Introduces the main characteristics of big data, i.e. the five Vs-Volume, Velocity, Variety, Veracity, and Variability

 Presents popular types of traditional machine learning in terms of their key features and limitations in the context of big data

 Discusses the need for and different uses of granular computing based machine learning

 Presents several case studies of big data by using biomedical data and sentiment data, demonstrating recent advances

 Stresses the theoretical significance, practical importance, methodological impact, and philosophical aspects