Combined mining of Web server logs and web contents for classifying user navigation patterns and predicting users’ future requests

2007 ◽  
Vol 61 (2) ◽  
pp. 304-330 ◽  
Author(s):  
Haibin Liu ◽  
Vlado Kešelj
Keyword(s):  
Web Server ◽  
Web Contents ◽  
Navigation Patterns ◽  
Server Logs ◽  
Web Server Logs ◽  
User Navigation

Building and Evaluating an Adaptive Smart Web Pages

2012 ◽  
Vol 3 (1) ◽  
pp. 30
Author(s):  
Mona M. Abu Al-Khair ◽  
M. Koutb ◽  
H. Kelash
Keyword(s):  
Web Server ◽  
Web Pages ◽  
Server Logs ◽  
Web Server Logs ◽  
The Web

Each year the number of consumers and the variety of their interests increase. As a result, providers are seeking ways to infer the customer's interests and to adapt their websites to make the content of interest more easily accessible. Assume that past navigation behavior as an indicator of the user's interests. Then, the records of this behavior, kept in the web-server logs, can be mined to extract the user's interests. On this principal, recommendations can be generated, to help old and new website's visitors to find the information about their interest faster.

scholarly journals Data Mining applied on Web Robots Detection: A Systematic Mapping

2021 ◽  
Author(s):  
Ramon Abilio ◽  
Cristiano Garcia ◽  
Victor Fernandes
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Learning Algorithms ◽  
Web Server ◽  
Machine Learning Algorithms ◽  
Web Pages ◽  
Systematic Mapping ◽  
Mining Methods ◽  
Server Logs ◽  
Web Server Logs

Browsing on Internet is part of the world population’s daily routine. The number of web pages is increasing and so is the amount of published content (news, tutorials, images, videos) provided by them. Search engines use web robots to index web contents and to offer better results to their users. However, web robots have also been used for exploiting vulnerabilities in web pages. Thus, monitoring and detecting web robots’ accesses is important in order to keep the web server as safe as possible. Data Mining methods have been applied to web server logs (used as data source) in order to detect web robots. Then, the main objective of this work was to observe evidences of definition or use of web robots detection by analyzing web server-side logs using Data Mining methods. Thus, we conducted a systematic Literature mapping, analyzing papers published between 2013 and 2020. In the systematic mapping, we analyzed 34 studies and they allowed us to better understand the area of web robots detection, mapping what is being done, the data used to perform web robots detection, the tools, and algorithms used in the Literature. From those studies, we extracted 33 machine learning algorithms, 64 features, and 13 tools. This study is helpful for researchers to find machine learning algorithms, features, and tools to detect web robots by analyzing web server logs.

From Editors' Choice to Readers’ Favorites: Analyzing Server Logs of China's Biggest Online Newspaper

2013 ◽  
Author(s):  
Yijun Gao
Keyword(s):  
Web Server ◽  
Online Newspaper ◽  
People’S Daily ◽  
The Common ◽  
Server Logs ◽  
People's Daily ◽  
Web Server Logs ◽  
The Web

This study analyzed the Web server logs from the People's Daily Online and revealed some interesting findings: Pageview numbers of the mportant news in editors’ mind on the most obvious sections of the homepage, are not significantly different than those of the "common" news put on the less obvious sections.Cette étude a porté sur l'analyse des fichiers de journalisation de serveurs Web du Quotidien du Peuple en ligne et a révélé quelques données intéressantes : le nombre de pages vues pour les dépêches jugées importantes par la rédaction et placées en évidence de la page d'accueil n'est pas significativement différent du nombre de pages vues pour les dépêches plus « courantes » placés moins en évidence. 

Preprocessing the web server logs

2012 ◽  
Vol 37 (3) ◽  
pp. 1-5 ◽  
Author(s):  
K. Sudheer Reddy ◽  
G. Partha Saradhi Varma ◽  
I. Ramesh Babu
Keyword(s):  
Web Server ◽  
Server Logs ◽  
Web Server Logs ◽  
The Web
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