Granular Computing is a general computation approach for effectively using granules such as classes, clusters, sets, groups and intervals to build an efficient computational model for complex applications with huge amounts of data, information and knowledge. Though the label is relatively recent, the notions and principles of Granular Computing and Information Granulation, under different names, have appeared in many related fields, such as information hiding in programming, granularity in artificial intelligence, divide and conquer paradigms in theoretical computer science, interval computing, cluster analysis, fuzzy and rough set systems, neutrosophic computing, quotient spaces, belief functions, approximate analytics, approximate computing, and many others.

Special Session on Information Granulation in Data Science and Scalable Computing will continue to address the issues related to Granular Computing and its applications. It will provide researchers from universities, laboratories and industry with the means to present state-of-the-art research results and methodologies in theory and applications. The session will also make it possible for scientists and developers to highlight their new research directions and new interactions with novel computing models. The session will focus particularly on currently important research tracks such as social network computing, cloud computing, cyber-security, data mining, machine learning, knowledge management, intelligent systems and soft computing (neural networks, fuzzy systems, evolutionary computation, rough sets, self-organizing systems), e-Intelligence (Web intelligence, semantic Web, Web informatics), bioinformatics and medical informatics.