Statistics Analysis and Visualization for Big Data of E-commerce Platform Sales Evaluation

Abstract

With the rapid increase in the sales scale of e-commerce platforms is accompanied by the rapid growth of consumer evaluation data on commodities at the same time. How to use big data analysis and visualization technology to mine the valuable information in the massive consumers evaluation data is an urgent issue in promoting the development of e-commerce platforms. However, the amount of e-commerce evaluation data is huge, growing fast, and mostly unstructured data, which is typical big data. In order to efficiently realize the visualization of e-commerce evaluation big data, this paper proposes an end-to-end four-layer framework for data visualization system. The data acquisition layer uses the Webcollector crawler to crawl a total of 420,000 mobile sales evaluation data on the JD website and stores them in the MySQL database; The data import layer uses the Sqoop tool to import MySQL data into the Hadoop platform; The data processing layer uses HDFS and MapReduce to process and analyze big data; The visualization implementation layer uses Jsp+Servelet+JavaScript+echart integrated technology to visualize the big data of distribution of mobile phone sales, user purchase impressions, and user mobile phone portraits. Which helps consumers choose their favorite mobile phones conveniently, and provide decision-making support for e-commerce companies to more accurately launch products, benefiting both parties