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Big data over networks
Cui S., Hero A., Luo Z., Moura J., Cambridge University Press, New York, NY, 2016. 457 pp. Type: Book (978-1-107099-00-5)
Date Reviewed: Nov 15 2016

It is always hard to provide a balanced review of an edited book with varied contributions. In this case, it is no different. Four editors, all of them IEEE Fellows, have collected 15 chapters written by 50 different authors that provide different perspectives on large-scale information processing on, and over, networks. The editors arguably claim that they address three complementary angles to study the interactions between big data and networks: how the underlying network constrains big data processing, how big data processing can help improve network performance, and fundamental limits in the analysis of big data. The contributed chapters, however, are not organized around these three points of view. They revolve around application domains.

The first part of the book surveys mathematical tools for big data processing. It serves as a formal introduction to some of the tools that can be used in specific application domains. In particular, the four chapters in this part of the book delve into tensor models (that is, n-dimensional arrays), sparsity-aware distributed learning methods (that is, regularized regression models), optimization algorithms (that is, the alternating direction method of multipliers (ADMM)), and distributed algorithms for game theoretical models (as those used in multiagent systems). Even when not all of those mathematical techniques are used in later chapters, they still provide some useful information and can serve as inspiration to aspiring researchers, who might find novel situations where such tools can be helpful in practice.

The second part of the book focuses on cyber networks. Again, four contributed chapters provide a bird’s-eye perspective on the interaction of big data processing and man-made networks. The first chapter, written by two Microsoft researchers, is an excellent survey of the issues surrounding task scheduling and data storage in big data systems. Later, two chapters focus on wireless networks: the first one on infrastructure issues (namely, distributed data storage) and the second one on the challenges and opportunities in the design of scalable wireless systems. The fourth chapter in this part delves into another challenge: security in the smart grid, the next-generation electric power distribution system.

The third part of the book turns its attention toward social networks. Two chapters focus on specific kinds of social networks, whereas the last chapter surveys different methods for measuring influence in networks, starting from well-known network structural properties and ending with the study of influence propagation and maximization. The two chapters that precede this noteworthy survey deal on the one hand with the study of infrastructure and mobility networks in cities, and on the other hand with topic modeling in Twitter, using tweets related to the Arab Spring revolutions and combining network analysis algorithms with text mining techniques such as latent Dirichlet allocation (LDA).

The fourth and final part of the book shifts its attention to the analysis of biological networks. Four chapters cover inference validation based on defining a distance between networks, factor models for gene expression analysis, inference of biological network properties from big data, and the limits to information extraction from correlation mining. These chapters might be more application specific than chapters in the previous parts of the book, whose results can be extrapolated to alternative application domains more easily; however, readers might still find them useful for completing a variety of perspectives for an inherently interdisciplinary field.

This book collects contributions from experts in fields that range from large-scale computing and machine learning to mathematical optimization, wireless communications, and molecular biology. I would not recommend it as a textbook mainly because the different chapters are self-contained in varying degrees. They range from really outstanding readable surveys for the uninitiated to less clarifying mathematical treatments of current research issues.

However, the book chapters distill some useful information for graduate students and researchers in the field, information that might be more difficult to obtain just by reading conventional research papers. In general, beyond merely describing the state of the art in their own specialties, the authors try to identify open problems and current research challenges. Accompanied by hundreds of bibliographic references to relevant work in each area, the chapters in this book might be useful for those trying to identify potentially fruitful research opportunities at the confluence of big data and networks. So, in the end, I would recommend this book, certainly not as a textbook, but as a road map for graduate students and researchers who intend to make inroads into this fascinating research field.

Reviewer:  Fernando Berzal Review #: CR144923 (1702-0096)
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