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    Challenges in Big Data Architecture and Systems
    Update time: 2011-06-10
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    Speaker: Dr. Jian Li, IBM Research
    Time: 09:00am—10:30am, June 14th, 2011 (Tuesday)
    Place: Meeting Hall, 4th Floor, ICT, CAS
    Data continue a massive expansion in scale, diversity, and complexity -- hence Big Data. Data underpin activities in all sectors of society. As informatics is the science of information, analytics is the science of data analysis. Achieving the full transformative potential from the use of data in this increasingly digital world requires not only new data analysis algorithms but also a new generation of systems and distributed computing environments to handle the dramatic growth in the volume of data, the lack of structure for much of it and the increasing computational needs of massive-scale analytics. Not only is there a need for new environments and platforms, but even there is even a lack of metrics and benchmarks to evaluate them. In this talk, we first outline the industry trend that moves to Big Data systems. We then enumerate several challenges in building such systems. Finally, we discuss possible ways to engineering efficient Big Data systems.
    Jian Li is a research staff member at IBM Research in Austin. He holds a Ph.D. degree in Electrical and Computer Engineering from Cornell University. He has worked and published papers and patents in the areas of: architectural support for power- and variation-aware computing, interconnection network design for high-performance computing systems, workload-driven three-dimensional (3D) integration architecture, architectural applications of non-volatile memory (NVM) and storage class memory (SCM), energy-efficient interconnection networks, data center networks, workload optimized systems and analytics, with a strong emphasis on entertaining his family members in Austin, Texas. He enjoys working with researchers and colleagues both within and outside of IBM. He serves as an adjunct professor at the Texas A&M University.

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