Applications of Big Data Analytics Tools for Data Management

Abstract

Data, at a very large scale, has been accumulating in all aspects of our lives for a long time. Advances in sensor technology, the Internet, social networks, wireless communication, and inexpensive memory have all contributed to an explosion of “Big Data”. Our interconnected world of today and the advent of cyber-physical or system of systems (SoS) are also a key source of data accumulation- be it numerical, image, text or texture, etc. SoS is basically defined as an integration of independently operating, non-homogeneous systems for certain duration to achieve a higher goal than the sum of the parts. Recent efforts have developed a promising approach, called “Data Analytics”, which uses statistical and computational intelligence (CI) tools such as principal component analysis (PCA), clustering, fuzzy logic, neuro-computing, evolutionary computation, Bayesian networks, data mining, pattern recognition, deep learning, etc. to reduce the size of “Big Data” to a manageable size and apply these tools to (a) extract information, (b) build a knowledge base using the derived data, (c) optimize validation of clustered knowledge through evolutionary computing and eventually develop a non-parametric model for the “Big Data”, and (d) Test and verify the model. This chapter attempts to construct a bridge between SoS and Data Analytics to develop reliable models for such systems. Four applications of big data analytics will be presented, i.e. solar, wind, financial and biological data.