Preface

The recent pursuits emerging in big data processing, interpretation, collection, and organization have emerged in numerous sectors including business, industry, and not-for-profit organizations. Data sets such as customer transactions for a megaretailer, weather monitoring, intelligence gathering can quickly outpace the capacity of traditional techniques and tools of data analysis. We have been witnessing an emergence of new techniques and tools including NoSQL databases, MapReduce, Natural Language Processing, Machine Learning, visualization, acquisition, and serialization. It becomes imperative to fully become aware what happens when big data grows up: how they are being applied and where they start playing a crucial role. We also need to become fully become aware of implications and requirements imposed on the existing techniques and various methods under development. Soft Computing regarded as a plethora of technologies of fuzzy sets (or Granular Computing, in general), neurocomputing, and evolutionary optimization brings forward a number of unique features that might be instrumental to the development of concepts and algorithms to deal with big data. In particular, setting up a suitable and fully legitimate level of abstraction by forming semantically meaningful information granules is of paramount relevance. In light of their sheer volume, big data may call for distributed processing, where results of intensive data mining realized locally are afterwards reconciled leading to information granules of higher type. Neurocomputing operating at information granules leads to more tractable learning tasks. Evolutionary computing delivers an essential framework supporting global optimization. In light of the inherent human-centric facet of Granular Computing the principles and practice of Computational Intelligence have been poised to play a vital role in the analysis, design, and interpretation of the architectures and functioning of mechanisms of big data. Our ultimate objectives of this edited volume is to provide the reader with an updated, in-depth material on the emerging principles, conceptual underpinnings, algorithms, and practice of Computational Intelligence in the realization of concepts and implementation of big data architectures, analysis, and interpretation as well as data analytics.