Edited by Ali Emrouznejad, this book is volume 18 in Springer’s “Studies in Big Data” series. The inside cover of the book states that the series publishes big-data-related developments, quickly and with high quality.

The book I reviewed was published on high-quality paper with color diagrams and pictures as needed. It is composed of 20 chapters. The common theme throughout the chapters is a focus on optimization. The overall content, although diverse, is substantive and informative. This book can be used by both beginning and experienced individuals interested in big data analytics, with a special interest in optimization, since it covers a broad spectrum of areas in big data optimization. More specifically, the first two chapters could be most useful for beginning researchers. The rest of the chapters cover very specific areas; expertise in these areas is needed to benefit from each chapter. Due to space limitations and the very specific nature of each chapter, I will elaborate on the introductory chapters in more detail than the rest. However, for each chapter, I provide enough insight to highlight the critical part of the content for the potential expert in that field.

The first chapter, written by the editor Emrouznejad and Marianna Marra, is “Big Data: Who, What and Where? Social, Cognitive and Journals Map of Big Data Publications with Focus on Optimization.” It is beautifully written using scientometric mapping techniques. Wikipedia correctly defines “scientometrics” as “the study of measuring and analyzing science, technology and innovation.” Scientometric mapping is a method largely developed starting around the mid-1970s. It provides quantitative clustering of information according to certain selected criteria; as such, it is a kind of effective meta-analysis technique. The authors of this chapter use these techniques very effectively and produce a wealth of global information about big data and optimization. They also provide an extensive and critical citation list of 47 references. The second chapter, “Setting Up a Big Data Project: Challenges, Opportunities, Technologies and Optimization,” sets up a big data project, as the title implies. Two use cases demonstrate the value of big data projects for organizations.

The length of each chapter is, on average, about 30 pages. Chapter 3, “Optimizing Intelligent Reduction Techniques for Big Data,” presents descriptive analytics, predictive analytics, and prescriptive analytics as big data reduction techniques; a case study is also included. Chapter 4, “Performance Tools for Big Data Optimization,” reviews perspectives of performance tools, including ideal tool requirements and challenges posed by performance tools; state-of-the-art performance tool examples are also included. Chapter 5, “Optimizing Big Images,” reviews the current state of the art and future challenges in optimizing methods for image processing. In chapter 6, “Interlinking Big Data to Web of Data,” the linked data approach is shown to introduce advantages over big data processing as islands. This chapter reviews the advantages of linked data approaches. In chapter 7, “Topology, Big Data and Optimization,” the recent use of topological techniques in big data analysis produces promising results. This chapter introduces the use of topological approaches as supplementary techniques to existing big data analysis and optimization approaches.

Chapter 8, “Applications of Big Data Analytics Tools for Data Management,” is a very comprehensive chapter dealing with big data management issues, including applications to wind, solar, biological, and financial data. I found the presentation of these four different applications very informative. Chapter 9, “Optimizing Access Policies for Big Data Repositories: Latency Variables and the Genome Commons,” is unique in that the author holds a JD degree. The chapter treats policy issues and the methodology proposed deals with the timing of data releases, which is applied to genomic data. Chapter 10, “Big Data Optimization Via Next Generation Data Center Architecture,” introduces a high-level description of a next-generation data center architecture. The distinguishing aspect of the proposal is its emphasis on openness and service layer flexibility. Chapter 11, “Big Data Optimization Within Real World Monitoring Constraints,” discusses different approaches in optimization. The authors differentiate between data, analysis, system architecture, and goal-oriented optimization, and introduce a different perspective on real-time monitoring. Chapter 12, “Smart Sampling and Optimal Dimensionality Reduction of Big Data Using Compressed Sensing,” offers a detailed presentation of dimensionality reduction using a relatively narrow concept of compressed sensing. A new hierarchical compressed sensing approach is also proposed.

Chapter 13, “Optimized Management of BIG Data Produced in Brain Disorder Rehabilitation,” introduces a unique experiment in dealing with the optimized management of brain disorder rehabilitation. The authors provide a comprehensive review of the area, together with a detailed account of their approach. Chapter 14, “Big Data Optimization in Maritime Logistics,” covers predictive big data analytics and prescriptive optimization techniques applied to maritime logistics. In chapter 15, “Big Network Analytics Based on Nonconvex Optimization,” the authors investigate single and multiple objective optimization models. They demonstrate the effectiveness of these approaches in big data community analytics. In chapter 16, “Large-Scale and Big Optimization Based on Hadoop,” the large-scale integer linear programming (ILP) problem is considered and a Hadoop-based strategy is proposed to address the growth of the well-known computation problem in ILP. Chapter 17, “Computational Approaches in Large-Scale Unconstrained Optimization,” discusses line search-based approaches in large-scale unconstrained optimization. A detailed comparative analysis is missing in the discussion. Chapter 18, “Numerical Methods for Large-Scale Nonsmooth Optimization,” tackles nonsmooth optimization using numerical methods. Non-differentiable functions pose a problem in the identification of optimality points. The large-scale numerical experiments presented in the chapter indicate the usability of these methods in real-life problems. Chapter 19, “Metaheuristics for Continuous Optimization of High-Dimensional Problems: State of the Art and Perspectives,” presents a survey of successful algorithms in the field of high-dimensional continuous optimization. In addition, future research directions are identified. Chapter 20, “Convergent Parallel Algorithms for Big Data Optimization Problems,” presents a decomposition framework for designing parallel algorithms and provides practical implementation guidelines.

In conclusion, these days it is not common for an edited book to be useful to anyone other than the specialists targeted. This edited book is an exception. 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.