Big data analytics has generated much research attention in the past decade, focusing on the architectural and methodological challenges of processing enormous datasets and extreme rates of data generation and collection (two of the three “Vs” of this field: volume and velocity).

As is typical for a major field of information technology, research conferences on this topic emerged in the late 1990s and have grown larger and more frequent, with prominent professional, academic, and industry sponsors. Big data analytics: methods and applications is a collection of key research papers inspired by one such conference, STAT13 [1], held in India to mark the International Year of Statistics. The editors of this volume have assembled 13 papers by big data experts who participated in that conference, covering analytics and algorithms, benchmarks, use cases, problem statements, and solutions.

The introduction to the edition by the editors highlights the statistical terminology and concepts associated with big data analytics along with its inherent technical and nontechnical issues, infrastructure, data complexity, and privacy concerns.

The contributed papers presented in the collection include analyses of the major challenges of the field, including examples of massive data analytics using relevant tools and methods, statistical challenges for decision making, visualization methods and innovations, performance analysis of big data systems, complex event processing, and platforms for the evolving area of sensor-generated data (the Internet of Things). Such a gathering of papers into a single resource provides a rich set of models for future research efforts. However, not surprisingly, there is no overall index for the book, which would help readers find specific terms. The book could serve as supplemental recommended reading for graduate data analytics courses; the use cases are varied and comprehensive.