Total plant performance evaluation based on big data: Visualization analysis of TE process

2018 ◽  
Vol 26 (8) ◽  
pp. 1736-1749 ◽  
Author(s):  
Mengyao Li ◽  
Wenli Du ◽  
Feng Qian ◽  
Weiming Zhong
2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Xi Chen ◽  
Bo Fan ◽  
Jie Zheng ◽  
Hongyan Cui

At present, it has become a hot research field to improve production efficiency and improve life experience through big data analysis. In the process of big data analysis, how to vividly display the results of the analysis is crucial. So, this paper introduces a set of big data visualization analysis platform based on financial field. The platform adopts the MVC system architecture, which is mainly composed of two parts: the background and the front end. The background part is built on the Django framework, and the front end is built with html5, css3, and JavaScript. The chart is rendered by Echarts. The platform can realize the classification of customers' savings potential through bank data, and make portraits of customers with different savings levels. The data analysis results can be dynamically displayed and interact wit


2019 ◽  
Vol 15 (1) ◽  
pp. 490-497 ◽  
Author(s):  
Antonino Galletta ◽  
Lorenzo Carnevale ◽  
Alessia Bramanti ◽  
Maria Fazio

2021 ◽  
Vol 12 (3) ◽  
pp. 19-33
Author(s):  
Shadi Maleki ◽  
Milad Mohammadalizadehkorde

Big data provided by social media has been increasingly used in various fields of research including disaster studies and emergency management. Effective data visualization plays a central role in generating meaningful insight from big data. However, big data visualization has been a challenge due to the high complexity and high dimensionality of it. The purpose of this study is to examine how the number and spatial distribution of tweets changed on the day Hurricane Harvey made landfall near Houston, Texas. For this purpose, this study analyzed the change in tweeting activity between the Friday of Hurricane Harvey and a typical Friday before the event.


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