Design and Implementation of Tourism Big Data Analysis Platform

2021 ◽  
Author(s):  
Xingjian Liu ◽  
Xiao Chen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Design And Implementation ◽  
Analysis Platform

Design and implementation of digital big data analysis platform based on substation maintenance full link

2021 ◽  
Author(s):  
Jianwu Chen ◽  
Jiayi Zhu ◽  
Lijun Ma ◽  
Xin Xu ◽  
Jiayi Wu ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Design And Implementation ◽  
Analysis Platform

scholarly journals A Microservice-Based Big Data Analysis Platform for Online Educational Applications

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Kehua Miao ◽  
Jie Li ◽  
Wenxing Hong ◽  
Mingtao Chen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Science ◽  
Modular Design ◽  
Science Research ◽  
Big Data Analysis ◽  
Research Field ◽  
Traditional Work ◽  
Educational Applications ◽  
Analysis Platform

The booming development of data science and big data technology stacks has inspired continuous iterative updates of data science research or working methods. At present, the granularity of the labor division between data science and big data is more refined. Traditional work methods, from work infrastructure environment construction to data modelling and analysis of working methods, will greatly delay work and research efficiency. In this paper, we focus on the purpose of the current friendly collaboration of the data science team to build data science and big data analysis application platform based on microservices architecture for education or nonprofessional research field. In the environment based on microservices that facilitates updating the components of each component, the platform has a personal code experiment environment that integrates JupyterHub based on Spark and HDFS for multiuser use and a visualized modelling tools which follow the modular design of data science engineering based on Greenplum in-database analysis. The entire web service system is developed based on spring boot.

scholarly journals BDAP: A Big Data Analysis Platform Based On Spark

2018 ◽  
Vol 1060 ◽  
pp. 012023
Author(s):  
Zhixiang Wang ◽  
Yao Bu ◽  
Demeng Bai ◽  
Bin Wu ◽  
Jiafeng Qin
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Analysis Platform

Research of Big Data Processing Platform

2014 ◽  
Vol 484-485 ◽  
pp. 922-926
Author(s):  
Xiang Ju Liu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Network Architecture ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Computing Platform ◽  
Operational Characteristics ◽  
Analysis Platform

This paper introduces the operational characteristics of the era of big data and the current era of big data challenges, and exhaustive research and design of big data analytics platform based on cloud computing, including big data analytics platform architecture system, big data analytics platform software architecture , big data analytics platform network architecture big data analysis platform unified program features and so on. The paper also analyzes the cloud computing platform for big data analysis program unified competitive advantage and development of business telecom operators play a certain role in the future.

Design and Implementation of Financial Big Data Visualization Analysis Platform

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Xi Chen ◽  
Bo Fan ◽  
Jie Zheng ◽  
Hongyan Cui
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Visualization ◽  
Production Efficiency ◽  
Big Data Analysis ◽  
Research Field ◽  
Visualization Analysis ◽  
Front End ◽  
Big Data Visualization ◽  
Analysis Platform

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

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