Real Time Interpretation and Optimization of Time Series Data Stream in Big Data

被引：0
|
作者：
Jiang, Zheyuan [1 ]
Liu, Ke [1 ]
机构：
[1] Hefei Univ Technol, Sch Comp & Informat, Hefei, Anhui, Peoples R China
来源：
2018 IEEE 3RD INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYSIS (ICCCBDA) | 2018年
基金：
中国国家自然科学基金;
关键词：
ARTMMR algorithm; time series data stream; real-time interpretation; optimization;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In view of massive historical data and high-speed time series data stream in the futures market program trading, how to mine related data and explain data real-timely, this paper proposes a real-timely proceeding model for massive data based on ARTMMR algorithm. The ARTMMR algorithm obtains frequent itemsets by using the parallelism of MapReduce, which saves the storage space and decreases the time overhead. The CPU utilization is improved by using Batch algorithm and thread calling algorithm, it meets real-time processing requirement and excavate trading opportunities or feature model according to the requirements of traders. The results indicate that the model can not only explain the time series data stream real-timely, but also help traders analyze data quickly and achieve accuracy trade-offs.
引用
收藏
页码：243 / 247
页数：5
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