A Four-layer Architecture for Online and Historical Big Data Analytics

被引：1
|
作者：
Zhu, Julie Yixuan [1 ]
Xu, Jialing [1 ]
Li, Victor O. K. [1 ]
机构：
[1] Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China
来源：
2016 IEEE 14TH INTL CONF ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING, 14TH INTL CONF ON PERVASIVE INTELLIGENCE AND COMPUTING, 2ND INTL CONF ON BIG DATA INTELLIGENCE AND COMPUTING AND CYBER SCIENCE AND TECHNOLOGY CONGRESS (DASC/PICOM/DATACOM/CYBERSC | 2016年
关键词：
Big data analytics; online big data stream; OHBDA; four-layer architecture;
D O I：
10.1109/DASC-PICom-DataCom-CyberSciTec.2016.115
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Big data processing and analytics technologies have drawn much attention in recent years. However, the recent explosive growth of online data streams brings new challenges to the existing technologies. These online data streams tend to be massive, continuously arriving, heterogeneous, time-varying and unbounded. Therefore, it is necessary to have an integrated approach to process both big static data and online big data streams. We call this integrated approach online and historical big data analytics (OHBDA). We propose a four-layer architecture of OHBDA, i.e. including the storage layer, online and historical data processing layer, analytics layer, and decision-making layer. Functionalities and challenges of the four layers are further discussed. We conclude with a discussion of the requirements for the future OHBDA solutions, which may serve as a foundation for future big data analytics research.
引用
收藏
页码：634 / 639
页数：6
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