Big Data Analytic via Soft Computing Paradigms

Large sets of data have 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.” System of Systems (SoS) integrate independently operating, nonhomogeneous systems to achieve a higher goal than the sum of the parts. Today’s SoS are also contributing to the existence of unmanageable “Big Data.” Recent efforts have developed a promising approach, called “data analytic,” which uses statistical and computational intelligence (CI) tools such as principal component analysis (PCA), clustering, fuzzy logic, neuro-computing, evolutionary computation, Bayesian networks, 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, and (c) eventually develop a nonparametric model for the “Big Data.” This chapter attempts to construct a bridge between SoS and data analytic to develop reliable models for such systems. The first application prediction of the stock market close is presented using a neural network paradigm. In the second application, a photovoltaic energy forecasting problem of a micro-grid SoS will be offered here for a case study of this modeling relation. The given input data represent the market price of the years which are between 2012 and 2013. The real exchange rate value of the NASDAQ stock market index is used. The results in both applications are quite promising.