In past few years, big data analytics was seen in many areas including social computing, services, Internet of Things, sensor networks, telecommunications, biology, health-care, and cloud. It encompasses knowledge, theory, and experience from a wide range of areas including data mining, machine learning, statistics, optimization, operation, information retrieval, and big data management, among others. It has become essential for mining large data sets for knowledge discovery, and converting data into actionable intelligence, be it data available to industry, academia, Government or on the Web. It has become a driving force for many recent breakthrough achievements, from biological science and economics to environmental science and social networking. However, while enjoying its power, there are still many associated data analytics challenges arising from data capture, creation, storage, search, sharing, modeling, analysis, and visualization. This special issue aims at providing an in-depth discussion of the latest academic and industrial research findings of big data analytics, and is especially concerned with how model-driven approaches, theories, or empirical applications can be used to address various challenges arising from exploiting large data sets.