A Selective Review on Statistical Methods for Massive Data Computation:
  Distributed Computing, Subsampling, and Minibatch Techniques
Xuetong Li Yuan Gao Hong Chang Danyang Huang Yingying Ma Rui Pan Haobo Qi Feifei Wang Shuyuan Wu Ke Xu Jing Zhou Xuening Zhu Yingqiu Zhu Hansheng Wang
This paper presents a selective review of statistical computation methods for
massive data analysis. A huge amount of statistical methods for massive data
computation have been rapidly developed in the past decades. In this work, we
focus on three categories of statistical computation methods: (1) distributed
computing, (2) subsampling methods, and (3) minibatch gradient techniques. The
first class of literature is about distributed computing and focuses on the
situation, where the dataset size is too huge to be comfortably handled by one
single computer. In this case, a distributed computation system with multiple
computers has to be utilized. The second class of literature is about
subsampling methods and concerns about the situation, where the sample size of
dataset is small enough to be placed on one single computer but too large to be
easily processed by its memory as a whole. The last class of literature studies
those minibatch gradient related optimization techniques, which have been
extensively used for optimizing various deep learning models.