We are living in a world where vast volumes of scientific data are being produced in all kinds of disciplines, including astronomy, biology, medicine, and the social sciences to name a few. To cope with this explosive growth in data, the academic community is paying increasing attention to efficient data management and analytics tools, which mainly consider the preparation, experimentation, collection, results dissemination, and long-term storage and accessibility of data generated by technological processes. Existing solutions to problems involving large-scale data are typically concerned with complex mathematical modeling, simulation, and analysis by virtue of the high-performance computing environments provided by super computers or cloud computing facilities.

However, the recent advent of data-intensive science has opened up a new chapter in the field of scientific programming, with untapped information now being mined from large-scale data with the help of data intensive analytics platforms. The ease with which any and all information can be disseminated digitally in a cost-efficient and scalable manner is phenomenal. However, technological barriers exist within these opportunities due to the ever-increasing volume, velocity, and variety of information continually being generated, which poses a challenge to effective big data management and analysis in scientific programming.

Therefore, this special issue aims to collect original research articles that showcase novel analytical methods and applications related to scientific big data management and analysis in relation to scientific programming. Review articles that broadly discuss the nature of big data in scientific programming, alongside management and analytics challenges and commonly-used approaches, are also encouraged.

Potential topics include but are not limited to the following:

• Optimization technology for Spark/MapReduce
• Task scheduling algorithms in Spark/Hadoop
• Knowledge graph construction, visualization, and queries for scientific big data
• Machine learning and Spark applications in data processing
• Parallel and distributed big data analysis algorithms in scientific programming
• Scientific programming for large-scale data storage and management
• Stream data processing in scientific programming
• Performance optimization of distributed computing systems for scientific programming
• Geodistributed big data processing for scientific programming
• Data-intensive scientific workflow scheduling in geodistributed datacenters
• Deep learning and its optimization for big data analytics in scientific programming