References
Greenwald G, MacAskill E. NSA Prism program taps into user data of Apple, Google and others. The Guardian, 2013
He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. 2015, arXiv preprint arXiv:1512.03385
Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z F, Citro C, Corrado G S, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y Q, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mane D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vijay V, Viegas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X Q. TensorFlow: large-scale machine learning on heterogeneous systems. 2016, arXiv preprint arXiv:1603.04467
Barga R, Fontama V, Tok W H. Cortana analytics. In: Barga R, Fontama V, Wee Tok W H, eds. Predictive Analytics with Microsoft Azure Machine Learning. New York: Apress, 2015, 279–283
Author information
Authors and Affiliations
Corresponding author
Additional information
Lionel M. Ni is currently the Vice Rector (Academic Affairs) and Chair Professor of computer and information science at the University of Macau, China. He is serving on the editorial boards of Communications of the ACM and IEEE Transactions on Big Data.
Haoyu Tan is a research associate at Hong Kong University of Science and Technology (HKUST), China. He received the PhD degree in computer science and engineering from HKUST in 2013. His research interests include big data processing, large scale data mining, machine learning, and distributed systems.
Jiang Xiao is a research associate at Hong Kong University of Science and Technology (HKUST), China. She received the PhD degree in computer science and engineering from HKUST in 2014. Her research interests include wireless communication, mobile computing, indoor localization, and big data processing.
Rights and permissions
About this article
Cite this article
Ni, L.M., Tan, H. & Xiao, J. Rethinking big data in a networked world. Front. Comput. Sci. 10, 965–967 (2016). https://doi.org/10.1007/s11704-016-6902-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11704-016-6902-7