This
book covers three major parts of Big Data: concepts, theories and
applications. Written by world-renowned leaders in Big Data, this book explores
the problems, possible solutions and directions for Big Data in research and
practice.  It also focuses on high level concepts such as definitions
of Big Data from different angles; surveys in research and applications; and
existing tools, mechanisms, and systems in practice. Each chapter is independent
from the other chapters, allowing users to read any chapter directly.

After examining the practical side
of Big Data, this book presents theoretical perspectives. The theoretical
research ranges from Big Data representation, modeling and topology to
distribution and dimension reducing.  Chapters also investigate the many
disciplines that involve Big Data, such as statistics, data mining, machine
learning, networking, algorithms, security and differential geometry. The last
section of this book introduces Big Data applications from different
communities, such as business, engineering and science.

Big Data Concepts, Theories and Applications is designed as a reference for researchers
and advanced level students in computer science, electrical engineering and
mathematics. Practitioners who focus on information systems, big data, data
mining, business analysis and other related fields will also find this
material valuable.