scholarly journals A Secured Log Mining Approach to Collection, Monitoring, Rotation, and Analysis of Frequent and Heterogeneous Logs

2021 ◽  
Author(s):  
Surendra Gaur ◽  
Nafisur Rahman
Keyword(s):  
Log Mining

Web Log Mining als Controllinginstrument für Online-Shops

Controlling ◽  
2001 ◽  
Vol 13 (3) ◽  
pp. 157-166 ◽  
Author(s):  
Reinhold Mayer ◽  
Frank Bensberg ◽  
Anita Hukemann
Keyword(s):  
Web Log Mining ◽  
Web Log ◽  
Log Mining ◽  
Online Shops

The Research of the Accessibility Website Design Based on Web Log Mining

2012 ◽  
Vol 241-244 ◽  
pp. 2779-2782
Author(s):  
Heng Yao Tang ◽  
Xiao Yan Zhan
Keyword(s):  
Website Design ◽  
Web Log Mining ◽  
Web Page ◽  
Web Log ◽  
User Interests ◽  
Mining Technique ◽  
Dynamic Web ◽  
Log Mining ◽  
The Web

On the problems existing in the realization of current accessibility website, we design a web designing architecture, using the web log mining technique to extract user interests and access priority sequence and adopting the dynamic web page information to fill the web page commonly used structure, realize the intelligent , personalized accessibility.

Application of Data Pre-Processing Method in Web Mining

2014 ◽  
Vol 687-691 ◽  
pp. 1592-1595
Author(s):  
Yun Peng Duan ◽  
Chun Xi Zhao ◽  
Ying Shi
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Early Stage ◽  
Data Preprocessing ◽  
Web Technology ◽  
Processing Method ◽  
Web Log Mining ◽  
Web Log ◽  
User Access ◽  
Log Mining

With the widely application of the WWW and the emergence of Web technology, make the research of data mining has entered a new stage. Web log mining is based on the idea of data mining to analyze the server log processing. Paper aimed at the early stage of the data mining is put forward based on log data preprocessing methods, the purpose is to divide server logs into multiple unique user access sequence at a time, and to give a good algorithm.

A DYNAMICS RECOMMENDATION SYSTEM BASED ON LOG MINING

2002 ◽  
Vol 13 (04) ◽  
pp. 521-530 ◽  
Author(s):  
WEN GAO ◽  
SHI WANG ◽  
BIN LIU
Keyword(s):  
Recommendation System ◽  
Predictive Accuracy ◽  
Web Pages ◽  
Web Log Mining ◽  
Web Page ◽  
Time Dynamic ◽  
Active User ◽  
Dynamic Web ◽  
Log Mining ◽  
A Current

This paper presents a new real-time, dynamic web page recommendation system based on web-log mining. The visit sequences of previous visitors are used to train a classifier for web page recommendation. The recommendation engine identifies a current active user, and submits its visit sequence as an input to the classifier. The output of the recommendation engine is a set of recommended web pages, whose links are attached to bottom of the requested page. Our experiments show that the proposed approach is effective: the predictive accuracy is quite high (over 90%), and the time for the recommendation is quite small.

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