A spatiotemporal compression based approach for efficient big data processing on Cloud

作者：

Highlights：

• Spatial and temporal compression for big graph data storage and processing on Cloud.

• Temporal compression for reducing data from a single node in a graph.

• Spatial compression for reducing data from correlated nodes in a graph.

• Significant time performance gains achieved by a novel scheduling on Cloud.

• Trade off between data quality and processing efficiency being guaranteed.

摘要

•Spatial and temporal compression for big graph data storage and processing on Cloud.•Temporal compression for reducing data from a single node in a graph.•Spatial compression for reducing data from correlated nodes in a graph.•Significant time performance gains achieved by a novel scheduling on Cloud.•Trade off between data quality and processing efficiency being guaranteed.

论文关键词：Big data,Graph data,Spatiotemporal compression,Cloud computing,Scheduling

论文评审过程：Received 16 May 2013, Revised 10 December 2013, Accepted 10 April 2014, Available online 24 April 2014.

论文官网地址：https://doi.org/10.1016/j.jcss.2014.04.022