In business intelligence (Bl) and Big Data analytics, computational intelligence (CI) paradigms have been adopted as an intelligent decision support system for prediction and optimization in a variety of applications. Traditional data analysis approaches are lacking in efficiency, have a limited computational capability, and have an inadequate and imprecise nature of handling unstructured data. However, CI methodologies have a high computational efficiency to integrate, explore, and share a high volume of unstructured data in real time, using diverse analytical techniques for enhanced decision making. Furthermore, CI has the capability to implement complex data through sophisticated mathematical models and analytical techniques. Recently, CI approaches have been applied to a wide variety of complex problems, including engineering, science, and business. However, because of the complexity and uncertainty in these problems, it can be difficult to find the optimal solution. Hence, there is a great need to explore in depth the various CI techniques in conjunction with data analytics to support BI and adaptive computational models for solving real-world problems. Computational Intelligence Applications in Business Intelligence and Big Data Analytics' main objective is to explore novel contributions that bridge the gap between applying CI to Bl, data analytics processes, and the overall business success of the organization. This book focuses on: Fundamentals of Cl approaches, metaheuristic approaches, and related research in Big Data analytics, Issues related to data analytics of various applications as well as methodologies and processes involved in organizational decision-making, Tools for data analytics, such as Hadoop, NoSQL, and MongoDB, and tools for Bl and its applications, Using CI paradigms in applications and using heuristics and data analysis technologies in various application domains including video surveillance, medical informatics, smart homes, business process intelligence, and decision-making The book promotes insight to the hybridization of CI techniques and advances in the design of computational models, algorithms, and case studies, which can aid in finding novel solutions to contemporary complex problems. Book jacket.