Big-Data Analytics for Cloud, IoT and Cognitive Computing

Preface

Motivations and Objectives

In the past decade, the computer and information industry has experienced rapid changes in both platform scale and scope of applications. Computers, smart phones, clouds and social networks demand not only high performance but also a high degree of machine intelligence. In fact, we are entering an era of big data analysis and cognitive computing. This trendy movement is observed by the pervasive use of mobile phones, storage and computing clouds, revival of artificial intelligence in practice, extended supercomputer applications, and widespread deployment of Internet of Things (IoT) platforms. To face these new computing and communication paradigm, we must upgrade the cloud and IoT ecosystems with new capabilities such as machine learning, IoT sensing, data analytics, and cognitive power that can mimic or augment human intelligence.

In the big data era, successful cloud systems, web services and data centers must be designed to store, process, learn and analyze big data to discover new knowledge or make critical decisions. The purpose is to build up a big data industry to provide cognitive services to offset human shortcomings in handling labor-intensive tasks with high efficiency. These goals are achieved through hardware virtualization, machine learning, deep learning, IoT sensing, data analytics, and cognitive computing. For example, new cloud services appear as Learning as a Services (LaaS), Analytics as a Service (AaaS), or Security as ...