Note: Intro -- Preface -- Acknowledgments -- Contents -- About the Editor -- 1 Big Data: Who, What and Where? Social, Cognitive and Journals Map of Big Data Publications with Focus on Optimization -- Abstract -- 1 Introduction -- 2 Methodology -- 3 Data and Basic Statistics -- 4 Results -- 4.1 Mapping the Cognitive Space -- 4.2 Mapping the Social Space -- 5 Conclusion -- References -- 2 Setting Up a Big Data Project: Challenges, Opportunities, Technologies and Optimization -- Abstract -- 1 How to Set Up a Big Data Project -- 2 Big Data Management Technologies -- 2.1 NoSQL Systems -- 2.2 NewSQL Systems -- 2.3 In-Memory Databases -- 2.4 Analytical Platforms -- 2.5 Hadoop Based Solutions -- 2.6 Big Data Streaming Systems -- 3 Big Data Benchmarking -- 3.1 Why Do We Need Big Data Benchmarking? -- 3.2 Big Data Benchmarking Challenges -- 3.3 Big Data Benchmarking Comparison -- 4 Conclusions -- References -- 3 Optimizing Intelligent Reduction Techniques for Big Data -- Abstract -- 1 Introduction -- 2 Data Manipulation Challenges -- 2.1 Spatial and Temporal Databases -- 2.2 Key-Value Stores and NoSQL -- 2.3 Data Handling and Data Cleaning -- 2.4 Big Data Processing Stack -- 2.5 Processing in Big Data Platforms -- 3 Big Data Reduction Techniques -- 3.1 Intelligent Reduction Techniques -- 3.2 Descriptive Analytics -- 3.3 Predictive Analytics -- 3.4 Prescriptive Analytics -- 4 CyberWater Case Study -- 5 Conclusion -- Acknowledgments -- References -- Performance Tools for Big Data Optimization -- 1 What Performance Tool the Users Really Need for Big Data Optimization? -- 2 Challenges of Ideal Performance Tool -- 2.1 Data Collection -- 2.2 Data Presentation -- 2.3 Data Analysis -- 3 A Performance Tool for Tuning Experts: Sonata -- 3.1 Target Users -- 3.2 Design Considerations -- 3.3 Overall Architecture -- 3.4 Implementation Details. , 3.5 User Cases of Performance Analysis -- 3.6 Other Performance Analysis Tools -- 4 An Easy-of-Use Full-System Auto-Tuning Tool: Turbo -- 4.1 Target Users -- 4.2 Design Considerations -- 4.3 Overall Architecture -- 4.4 Implementation Details -- 4.5 Industrial Use Cases -- 4.6 Other Auto-Tuning Tools -- 5 Conclusion -- References -- Optimising Big Images -- 1 Introduction: Big Image Processing Tasks -- 1.1 Types of Image Processing Tasks -- 2 Regularisation of Inverse Problems -- 3 Non-smooth Geometric Regularisers for Imaging -- 4 First-Order Optimisation Methods for Imaging -- 4.1 Remarks About Notation and Discretisation -- 4.2 Primal: FISTA, NESTA, etc. -- 4.3 Primal-Dual: PDHGM, ADMM, and Other Variants on a Theme -- 4.4 When the Proximal Mapping is Difficult -- 5 Second-Order Optimisation Methods for Imaging -- 5.1 Huber-Regularisation -- 5.2 A Primal-Dual Semi-smooth Newton Approach -- 5.3 A Note on Interior Point Methods -- 5.4 Methods for Non-convex Regularisers -- 6 Non-linear Operators and Methods for Iterative Regularisation -- 6.1 Inverse Problems with Non-linear Operators -- 6.2 Iterative Regularisation -- 7 Emerging Topics -- 7.1 Convex Relaxation -- 7.2 Decomposition and Preconditioning Techniques -- 8 Conclusions -- References -- 6 Interlinking Big Data to Web of Data -- Abstract -- 1 Introduction -- 2 Interlinking Tools -- 2.1 Silk -- 2.2 LIMES -- 2.3 LODRefine -- 3 Interlinking Process -- 4 A Case Study for Interlinking -- 5 Conclusions and Future Directions -- References -- Topology, Big Data and Optimization -- 1 Introduction -- 2 Topology -- 3 Persistence -- 3.1 Persistence Diagrams as Features -- 3.2 Cohomology and Circular Coordinates -- 4 Mapper -- 5 Optimization -- 6 Applications -- 6.1 Local to Global -- 6.2 Nonlinear Dimensionality Reduction -- 6.3 Dynamics -- 6.4 Visualization -- 7 Software and Limitations. , 8 Conclusions -- References -- 8 Applications of Big Data Analytics Tools for Data Management -- Abstract -- 1 Introduction -- 2 Big Data and Big Data Analytics -- 2.1 Big Data -- 2.2 Big Data Analytics -- 2.2.1 Principal Component Analysis -- 2.2.2 Fuzzy Logic -- 2.2.3 Fuzzy C-Means Clustering -- 2.2.4 Traditional Artificial Neural Networks -- 2.2.5 Traditional Genetic Algorithms -- 3 Applications of Data Analytics -- 3.1 Solar Energy Forecasting -- 3.2 Wind Energy Forecasting -- 3.3 Financial Data Analytics -- 3.3.1 Training Process -- 3.3.2 Biological Data Analytics -- 4 Conclusions -- Acknowledgments -- References -- 9 Optimizing Access Policies for Big Data Repositories: Latency Variables and the Genome Commons -- Abstract -- 1 Introduction -- 2 Information Commons and Latency Variables -- 3 Evolution and Design of the Genome Commons -- 4 Latency Analysis and the Genome Commons -- 5 Conclusion -- References -- 10 Big Data Optimization via Next Generation Data Center Architecture -- Abstract -- 1 Introduction -- 1.1 Challenges of Big Data Processing -- 1.2 DC Evolution: Limitations and Strategies -- 1.3 Vision on Future DC -- 2 DC3.0: HTC-DC -- 2.1 HTC-DC Overview -- 2.2 Key Features -- 2.3 Pooled Resource Access Protocol (PRAP) -- 2.4 Many-Core Data Processing Unit -- 2.5 NVM Based Storage -- 2.6 Optical Interconnects -- 2.7 DC-Level Efficient Programming Framework -- 3 Optimization of Big Data -- 4 Conclusions -- Acknowledgments -- References -- 11 Big Data Optimization Within Real World Monitoring Constraints -- Abstract -- 1 Introduction -- 2 Monitoring -- 2.1 General Definition -- 2.2 Dike Monitoring Example -- 3 Big Data Related Constraints to Monitoring -- 3.1 The Big Data in Monitoring -- 3.1.1 Abstraction Level of Interpreted Information -- 3.1.2 Temporal Issues -- 3.1.3 Growth of Available Data -- 3.2 Performance -- 3.3 Availability. , 3.4 Reliability -- 4 Solutions Within Constraints -- 4.1 Approaches in Optimization -- 4.1.1 Data Oriented Optimization -- 4.1.2 Analysis Oriented Optimization -- 4.1.3 System Architecture Oriented Optimization -- 4.1.4 Goal Oriented Optimization -- 4.2 Summary of Optimizations and Constraints -- 4.2.1 Impact on Other Constraints -- 5 Conclusion -- References -- Smart Sampling and Optimal Dimensionality Reduction of Big Data Using Compressed Sensing -- 1 Introduction -- 2 Background -- 2.1 Notation and Fundamental Knowledge -- 2.2 Compressed Sensing and Sparse Reconstruction -- 3 Methodology -- 3.1 Feature Extraction -- 3.2 Smart Sampling -- 3.3 Classification and Evaluation -- 3.4 Dependance of Random Projections to Data Sparsity in Classification Problems -- 4 Experiments -- 4.1 Dataset Description -- 4.2 Sensitivity of Compressed Sensing to Randomness -- 4.3 Compressibility Estimation and Optimal Dimensionality Investigation -- 4.4 Investigating the Robustness of Hierarchical Compressed Sensing -- 4.5 Dependance of Random Projections to Data Sparsity -- 4.6 Comparison with Principal Component Analysis -- 5 Related Work -- 5.1 Dimensionality Reduction and Data Compression -- 5.2 Pattern Analysis Using Compressed Sensing -- 6 Conclusions -- References -- Optimized Management of BIG Data Produced in Brain Disorder Rehabilitation -- 1 Introduction -- 2 Scientific Dataspace Model -- 2.1 e-Science Life Cycle Activities -- 2.2 The Environment of Dataspaces in e-Science---a BIG Data Challenge -- 2.3 Relationships in the Scientific Dataspace -- 2.4 The e-Science Life Cycle Ontology and Towards Conceptualization in the Brain Damage Restoration Domain -- 3 Data Capture and Processing Model -- 3.1 Use-Cases -- 3.2 Indoor Treatment -- 3.3 Outdoor Treatment -- 3.4 Data Model -- 3.5 Event-Based Data -- 4 Towards the BDRI-Cloud. , 4.1 Motivation and System Usage -- 4.2 System Design -- 4.3 Architecture---a Service-Level View -- 5 Data Analysis and Visualization Services -- 5.1 Sequential Pattern Mining -- 5.2 Association Rule Mining -- 6 Conclusions -- References -- Big Data Optimization in Maritime Logistics -- 1 Introduction -- 2 Liner Shipping Network Design -- 2.1 Container Routing -- 3 Mat-Heuristic for Liner Shipping Network Design -- 4 Computational Results Using LINER-LIB -- 5 Empty Container Repositioning -- 5.1 Path Flow Formulation -- 6 Container Vessel Stowage Plans -- 6.1 Mathematical Model -- 7 Bunker Purchasing -- 7.1 Bunker Purchasing with Contracts -- 8 The Vessel Schedule Recovery Problem -- 8.1 Definitions -- 8.2 Mathematical Model -- 9 Conclusion and Future Challenges -- References -- Big Network Analytics Based on Nonconvex Optimization -- 1 Introduction -- 2 Network Issues, Properties and Notations -- 2.1 Issues Concerning Network Analytics -- 2.2 Eminent Properties of Network -- 2.3 Graph Based Network Notation -- 3 Introduction to Nonconvex Optimization and Evolutionary Computation -- 3.1 What is Optimization -- 3.2 How to Tackle Optimization Problems -- 4 Community Structure Analytics -- 4.1 Description of Community Discovery -- 4.2 Qualitative Community Definition -- 4.3 Existing Approaches for Community Discovery -- 5 Optimization Models for Community Structure Analytics -- 5.1 Single Objective Optimization Model -- 5.2 Multi-objective Optimization Model -- 6 Network Data Sets -- 6.1 Artificial Generated Benchmark Networks -- 6.2 Real-World Networks -- 6.3 Famous Websites -- 7 Experimental Exhibition -- 8 Concluding Remarks -- References -- 16 Large-Scale and Big Optimization Based on Hadoop -- Abstract -- 1 Introduction -- 2 Air Traffic Flow Optimization -- 2.1 Problem Formulation -- 2.2 Dual Decomposition Method. , 3 Hadoop MapReduce Programming Model.