Volume 11, Number 2/3/4

Petrochemical Production Big Data and its Four Typical Application Paradigms


Hu Shaolin, Zhang Qinghua, Su Naiquan and Li Xiwu, Guangdong University of Petrochemical Technology, China


In recent years, the big data has attracted more and more attention. It can bring us more information and broader perspective to analyse and deal with problems than the conventional situation. However, so far, there is no widely acceptable and measurable definition for the term “big data”. For example, what significant features a data set needs to have can be called big data, and how large a data set is can be called big data, and so on. Although the "5V" description widely used in textbooks has been tried to solve the above problems in many big data literatures, "5V" still has significant shortcomings and limitations, and is not suitable for completely describing big data problems in practical fields such as industrial production. Therefore, this paper creatively puts forward the new concept of data cloud and the data cloud-based "3M" descriptive definition of big data, which refers to a wide range of data sources (Multisource), ultra-high dimensions (Multi-dimensional) and a long enough time span (Multi-spatiotemporal). Based on the 3M description of big data, this paper sets up four typical application paradigms for the production big data, analyses the typical application of four paradigms of big data, and lays the foundation for applications of big data from petrochemical industry.


Big Data, Paradigms, Industrial Big Data.