自动化学报Issue(2)：161-171,11.DOI:10.16383/j.aas.2016.c150510
过程工业大数据建模研究展望
Perspectives on Big Data Modeling of Process Industries
摘要
Abstract
The understanding of big data goes through three stages, i.e., “3Vs” (Volume, variety and velocity), “4Vs”(“3Vs”and value), and“5Vs”(“4Vs”and veracity). In the era of big data of process industries, the“5Vs”characteristics of industrial big data are analyzed. After that, the existing methods on data modeling are reviewed while the corresponding limitations are analyzed under industrial big data circumstances with specific characteristics, i.e., multi-layer irregularly sampling, multiple temporal and spatial time series, and non-veracity with outlier. Finally, the perspectives on industrial big data modeling are discussed, including: i) latent structure modeling of multi-layer irregularly sampled big data; ii) multiple temporal and spatial time-series data modeling for event discovery, decision-making, and causality analysis; iii) robust modeling of data with non-veracity samples; and iv) data-friendly system architecture and method towards big data real-time modeling.关键词
过程工业大数据/多层面数据潜结构建模/多时空时间序列数据建模/大数据计算架构Key words
Process industrial big data/multi-layer data latent structure modeling/multiple temporal and spatial time-series data modeling/big data computing framework引用本文复制引用
刘强,秦泗钊..过程工业大数据建模研究展望[J].自动化学报,2016,(2):161-171,11.基金项目
国家自然科学基金(61304107,61490704,61573022,61290323,61203102),中国博士后科学基金(2013M541242),博士后国际交流计划派出项目(20130020),中央高校基本科研业务费(N130408002, N130108001)资助@@@@Supported by National Natural Science Foundation of China (61304107,61490704,61573022,61290323,61203102), the China Postdoctoral Science Foundation (2013M541242), the Interna-tional Postdoctoral Exchange Fellowship Program (20130020) and the Fundamental Research Funds for the Central Universi-ties (N130408002, N130108001) （61304107,61490704,61573022,61290323,61203102）
