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The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.
List of contents
Big data: Who, What and Where? Social, Cognitive and Journals Map of Big Data Publications with Focus on Optimization.- Setting up a Big Data Project: Challenges, Opportunities, Technologies and Optimization.- Optimizing Intelligent Reduction Techniques for Big Data.- Performance Tools for Big Data Optimization.- Optimising Big Images.- Interlinking Big Data to Web of Data.- Topology, Big Data and Optimization.- Applications of Big Data Analytics Tools for Data Management.- Optimizing Access Policies for Big Data Repositories: Latency Variables and the Genome Commons.- Big Data Optimization via Next Generation Data Center Architecture.- Big Data Optimization within Real World Monitoring Constraints.- Smart Sampling and Optimal Dimensionality Reduction of Big Data Using Compressed Sensing.- Optimized Management of BIG Data Produced
in Brain Disorder Rehabilitation.- Big Data Optimization in Maritime Logistics.- Big Network Analytics Based on Nonconvex Optimization.- Large-scale and Big Optimization Based on Hadoop.- Computational Approaches in Large-Scale Unconstrained Optimization.- Numerical Methods for Large-Scale Nonsmooth Optimization.- Metaheuristics for Continuous Optimization of High-Dimensional Problems: State of the Art and Perspectives.- Convergent Parallel Algorithms for Big Data Optimization Problems.
Summary
The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.
Additional text
“It can be used as a reference book on big data, to obtain a broad view of the direction and landscape. In addition, it can be used by specialists in specific areas of big data, especially optimization-related areas. In this respect, the preview of chapter titles and brief explanations provided in this review reveal specific areas of interest for the intended specialists. I like this edited volume and recommend it.” (M. M. Tanik, Computing Reviews, January, 2017)
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"It can be used as a reference book on big data, to obtain a broad view of the direction and landscape. In addition, it can be used by specialists in specific areas of big data, especially optimization-related areas. In this respect, the preview of chapter titles and brief explanations provided in this review reveal specific areas of interest for the intended specialists. I like this edited volume and recommend it." (M. M. Tanik, Computing Reviews, January, 2017)
