Modeling Web Logs to Enhance the Analysis of Web Usage Data

2010 ◽  
Author(s):  
Paul Hernande ◽  
Irene Garrigos ◽  
Jose-Norberto Mazon
Keyword(s):  
Web Usage ◽  
Web Logs ◽  
Usage Data

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.

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.

Traversal Pattern Mining in Web Usage Data

2005 ◽  
pp. 2857-2860
Author(s):  
Jenq-Foung (J.F.) Yao ◽  
Yongqiao Xiao
Keyword(s):  
Web Site ◽  
Pattern Mining ◽  
Web Server ◽  
Web Design ◽  
Web Usage Mining ◽  
Web Usage ◽  
Web Logs ◽  
Browsing Behavior ◽  
Web Server Performance ◽  
Usage Data

Web usage mining is designed to discover useful patterns in Web usage data, i.e., Web logs. Web logs record the user’s browsing of a Web site, and the patterns provide useful information about the user’s browsing behavior. Such patterns can be used for Web design, improving Web server performance, personalization, etc.

MINING WEB DATA USING CLUSTERING TECHNIQUE FOR WEB PERSONALIZATION

2002 ◽  
Vol 02 (03) ◽  
pp. 255-265 ◽  
Author(s):  
SUPRIYA KUMAR DE ◽  
P. RADHA KRISHNA
Keyword(s):  
Dimensional Space ◽  
Web Personalization ◽  
Web Data ◽  
Concept Hierarchy ◽  
Web Usage ◽  
Hierarchy Model ◽  
Dimension Space ◽  
The Relationship ◽  
The Web ◽  
Usage Data

Clustering of data in a large dimension space is of great interest in many data mining applications. In this paper, we propose a method for clustering of web usage data in a high-dimensional space based on a concept hierarchy model. In this method, the relationship present in the web usage data are mapped into a fuzzy proximity relation of user transactions. We also described an approach to present the preference set of URLs to a new user transaction based on the match score with the clusters. The study demonstrates that our approach is general and effective for mining the web data for web personalization.

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.

On the Use of Soft Computing Techniques for Web Personalization

2011 ◽  
pp. 2381-2402
Author(s):  
G. Castellano ◽  
A.M. Fanelli ◽  
M.A. Torsello
Keyword(s):  
Soft Computing ◽  
Domain Knowledge ◽  
User Preferences ◽  
Web Personalization ◽  
Web Based ◽  
Hybrid Schemes ◽  
Web Usage ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Usage Data

Due to the growing variety and quantity of information available on the Web, there is urgent need for developing Web-based applications capable of adapting their services to the needs of the users. This is the main rationale behind the flourishing area of Web personalization that finds in soft computing (SC) techniques a valid tool to handle uncertainty in Web usage data and develop Web-based applications tailored to user preferences. The main reason for this success seems to be the synergy resulting from SC paradigms, such as fuzzy logic, neural networks, and genetic algorithms. Each of these computing paradigms provides complementary reasoning and searching methods that allow the use of domain knowledge and empirical data to solve complex problems. In this chapter, we emphasize the suitability of hybrid schemes combining different SC techniques for the development of effective Web personalization systems. In particular, we present a neuro-fuzzy approach for Web personalization that combines techniques from the fuzzy and the neural paradigms to derive knowledge from Web usage data and represent the knowledge in the comprehensible form of fuzzy rules. The derived knowledge is ultimately used to dynamically suggest interesting links to the user of a Web site.

Intelligent Analytics

2011 ◽  
Vol 2 (1) ◽  
pp. 31-45 ◽  
Author(s):  
Lakshmi S. Iyer ◽  
Rajeshwari M. Raman
Keyword(s):  
Business Intelligence ◽  
New Technology ◽  
Customer Relationship ◽  
Web Analytics ◽  
Web Usage ◽  
Emerging Trends ◽  
Key Players ◽  
Transactional Data ◽  
Technology Trends ◽  
Usage Data

Organizations use web analytic tools and technologies to measure, collect, analyze, and report web usage data to help optimize websites. Traditionally, most of this data tends to be non-transactional and non-identifiable. In this regard, there has not been much integration with transactional data that is collected, stored, analyzed, and reported through Business Intelligence (BI). Emerging trends in web analytics provide organizations the ability to aggregate and analyze web analytics data with transactional data to provide valuable insights for building better customer relationship strategies. In this paper, the authors give an overview of web analytics tools, key players, new technology trends and capabilities to integrate web analytics with BI so organizations can leverage intelligent analytics for new marketing initiatives. While the benefits are significant, there are some challenges associated with the integration and a few possible solutions to address.

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