The Analysis of China’s Integrity Situation Based on Big Data

In order to study deeply the prominent problems faced by China’s clean government work,and put forward effective coping strategies,this article analyzes the network information of anti-corruption related news events,which is based on big data technology.In this study,we take the news report from the website of the Communist Party of China(CPC)Central Commission for Discipline Inspection(CCDI)as the source of data.Firstly,the obtained text data is converted to word segmentation and stop words under preprocessing,and then the pre-processed data is improved by vectorization and text clustering,finally,after text clustering,the key words of clean government work is derived from visualization analysis.According to the results of this study,it shows that China’s clean government work should focus on‘the four forms of decadence’issue,and related departments must strictly crack down five categories of phenomena,such as“illegal payment of subsidies or benefits,illegal delivery of gifts and cash gift,illegal use of official vehicles,banquets using public funds,extravagant wedding ceremonies and funeral”.The results of this study are consistent with the official data released by the CCDI’s website,which also suggests that the method is feasible and effective.

Visualization Analysis for Business Performance of Chinese Listed Companies Based on Gephi

When conducting company performance evaluations,the traditional method cannot reflect the distribution characteristics of the company’s operating conditions in the entire securities market.Gephi is an efficient tool for data analysis and visualization in the era of big data.It can convert the evaluation results of all listed companies into nodes and edges,and directly display them in the form of graphs,thus making up for the defects of traditional methods.This paper will take all the listed companies in the Shanghai and Shenzhen Stock Exchange as the analysis object.First uses tushare and web crawlers to collect the financial statement data of these companies.And then,uses the Economic Value Added model to calculate the EVA of each listed company and build graph data.Next,import the graph data into gephi to generate the distribution graph of all listed companies’performance,and summarize the distribution characteristics of business performance.Finally,select a listed company that you want to analyze in detail,using the traditional DuPont analysis method to conduct micro level visualization analysis of the business performance to find the main factors affecting the company’s operating performance.Incorporating gephi into traditional performance analysis methods will make the results of traditional analytical methods more effective and complete.

A Survey of Time Series Data Visualization Methods

In the era of big data,the general public is more likely to access big data,but they wouldn’t like to analyze the data.Therefore,the traditional data visualization with certain professionalism is not easy to be accepted by the general public living in the fast pace.Under this background,a new general visualization method for dynamic time series data emerges as the times require.Time series data visualization organizes abstract and hard-to-understand data into a form that is easily understood by the public.This method integrates data visualization into short videos,which is more in line with the way people get information in modern fast-paced lifestyles.The modular approach also facilitates public participation in production.This paper summarizes the dynamic visualization methods of time series data ranking,studies the relevant literature,shows its value and existing problems,and gives corresponding suggestions and future research prospects.