8.1 Forecasting with the M-factors -- 8.2 Forecasting with Two-factors -- 8.3 Forecasting with Three-factors -- 8.4 Statistical Significance -- 9 Conclusion and Discussion -- References -- Learning Using Hybrid Intelligence Techniques -- 1 Introduction -- 2 Gene Selection Using Intelligent Hybrid PSO and Quick-Reduct Algorithm -- 2.1 Particle Swarm Optimization -- 2.2 Proposed Algorithm -- 2.3 Implementation and Results -- 3 Rough Set Aided Hybrid Gene Selection for Cancer Classification -- 3.1 Rough Set -- 3.2 Gene Selection Based on Rough Set Method -- 3.3 Supervised Correlation Based Reduct Algorithm (CFS-RST) -- 3.4 Implementation and Results -- 4 Hybrid Data Mining Technique (CFS + PLS) for Improving Classification Accuracy of Microarray Data -- 4.1 SIMPLS and Dimension Reduction in the Classification Framework -- 4.2 Partial Least Squares Regression -- 4.3 Implementation and Results -- 5 Conclusion -- 6 Scope for Future Work -- References -- Neutrosophic Sets and Its Applications to Decision Making -- 1 Introduction -- 2 Single Valued Neutrosophic Multisets -- 3 Distance, Similarity and Entropy of Single Valued Neutrosophic Multisets -- 3.1 Distance between Two Neutrosophic Sets -- 3.2 Similarity Measure between Two Single Valued Neutrosophic Sets -- 4 Interval Valued Neutrosophic Soft Sets -- 4.1 Soft Set -- 4.2 Interval Valued Neutrosophic Soft Sets -- 4.3 An Application of IVNSS in Decision Making -- 5 Conclusion -- References -- Part II: Architecture for Big Data Analysis -- An Efficient Grouping Genetic Algorithm for Data Clustering and Big Data Analysis -- 1 Introduction -- 2 Problem Definition -- 3 The Proposed Algorithm -- 3.1 Encoding -- 3.2 Fitness Function -- 3.3 Selection Operator -- 3.4 Crossover Operator.

3.5 Mutation Operators -- 3.6 Replacement and Elitism -- 3.7 Local Search -- 4 Validation of Clustering -- 5 Experiments and Evaluation -- 5.1 Data Sets -- 5.2 Results -- 6 Conclusions -- References -- Self Organizing Migrating Algorithm with Nelder Mead Crossover and Log-Logistic Mutation for Large Scale Optimization -- 1 Introduction -- 2 Self Organizing Migrating Algorithm -- 3 Proposed NMSOMA-M Algorithm -- 3.1 Nelder Mead (NM) Crossover Operator -- 3.2 Log Logistic Mutation Operator -- 3.3 Methodology of the Proposed Algorithm NMSOMA-M -- 4 Benchmark Functions -- 5 Numerical Results on Benchmark Problems -- 6 Conclusion -- References -- A Spectrum of Big Data Applications for Data Analytics -- 1 Introduction -- 2 Big Data in Clinical Domain -- 3 Framework for Big Data Analytics -- 3.1 Big Data -- 3.2 Data Preprocessing -- 3.3 Training Set -- 3.4 Data Mining Techniques -- 3.5 Description and Visualization -- 4 Results and Implementation -- 5 Conclusion -- References -- Fundamentals of Brain Signals and Its Medical Application Using Data Analysis Techniques -- 1 Introduction -- 2 BrainWaves -- 2.1 Spontaneous EEG Waves -- 2.2 Event-Related Potential (ERP) -- 2.3 Components of EEG Based Systems -- 3 Generation of Visual Stimuli -- 4 Processing of Brain Signals -- 4.1 Preprocessing -- 4.2 Feature Extraction -- 4.3 Feature Selection and Reduction -- 4.4 Classification -- 5 Conclusion -- 6 Future Work -- References -- Part III: Big Data Analysis and Cloud Computing -- BigData: Processing of Data Intensive Applications on Cloud -- 1 Introduction -- 2 Cloud Computing and Big Data -- 2.1 Benefits for Big Data on Cloud Adoption [21] -- 3 Big Data Processing Challenges in Cloud Computing -- 3.1 Data Capture and Storage -- 3.2 Data Transmission -- 3.3 Data Curation -- 3.4 Data Analysis -- 3.5 Data Visualization.

4 Big Data Cloud Tools: Techniques and Technologies -- 4.1 Processing Big Data with MapReduce -- 4.2 Processing Big Data with Haloop -- 4.3 Cloudant -- 4.4 Xeround -- 4.5 StormDB -- 4.6 SAP -- 4.7 Rackspace -- 4.8 MongoLab -- 4.9 Microsoft Azure -- 4.10 Google Cloud SQL -- 4.11 Garantia Data -- 4.12 EnterpriseDB -- 4.13 Amazon Web Services -- 5 Conclusion -- References -- Framework for Supporting Heterogenous Clouds Using Model Driven Approach -- 1 Introduction -- 2 Background -- 2.1 Cloud Computing -- 2.2 Model Driven Engineering -- 2.3 Necessity for Using Multiple Clouds -- 2.4 Challenges for Migration -- 3 Techniques for Modernization of Application to Cloud -- 3.1 Existing Technologies -- 4 Portability Issues in Cloud Applications -- 5 Proposed Approach -- 6 Conclusion -- References -- Cloud Based Big Data Analytics: WAN Optimization Techniques and Solutions -- 1 Introduction -- 2 WAN Optimization -- 2.1 Issues and Challenges -- 3 WAN Optimization Techniques -- 3.1 WAN Optimization for Video Surveillance -- 4 Tools to Improve Application Performance -- 4.1 Blue Coat Application Delivery Network -- 5 WAN Optimization Appliances -- 6 WAN Optimization Controllers -- 6.1 Complementing WAN Optimization Controller Investment for Big Data and Bulk Data Transfer -- 6.2 WAN Optimization Controller Comparison: Evaluating Vendors and Products -- 7 WAN Optimization for Big Data Analytics -- 7.1 Key Trends in WAN Optimization for Big Data Analytics -- 7.2 Drivers of WAN Optimization for Big Data -- 8 WAN Optimization Solutions -- 8.1 Infineta Sytems and Qfabric -- 8.2 BIG-IP WAN Optimization Manager -- 8.3 Edge Virtual Server Infrastructure -- 8.4 EMC Isilon and Silver Peak WAN Optimization -- 8.5 F5 WAN OptimizationModule (WOM) -- 8.6 BIG-IP WAN Optimization Module.

8.7 F5 WAN Optimization for Oracle Database Replication Services Faster Replication across the WAN (Can Title be Short) -- 9 Future Trends and Research Potentials -- 9.1 WAN Optimization in Virtual Data Environments and Cloud Services -- 9.2 Limitations of WAN Optimization Products -- 9.3 Accelerating Data Migration with WAN Optimization -- 10 Conclusion -- References -- Cloud Based E-Governance Solution: A Case Study -- 1 Introduction -- 2 ACME Development Authorities Management System -- 3 The Cloud Solution -- 3.1 Technical Solution Architecture -- 3.2 The Modular aDAMS Solution -- 4 Conclusion -- References -- Author Index.

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.

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