Computational Intelligence for Big Data Analysis

A book by D.P. Acharjya, Satchidananda Dehuril, Sugata Sanyal, ISBN

The role of adaptation, learning and optimization are becoming increasingly essen- tial and intertwined. The capability of a system to adapt either through modification of its physiological structure or via some revalidation process of internal mecha- nisms that directly dictate the response or behavior is crucial in many real world applications.

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Computational Intelligence for Big Data Analysis by D.P. Acharjya, Satchidananda Dehuril, Sugata Sanyal

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The amount of data collected from various sources is expected to double every two years. It has no utility unless these are analyzed to get useful information and it necessitates the development of techniques which can be used to facilitate big data analysis. The transformation of big data into knowledge is by no means an easy task. Expressing data access requirements of applications and designing programming language abstractions to exploit parallelism are an immediate need. In addition, these data may involve uncertainties. Big-data analysis, intelligent and cloud com- puting are active areas of current research for their potential application to many real life problems. Therefore, it is challenging for human beings to analyze and extract expert knowledge from a universe due to lack of computing resources available. More importantly, these new challenges may comprise, sometimes even deteriorate, the performance, efficiency, and scalability of the dedicated data intensive computing systems. In addition, fast processing while achieving high performance and high throughput, and storing it efficiently for future use is another issue. The objective of this edited book is to provide the researchers and practitioners the recent advances in the fields of big-data analysis and to achieve these objectives, both theoretical advances, and its applications to real life problems, case studies are stressed upon. This will stimulate further research interest in big data analytics. Moreover, it will help those researchers who have interest in this field of big data analysis and cloud computing and their importance for applications in real life. The book is comprised of three sections. The first section is an attempt to provide an insight on theoretical foundation on big data analysis that includes scalable architecture for big data processing, time series forecasting for big data, hybrid intelligent techniques, and applications to decision making by using neutrosophic sets. The second section discusses architecture for big data analysis and its applications whereas final section discusses the issues pertaining to cloud computing.