ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2018, Vol. 55 ›› Issue (8): 1653-1666.doi: 10.7544/issn1000-1239.2018.20180219

Special Issue: 2018数据挖掘前沿进展专题

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Variant Entropy Profile: A Multi-Granular Information Model for Data on Things with Order-of-Magnitude Compression Ratios

Chao Lu1,2,3, Peng Xiaohui1,Xu Zhiwei1   

  1. 1(Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190);2(University of Chinese Academy of Sciences, Beijing 100049);3(Intelligent Processor Research Center (Institute of Computing Technology, Chinese Academy of Sciences), Beijing 100190)
  • Online:2018-08-01

Abstract: In recent years, the massive produced data by the devices of edges and things has brought new paradigms like edge computing and things computing to apply in the Internet of things, which can optimize the performance and energy consumption by moving the computation tasks to the data source as near as possible. However, innumerous resource-constrained devices of things expose two defects of current paradigms, which are computations cannot be offloaded to the endpoint due to the lack of massive data storage capacity, and the redundant computation and storage for raw data bring overheads due to the lack of multi-granular information support for various application demands. To address these two issues, this article proposes a multi-granular information model for data on things with order-of-magnitude compression ratios, called variant entropy model (VEP), and implements a prototype storage module of TSR-VEP. Evaluations on the real smart meter datasets and benchmarks show that VEP can achieve order-of-magnitude compression ratios and multi-granular information storage and query under low application observed errors. Discussion on the test results demonstrates the feasibility of applying VEP on devices of things and the potential of further optimizing for edge computing and things computing.

Key words: time series analysis, lossy compression, multi-granular data mining, information abstraction model, edge computing

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