Big data is data whose volume, diversity, and nature need to be shaped before meaningful insights can be extracted. As the volume of data increases, the use of advanced tools is needed to analyse data efficiently and to enable the visualisation of meaningful insights extracted from this data. The size of data is mostly increasing due to the advancements in the Internet of Things (IoT) as this involves different devices like sensors, wearables, actuators, and many others which produce large amounts of data. Industrial Internet of Things (IIoT) is an advanced version of IoT which deals with industries with relevant IoT.

Industries such as healthcare, education, and other organisations produce a huge amount of data that needs to be shaped in order to visualise the information for daily use. The production of big data in IIoT is evident due to huge developments in IoT devices. The processing of big data is a challenging issue due in part to limited resources like storage devices and limited computations. Scientific programming will play a significant role in providing solutions and mechanisms to such issues regarding big data in IIoT, such as by assisting in the processing of large volumes of data, complex system modelling, and sourcing derivations from IIoT data and simulations.

This Special Issue invites original research articles and review articles that explore the integration of big data and IIoT from a scientific programming perspective. Research that considers technological and computational barriers to big data management is particularly welcome.

Potential topics include but are not limited to the following:

• Big data and Internet of Things
• Big data and Industrial Internet of Things
• Scientific programming theories, methods, and tools for the convergence of artificial Intelligence (AI) and IIoT
• AI and IoT driven framework for ubiquitous sensing and data analytics
• Security, scalability, reliability, and portability in machine learning and IIoT
• Cost-benefit analysis of AI and IIoT enabled systems against legacy systems
• Testbeds, implementation, and practices of AI and IoT driven solutions in different application domains
• Big data in the healthcare industry
• Big data in education
• Big data, AI, and IIoT