Anomaly Detection in the Web Logs Using Unsupervised Algorithm

2019 ◽  
pp. 393-405 ◽  
Author(s):  
Lei Jin ◽  
Xiao Juan Wang ◽  
Yong Zhang ◽  
Li Yao
Keyword(s):  
Anomaly Detection ◽  
Web Logs ◽  
The Web

Anomaly detection in the web logs using user-behaviour networks

2019 ◽  
Vol 14 (2) ◽  
pp. 178
Author(s):  
Jingwen You ◽  
Xiaojuan Wang ◽  
Lei Jin ◽  
Yong Zhang
Keyword(s):  
Anomaly Detection ◽  
User Behaviour ◽  
Web Logs ◽  
The Web

scholarly journals Pelatihan Pemanfaatan Web Bagi Pemuda Muhammadiyah Cabang Kartasura

Warta LPM ◽  
2017 ◽  
Vol 13 (1) ◽  
pp. 11-19
Author(s):  
Azizah Fatmawati
Keyword(s):  
Information Technology ◽  
Web Logs ◽  
Before And After ◽  
Increasing Knowledge ◽  
The Web

Web is one medium that can be used by many activists in communityorganizations to actualize themselves and communicate effectively. Web UtilizationTraining for Muhammadiyah Youth in Kartasura branch is expected to provideknowledge into the importance of mastering Information Technology and increasethe understanding of the web (Web Logs) as the more effective information tool (AmarMa’ruf Nahi Munkar). The result shows that the implementation of the training isquite successful. This can be seen from the enthusiasm of the participants in providingfeedback from the questionnaires delivered before and after the training. Furthermore,the training also shows an increasing knowledge in the use of the web as much as64%.

Web Usage Mining and the Challenge of Big Data

Big Data ◽  
2016 ◽  
pp. 899-928
Author(s):  
Abubakr Gafar Abdalla ◽  
Tarig Mohamed Ahmed ◽  
Mohamed Elhassan Seliaman
Keyword(s):  
Data Mining ◽  
Pattern Discovery ◽  
Web Usage Mining ◽  
Data Mining Techniques ◽  
Web Log ◽  
Web Usage ◽  
Web Logs ◽  
Usage Patterns ◽  
Rich Data ◽  
The Web

The web is a rich data mining source which is dynamic and fast growing, providing great opportunities which are often not exploited. Web data represent a real challenge to traditional data mining techniques due to its huge amount and the unstructured nature. Web logs contain information about the interactions between visitors and the website. Analyzing these logs provides insights into visitors' behavior, usage patterns, and trends. Web usage mining, also known as web log mining, is the process of applying data mining techniques to discover useful information hidden in web server's logs. Web logs are primarily used by Web administrators to know how much traffic they get and to detect broken links and other types of errors. Web usage mining extracts useful information that can be beneficial to a number of application areas such as: web personalization, website restructuring, system performance improvement, and business intelligence. The Web usage mining process involves three main phases: pre-processing, pattern discovery, and pattern analysis. Various preprocessing techniques have been proposed to extract information from log files and group primitive data items into meaningful, lighter level abstractions that are suitable for mining, usually in forms of visitors' sessions. Major data mining techniques in web usage mining pattern discovery are: clustering, association analysis, classification, and sequential patterns discovery. This chapter discusses the process of web usage mining, its procedure, methods, and patterns discovery techniques. The chapter also presents a practical example using real web log data.

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.

Designing and Mining Web Applications

2010 ◽  
pp. 401-417
Author(s):  
Rosa Meo ◽  
Maristella Matera
Keyword(s):  
Web Applications ◽  
Conceptual Modeling ◽  
Modeling Language ◽  
Frequent Patterns ◽  
Application Management ◽  
Modeling Methods ◽  
Web Logs ◽  
Dynamic Web ◽  
The Web

In this chapter, we present the usage of a modeling language, WebML, for the design and the management of dynamic Web applications. WebML also makes easier the analysis of the usage of the application contents by the users, even if applications are dynamic. In fact, it makes use of some special-purpose logs, called conceptual logs, generated by the application runtime engine. In this chapter, we report on a case study about the analysis of conceptual logs for testifying to the effectiveness of WebML and its conceptual modeling methods. The methodology of the analysis of the Web logs is based on the datamining paradigm of item sets and frequent patterns, and makes full use of constraints on the conceptual logs’ content. As a consequence, we could obtain many interesting patterns for application management such as recurrent navigation paths, the most frequently visited page’s contents, and anomalies.

Traversal Pattern Mining in Web Usage Data

2008 ◽  
pp. 2004-2021
Author(s):  
Jenq-Foung Yao ◽  
Yongqiao Xiao
Keyword(s):  
Pattern Mining ◽  
Pattern Discovery ◽  
Web Usage Mining ◽  
Sequential Patterns ◽  
Web Usage ◽  
Web Logs ◽  
Frequent Episodes ◽  
Browsing Behavior ◽  
The Web ◽  
Usage Data

Web usage mining is to discover useful patterns in the web usage data, and the patterns provide useful information about the user’s browsing behavior. This chapter examines different types of web usage traversal patterns and the related techniques used to uncover them, including Association Rules, Sequential Patterns, Frequent Episodes, Maximal Frequent Forward Sequences, and Maximal Frequent Sequences. As a necessary step for pattern discovery, the preprocessing of the web logs is described. Some important issues, such as privacy, sessionization, are raised, and the possible solutions are also discussed.

BUILDING A KNOWLEDGE BASE FOR IMPLEMENTING A WEB-BASED COMPUTERIZED RECOMMENDATION SYSTEM

2007 ◽  
Vol 16 (05) ◽  
pp. 793-828 ◽  
Author(s):  
JUAN D. VELÁSQUEZ ◽  
VASILE PALADE
Keyword(s):  
Knowledge Base ◽  
Web Site ◽  
Web Mining ◽  
Recommendation System ◽  
The Internet ◽  
Web Pages ◽  
Web Based ◽  
Web Logs ◽  
Mining Tools ◽  
The Web

Understanding the web user browsing behaviour in order to adapt a web site to the needs of a particular user represents a key issue for many commercial companies that do their business over the Internet. This paper presents the implementation of a Knowledge Base (KB) for building web-based computerized recommender systems. The Knowledge Base consists of a Pattern Repository that contains patterns extracted from web logs and web pages, by applying various web mining tools, and a Rule Repository containing rules that describe the use of discovered patterns for building navigation or web site modification recommendations. The paper also focuses on testing the effectiveness of the proposed online and offline recommendations. An ample real-world experiment is carried out on a web site of a bank.

Adaptive Web Presence and Evolution through Web Log Analysis

2011 ◽  
pp. 691-698
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.

Traversal Pattern Mining in Web Usage Data

2004 ◽  
pp. 335-358 ◽  
Author(s):  
Yongqiao Xiao ◽  
Jenq-Foung (J.F.) Yao
Keyword(s):  
Pattern Mining ◽  
Pattern Discovery ◽  
Web Usage Mining ◽  
Sequential Patterns ◽  
Web Usage ◽  
Web Logs ◽  
Frequent Episodes ◽  
Browsing Behavior ◽  
The Web ◽  
Usage Data

Web usage mining is to discover useful patterns in the web usage data, and the patterns provide useful information about the user’s browsing behavior. This chapter examines different types of web usage traversal patterns and the related techniques used to uncover them, including Association Rules, Sequential Patterns, Frequent Episodes, Maximal Frequent Forward Sequences, and Maximal Frequent Sequences. As a necessary step for pattern discovery, the preprocessing of the web logs is described. Some important issues, such as privacy, sessionization, are raised, and the possible solutions are also discussed.

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