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In the era of big data, developing modern computing systems and system software that can scale to massive amounts of data becomes a key challenge to both researchers and practitioners. Scalability in distributed system usually means that the performance of a system should increase proportionally with the increase in resources. However, this is not sufficient in the big data era. The system should be designed in a way so that all the five Vs of big data can be tackled. Driven by this insight, this special issue aims at presenting the current state-of-the-art research and future trends on various aspects of big data programming and system software techniques for big data processing and attempts towards building highly adaptive big data systems that can automatically adapt their behaviours to the amount of available resources. The major subjects cover methodologies, modelling, analysis and newly introduced applications. Besides the latest research achievements, this special issue also covers innovative commercial data management systems, innovative commercial applications of big data technology and experience in applying recent research advances to real-world problems. Papers selected for this special issue represent recent progresses in the field, including works on big data systems and optimizations, novel techniques for big data

Advances in big data programming, system software and HPC convergence