The pervasive nature of big data technologies as witnessed in industry services and everyday life has given rise to an emergent, data-focused economy stemming from many aspects of industrial applications. The richness and vastness of these services are creating unprecedented research opportunities in a number of industrial fields including public health, urban studies, economics, finance, social science, and geography. We are moving towards the era of Big Data Services, which are deployed in a multi-scale complex distributed architecture. These services can form high-level computational intelligence based on emerging analytical techniques such as big data analytics and web analytics. In this context, computational intelligence employs software tools from advanced analytics disciplines such as data mining, predictive analytics, and machine learning. At the same time, it becomes increasingly important to anticipate technical and practical challenges and to identify best practices learned through experience.

The goal of this special session is to present both novel solutions to challenging technical issues as well as compelling industrial systems. This special session will share research works and related practical experiences to benefit the reader, and will provide clear proof that big data services are playing an ever-increasing important and critical role in supporting computational intelligence for industry systems. It has also established a new cross-discipline research topic in computer science and industry engineering. Topics of interest include, but are not limited to:

·         Novel theoretical and computational models for big data services

·         Requirement engineering of big data services for computational intelligence

·         Interoperability of heterogeneous big data services for computational intelligence

·         Big data analysis as a service

·         Mobile analysis as a service

·         Industrial wireless sensor networks as a service

·         Multi-tenant Business process as a service

·         Variability and configuration in large process repositories

·         Big data services processing in industrial networks

·         On-demand big data services selection, composition, and provisioning for computational intelligence

·         Context-aware big data service management and processing for computational intelligence

·         Scalable and efficient architectures and algorithms of big data services for computational intelligence

·         Security and privacy issues on big data services

·         Business and societal aspects of big data services for computational intelligence

·         Simulation and experiment of big data services for computational intelligence