User online behavior based on big data distributed clustering algorithm. (15th April 2020)

• Record Type: Journal Article
• Authors: Wang, Yan
• Is Part Of: International journal of advanced robotic systems. Volume 17:Number 2(2020:Mar./Apr.)
• Publication Date: 2020-04-15
• Subjects: Big data -- distributed clustering algorithm -- user online behavior -- campus network; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881420917293
• Languages: English
• ISSNs: 1729-8806

Abstract

With the development of big data technology more and more perfect, many colleges and universities have begun to use it to analyze the construction work. In daily life, such as class, study, and entertainment, the campus network exists. The purpose of this article is to study the online behavior of users, analyze students' use of the campus network by analyzing students, and not only have a clear understanding of the students' online access but also feedback on the operation and maintenance of the campus network. Based on the big data, this article uses distributed clustering algorithm to study the online behavior of users. This article selects a college online user as the research object and studies and analyzes the online behavior of school users. This study found that the second-year student network usage is as high as 330, 000, which is 60.98% more than the senior. In addition, the majority of student users spend most of their online time on the weekend, and the other time is not much different. The duration is concentrated within 1 h, 1–2 h, 2–3 h in these three time periods. By studying the user's online behavior, you can understand the utilization rate of the campus network bandwidth resources and the distribution of the use of the network, to prevent students from indulging in the virtual network world, and to ensure that the network users can improve the online experience of the campus network while accessing the network resources reasonably. The research provides a reference for network administrators to adjust network bandwidth and optimize the network.