scholarly journals Bot Detection Model using User Agent and User Behavior for Web Log Analysis

2020 ◽  
Vol 176 ◽  
pp. 1621-1625
Author(s):  
Takamasa TANAKA ◽  
Hidekazu NIIBORI ◽  
Shiyingxue LI ◽  
Shimpei NOMURA ◽  
Hiroki KAWASHIMA ◽  
...  
Keyword(s):  
User Behavior ◽  
Log Analysis ◽  
User Agent ◽  
Web Log ◽  
Detection Model ◽  
Bot Detection

Combining Data Warehousing and Data Mining Techniques for Web Log Analysis

2007 ◽  
pp. 1-28
Author(s):  
Torben Pedersen ◽  
Jesper Thorhauge ◽  
Søren Jespersen
Keyword(s):  
Data Mining ◽  
User Behavior ◽  
Data Warehousing ◽  
Log Analysis ◽  
Hybrid Technique ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Web Log ◽  
Navigation Patterns ◽  
High Level

Enormous amounts of information about Web site user behavior are collected in Web server logs. However, this information is only useful if it can be queried and analyzed to provide high-level knowledge about user navigation patterns, a task that requires powerful techniques. This chapter presents a number of approaches that combine data warehousing and data mining techniques in order to analyze Web logs. After introducing the well-known click and session data warehouse (DW) schemas, the chapter presents the subsession schema, which allows fast queries on sequences of page visits. Then, the chapter presents the so-called “hybrid” technique, which combines DW Web log schemas with a data mining technique called Hypertext Probabilistic Grammars, hereby providing fast and flexible constraint-based Web log analysis. Finally, the chapter presents a “post-check enhanced” improvement of the hybrid technique.

Combining Data Warehousing and Data Mining Techniques for Web Log Analysis

2008 ◽  
pp. 3364-3385
Author(s):  
Torben Bach Pedersen ◽  
Jesper Thorhauge ◽  
Søren E. Jespersen
Keyword(s):  
Data Mining ◽  
User Behavior ◽  
Data Warehousing ◽  
Log Analysis ◽  
Hybrid Technique ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Web Log ◽  
Navigation Patterns ◽  
High Level

Enormous amounts of information about Web site user behavior are collected in Web server logs. However, this information is only useful if it can be queried and analyzed to provide high-level knowledge about user navigation patterns, a task that requires powerful techniques. This chapter presents a number of approaches that combine data warehousing and data mining techniques in order to analyze Web logs. After introducing the well-known click and session data warehouse (DW) schemas, the chapter presents the subsession schema, which allows fast queries on sequences of page visits. Then, the chapter presents the so-called “hybrid” technique, which combines DW Web log schemas with a data mining technique called Hypertext Probabilistic Grammars, hereby providing fast and flexible constraint-based Web log analysis. Finally, the chapter presents a “post-check enhanced” improvement of the hybrid technique.

scholarly journals Web Log Analysis of a Digital Game-Based Learning System in Vocabulary Enrichment for College Students

2020 ◽  
Vol 10 (1) ◽  
pp. 25-30
Author(s):  
Ellenita R. Red ◽  
◽  
Aira Jessica B. Corpuz ◽  
Genrev C. Arambulo ◽  
Gabriel G. Delgado
Keyword(s):  
College Students ◽  
Learning System ◽  
Log Analysis ◽  
Digital Game ◽  
Game Based Learning ◽  
Web Log

Adaptive Web Presence and Evolution through Web Log Analysis

2011 ◽  
pp. 12-17
Author(s):  
Xueping Li
Keyword(s):  
Web Mining ◽  
Log Analysis ◽  
Electronic Business ◽  
Web Presence ◽  
Web Log ◽  
Significant Challenge ◽  
Web Logs ◽  
Popular Medium ◽  
Potential Customers ◽  
The Web

The Internet has become a popular medium to disseminate information and a new platform to conduct electronic business (e-business) and electronic commerce (e-commerce). With the rapid growth of the WWW and the intensified competition among the businesses, effective web presence is critical to attract potential customers and retain current customer thus the success of the business. This poses a significant challenge because the web is inherently dynamic and web data is more sophisticated, diverse, and dynamic than traditional well-structured data. Web mining is one method to gain insights into how to evolve the web presence and to ultimately produce a predictive model such that the evolution of a given web site can be categorized under its particular context for strategic planning. In particular, web logs contain potentially useful information and the analysis of web log data have opened new avenues to assist the web administrators and designers to establish adaptive web presence and evolution to fit user requirements.

Historic Perspective of Log Analysis

2011 ◽  
pp. 18-38 ◽  
Author(s):  
W. David Penniman
Keyword(s):  
World Wide ◽  
User Behavior ◽  
Deep Understanding ◽  
Historical Review ◽  
Early Years ◽  
Log Analysis ◽  
Qualitative Approaches ◽  
Retrieval Systems ◽  
Information Retrieval Systems ◽  
Privacy Issues

This historical review of the birth and evolution of transaction log analysis applied to information retrieval systems provides two perspectives. First, a detailed discussion of the early work in this area, and second, how this work has migrated into the evaluation of World Wide Web usage. The author describes the techniques and studies in the early years and makes suggestions for how that knowledge can be applied to current and future studies. A discussion of privacy issues with a framework for addressing the same is presented as well as an overview of the historical “eras” of transaction log analysis. The author concludes with the suggestion that a combination of transaction log analysis of the type used early in its application along with additional more qualitative approaches will be essential for a deep understanding of user behavior (and needs) with respect to current and future retrieval systems and their design.

They Know What You Will Do Next Click

2020 ◽  
pp. 100-122
Author(s):  
Serra Çelik
Keyword(s):  
Focus Group ◽  
User Behavior ◽  
Web Usage Mining ◽  
Web Log ◽  
Web Usage ◽  
User Behaviors ◽  
Log Files ◽  
The Web

This chapter focuses on predicting web user behaviors. When web users enter a website, every move they make on that website is stored as web log files. Unlike the focus group or questionnaire, the log files reflect real user behavior. It can easily be said that having actual user behavior is a gold value for the organizations. In this chapter, the ways of extracting user patterns (user behavior) from the log files are sought. In this context, the web usage mining process is explained. Some web usage mining techniques are mentioned.

scholarly journals Statistical analysis of city and the villages Internet users based on user logs

2018 ◽  
Vol 176 ◽  
pp. 03011
Author(s):  
ZHANG Yi-wen ◽  
BAI Yan-qi ◽  
YANG An-ju
Keyword(s):  
Statistical Analysis ◽  
User Behavior ◽  
Internet Access ◽  
Internet User ◽  
Log Analysis ◽  
The Internet ◽  
Internet Users ◽  
Internet User Behavior ◽  
Access Log Analysis

In recent years, with the rapid increase of users active on the Internet, Internet users access log is also increasing rapidly. According to the user's Internet access log analysis of the characteristics of user behavior on the Internet. In this paper, we classify the statistical analysis of the behavior of Internet users by collecting information and data on urban and rural Internet user behavior. This result may provide a basis for guiding the behavior of Internet software manufacturers or government.

scholarly journals Research and Implementation of a Method for Web Log Analysis Template Extraction

2019 ◽  
Vol 162 ◽  
pp. 673-681
Author(s):  
Zengchao Ni ◽  
Hongqi Liu ◽  
Yuanping Chen ◽  
Dengsheng Wu
Keyword(s):  
Log Analysis ◽  
Web Log ◽  
Template Extraction
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