Big data are data with large volume, fast and dynamic generation, and diversity of data formats. Their management, storage, retrieval and processing are challenging due to these features. In distributed computing environment, the MapReduce pattern and its Haddoop and Smark implementations are widely used for big data computing. However, they are not directly suitable for many real-world applications such as some bioinformatics problems and other all-to-all comparison problems. Also, the efficient utilisation and scheduling of the resources of each of the distributed machines are still a challenging task for big data computing.
This research focuses on development of innovative theory, new front-end programming models and back-end technology support for big data computing in distributed or cloud environments. It also investigates applications of big data computing in real-world systems.
The expected outcomes of this research include innovative theory, new front-end programming models and back-end resource management/scheduling technologies for big data computing in distributed or cloud environments. They also include implementations of big data computing for real-world systems.
Skills and experience
The following skills and experience are essential: general computing and software development, programming skills in Java and C/C++, knowledge of optimization and its heuristic solutions.
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Contact the supervisor for more information.