Hadoop-Based Painting Resource Storage and Retrieval Platform Construction and Testing

• Main Author: Chenhua Zu
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2021-01-01
• Series: Complexity
• Online Access: http://dx.doi.org/10.1155/2021/9933330

This paper adopts Hadoop to build and test the storage and retrieval platform for painting resources. This paper adopts Hadoop as the platform and MapReduce as the computing framework and uses Hadoop Distributed Filesystem (HDFS) distributed file system to store massive log data, which solves the storage problem of massive data. According to the business requirements of the system, this paper designs the system according to the process of web text mining, mainly divided into log data preprocessing module, log data storage module, log data analysis module, and log data visualization module. The core part of the system is the log data analysis module. The analysis of search keywords ranking, Uniform Resource Locator (URL), and user click relationship, URL ranking, and other dimensions are realized through data statistical analysis, and Canopy coarse clustering is performed first according to search keywords, and then K-means clustering is used for the results after Canopy clustering, and the calculation of cosine similarity is adopted to realize the grouping of users and build user portrait. The Hadoop development environment is installed and deployed, and functional and performance tests are conducted on the contents implemented in this system. The constructed private cloud platform for remote sensing image data can realize online retrieval of remote sensing image metadata and fast download of remote sensing image data and solve the problems in storage, data sharing, and management of remote sensing image data to a certain extent.