关键词: 时间序列分析, 有损压缩, 多粒度数据挖掘, 信息抽象模型, 边缘计算

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