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Jiang, H., Chen, Y., Qiao, Z., et al. (2015) Scaling up MapReduce-Based Big Data Processing on Multi-GPU Systems. Cluster Computing, 18, 369-383.
https://doi.org/10.1007/s10586-014-0400-1

has been cited by the following article:

• TITLE: Study on Massive Vegetation Data Processing of FY-3 Based on RAM (h)

  AUTHORS: Manyun Lin, Xiangang Zhao, Cunqun Fan, Lizi Xie, Lan Wei

  KEYWORDS: Meteorological Satellite, Vegetation Data, RAM (h), Massive Data Processing

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.5 No.4, April 25, 2017

  ABSTRACT: The vegetation data of the Fengyun meteorological satellite are segmented according to the latitude and longitude, and can be written into 648 blocks. However, the vegetation data processing efficiency is low because the data belongs to massive data. This paper presents a data processing method based on RAM (h) for Fengyun-3 vegetation data. First of all, we introduce the Locality-Aware model to segment the input data, then locate the data based on geographic location, and finally fuse the independent data based on geographical location. Experimental results show that the proposed method can effectively improve the data processing efficiency.