Introduction

Introduction

The recent development in Artificial Intelligence (AI) and Big Data technologies and the dramatic increase in adoption of these technologies signify an ongoing and exponentially growing demand to both storage capacity and for computational processing power vis-à-vis the broader adoption of these technologies.

Big Data technologies such as the Hadoop framework (notably its MongoDB, HDFS and Spark databases ) require vast amounts of storage capacity , either in a centralized or a distributed manner, for processing and managing Big Data files. To a large extent, Big Data technologies support the exponential growth of data in any type of organization , within web based services and social networks and their implementation is essential to support the proper operation and processing of these vast amounts of data (see Fig. 1).

Machine learning and deep learning processes (notably Google’s TensorFlow, Caffe and Theano; see also: Dean et al., 2012, Ray, 2017) carry out advanced computational pattern recognition, image recognition and predictive analytics that require high volume of computations . The scenario of an exponentially growing demand for both Big Data and AI capabilities is solid and highly tangible , given that both technological areas are the basis to support IoT and Industry 4.0 systems . Additionally , though Big Data and AI technologies are only at their infant stages of implementation, most of the corporates and public institutes have begun examining their application to improve many aspects of their operations.