Explores how granular computing plays a significant role in advancing machine learning towards in-depth processing of big data

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