Description
We have witnessed the dramatic increase of the use of information technology in every aspect of our lives. For example, Canada’s healthcare providers have been moving to electronic record systems that store patients’ personal health information in digital format. These provide healthcare professionals an easy, reliable, and safe way to share and access patients’ health information, thereby providing a reliable and cost-effective way to improve efficiency and quality of healthcare. However, e-health applications, together with many others that serve our society, lead to the explosive growth of data. Therefore, the crucial question is how to turn the vast amount of data into insight, helping us to better understand what’s really happening in our society. In other words, we have come to a point where we need to quickly identify the trends of societal changes through the analysis of the huge amounts of data generated in our daily lives so that proper recommendations can be made in order to react quickly before tragedy occurs. This brand new challenge is named Big Data.
Big Data is emerging as a very active research topic due to its pervasive applications in human society, such as governing, climate, finance, science, and so on. In 2012, the Obama administration announced the Big Data Research and Development Initiative, which aims to explore the potential of how Big Data could be used to address important problems facing the government. Although many research studies have been carried out over the past several years, most of them fall under data mining, machine learning, and data analysis. However, these amazing top-level killer applications would not be possible without the underlying support of network infrastructure due to their extremely large volume and computing complexity, especially when real-time or near-real-time applications are demanded.
To date, Big Data is still quite mysterious to various research communities, and particularly, the networking perspective for Big Data to the best of our knowledge is seldom tackled. Many problems wait to be solved, including optimal network topology for Big Data, parallel structures and algorithms for Big Data computing, information retrieval in Big Data, network security, and privacy issues in Big Data.
This book aims to fill the lacunae in Big Data research, and focuses on important networking issues in Big Data. Specifically, this book is divided into four major sections: Introduction to Big Data, Networking Theory and Design for Big Data, Networking Security for Big Data, and Platforms and Systems for Big Data Applications.