Web Usage Mining and Its Applications

2011 ◽  
pp. 1221-1225
Author(s):  
Yongjian Fu
Keyword(s):  
Data Mining ◽  
World Wide ◽  
Online Shopping ◽  
Rapid Development ◽  
User Feedback ◽  
Web Usage Mining ◽  
Web Based ◽  
Web Usage ◽  
The World ◽  
The Web

With the rapid development of the World Wide Web or the Web, many organizations now put their information on the Web and provide Web-based services such as online shopping, user feedback, technical support, and so on. Understanding Web usage through data mining techniques is recognized as an important area.

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.

Web Usage Mining Issues in Big Data

2021 ◽  
pp. 102-112
Author(s):  
Sunny Sharma ◽  
Manisha Malhotra
Keyword(s):  
Data Mining ◽  
Big Data ◽  
User Behavior ◽  
Web Usage Mining ◽  
Web Personalization ◽  
Data Mining Techniques ◽  
Meaningful Information ◽  
Web Usage ◽  
Use Of Data ◽  
The Web

Web usage mining is the use of data mining techniques to analyze user behavior in order to better serve the needs of the user. This process of personalization uses a set of techniques and methods for discovering the linking structure of information on the web. The goal of web personalization is to improve the user experience by mining the meaningful information and presented the retrieved information in a way the user intends. The arrival of big data instigated novel issues to the personalization community. This chapter provides an overview of personalization, big data, and identifies challenges related to web personalization with respect to big data. It also presents some approaches and models to fill the gap between big data and web personalization. Further, this research brings additional opportunities to web personalization from the perspective of big data.

Effectiveness of Web Usage Mining Techniques in Business Application

2017 ◽  
pp. 324-350 ◽  
Author(s):  
Ahmed El Azab ◽  
Mahmood A. Mahmood ◽  
Abd El-Aziz
Keyword(s):  
Data Mining ◽  
Academic Research ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Data ◽  
Web Data Mining ◽  
Web Usage ◽  
Business Application ◽  
Common Interests ◽  
The Web

Web usage mining techniques and applications across industries is still exploratory and, despite an increase in academic research, there are challenge of analyze web which quantitatively capture web users' common interests and characterize their underlying tasks. This chapter addresses the problem of how to support web usage mining techniques and applications across industries by combining language of web pages and algorithms that used in web data mining. Existing research in web usage mining techniques tend to focus on finding out how each techniques can apply in different industries fields. However, there is little evidence that researchers have approached the issue of web usage mining across industries. Consequently, the aim of this chapter is to provide an overview of how the web usage mining techniques and applications across industries can be supported.

scholarly journals Recent innovations in Distributed Systems: Challenges and Benefits

2013 ◽  
Vol 10 (10) ◽  
pp. 2057-2061
Author(s):  
Madhurima Hooda ◽  
Amandeep Kaur ◽  
Madhulika Bhadauria
Keyword(s):  
Distributed Systems ◽  
Distributed System ◽  
World Wide ◽  
Online Shopping ◽  
The Internet ◽  
Web Browser ◽  
The World ◽  
Access Information ◽  
Mobile Computers ◽  
The Web

The World Wide Web is used by millions of people everyday for various purposes including email, reading news, downloading music, online shopping or simply accessing information about anything. Using a standard web browser, the user can access information stored on Web servers situated anywhere on the globe. This gives the illusion that all this information is situated locally on the user’s computer. In reality, the Web represents a huge distributed system that appears as a single resource to the user available at the click of a button. This paper gives an overview of distributed systems in current IT sector. Distributed systems are everywhere. The internet enable users throughout the world to access its services wherever they may be located [1]. Each organization manages an intranet, which provides local services for local users and generally provides services to other users in the internet. Small distributed systems can be constructed from mobile computers and other small computational devices that are attached to a wireless network.

Clustering Algorithms for Data Streams

2011 ◽  
pp. 566-571
Author(s):  
Christos Makris ◽  
Nikos Tsirakis
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
World Wide ◽  
Clustering Algorithms ◽  
Multimedia Data ◽  
Web Pages ◽  
Web Based ◽  
Area Of Interest ◽  
The World ◽  
Available Information

The World Wide Web has rapidly become the dominant Internet tool which has overwhelmed us with a combination of rich hypertext information, multimedia data and various resources of dynamic information. This evolution in conjunction with the immense amount of available information imposes the need of new computational methods and techniques in order to provide, in a systematical way, useful information among billions of Web pages. In other words, this situation poses great challenges for providing knowledge from Web-based information. The area of data mining has arisen over the last decade to address this type of issues. There are many methods, techniques and algorithms that accomplish different tasks in this area. All these efforts examine the data and try to find a model that fits to their characteristics in order to examine them. Data can be either typical information from files, databases and so forth, or with the form of a stream. Streams constitute a data model where information is an undifferentiated, byte-by-byte flow that passes over the time. The area of algorithms for processing data streams and associated applications has become an emerging area of interest, especially when all this is done over the Web. Generally, there are many data mining functions (Tan, Steinbach, & Kumar, 2006) that can be applied in data streams. Among them one can discriminate clustering, which belongs to the descriptive data mining models. Clustering is a useful and ubiquitous tool in data analysis.

World Wide Web Usage Mining

2011 ◽  
pp. 1242-1248 ◽  
Author(s):  
Wen-Chen Hu ◽  
Hung-Jen Yang ◽  
Chung-wei Lee ◽  
Jyh-haw Yeh
Keyword(s):  
Data Mining ◽  
World Wide Web ◽  
World Wide ◽  
Data Gathering ◽  
Web Usage Mining ◽  
Web Data ◽  
Mining System ◽  
Data Mining Techniques ◽  
Web Usage ◽  
Content Mining

World Wide Web data mining includes content mining, hyperlink structure mining, and usage mining. All three approaches attempt to extract knowledge from the Web, produce some useful results from the knowledge extracted, and apply the results to certain real-world problems. The first two apply the data mining techniques to Web page contents and hyperlink structures, respectively. The third approach, Web usage mining (the theme of this article), is the application of data mining techniques to the usage logs of large Web data repositories in order to produce results that can be applied to many practical subjects, such as improving Web sites/pages, making additional topic or product recommendations, user/customer behavior studies, and so forth. This article provides a survey and analysis of current Web usage mining technologies and systems. A Web usage mining system must be able to perform five major functions: (i) data gathering, (ii) data preparation, (iii) navigation pattern discovery, (iv) pattern analysis and visualization, and (v) pattern applications. Many Web usage mining technologies have been proposed, and each technology employs a different approach. This article first describes a generalized Web usage mining system, which includes five individual functions. Each system function is then explained and analyzed in detail. Related surveys of Web usage mining techniques also can be found in Hu, et al. (2003) and Kosala and Blockeel (2000).

Web Usage Mining

Web Mining ◽  
2011 ◽  
pp. 373-392 ◽  
Author(s):  
Yew-Kwong Woon ◽  
Wee-Keong Ng ◽  
Ee-Peng Lim
Keyword(s):  
Data Mining ◽  
World Wide ◽  
Web Usage Mining ◽  
Web Usage ◽  
Online Business ◽  
Web Access ◽  
Business Competitiveness ◽  
Access Logs ◽  
Web Server Logs ◽  
Web Access Logs

The rising popularity of electronic commerce makes data mining an indispensable technology for several applications, especially online business competitiveness. The World Wide Web provides abundant raw data in the form of Web access logs. However, without data mining techniques, it is difficult to make any sense out of such massive data. In this chapter, we focus on the mining of Web access logs, commonly known as Web usage mining. We analyze algorithms for preprocessing and extracting knowledge from such logs. We will also propose our own techniques to mine the logs in a more holistic manner. Experiments conducted on real Web server logs verify the practicality as well as the efficiency of the proposed techniques as compared to an existing technique. Finally, challenges in Web usage mining are discussed.

Web-Based Systems and Performance Testing

2006 ◽  
pp. 1-28
Author(s):  
B. M. Subraya
Keyword(s):  
World Wide ◽  
Performance Testing ◽  
Dimensional Change ◽  
Web Page ◽  
Web Based ◽  
The World ◽  
And Performance ◽  
The Web ◽  
Growing Population

For many years, the World Wide Web (Web) functioned quite well without any concern about the quality of performance. The designers of the Web page, as well as the users were not much worried about the performance attributes. The Web, in the initial stages of development, was primarily meant to be an information provider rather than a medium to transact business, into which it has grown. The expectations from the users were also limited only to seek the information available on the Web. Thanks to the ever growing population of Web surfers (now in the millions), information found on the Web underwent a dimensional change in terms of nature, content, and depth.

Classifying Web Usage Behavior in the Workplace

2011 ◽  
pp. 211-234
Author(s):  
Murugan Anandarajan
Keyword(s):  
United States ◽  
World Wide Web ◽  
World Wide ◽  
The United States ◽  
The Internet ◽  
Web Usage ◽  
The World ◽  
International Data ◽  
Data Group ◽  
The Web

The ubiquitous nature of the World Wide Web (commonly known as the Web) is dramatically revolutionizing the manner in which organizations and individuals alike acquire and distribute information. Recent reports from the International Data Group indicate that the number of people on the Internet will reach 320 million by the year 2002 (Needle, 1999). Studies also indicate that in the United States alone, Web commerce will account for approximately $325 billion by the year 2002.

Are there Age-Related Differences in Problem Solving on the World Wide Web?

2002 ◽  
Vol 46 (21) ◽  
pp. 1772-1776
Author(s):  
Aideen J. Stronge ◽  
Wendy A. Rogers ◽  
Arthur D. Fisk
Keyword(s):  
Problem Solving ◽  
World Wide ◽  
Top Down ◽  
Younger Adults ◽  
Web Based ◽  
Search Tasks ◽  
Age Related ◽  
The World ◽  
Problem Solving Strategies ◽  
The Web

The present study investigated the Web-based problem solving strategies of 16 younger and 16 older experienced Web users. Participants searched for answers to 8 search tasks varying in complexity. Three questions were addressed in this study: (1) Are there age-related differences in success?, (2) If differences in success emerge, are these age-related differences quantitative (e.g., number of strategies)?, or (3) Are these age-related differences qualitative (e.g., type of strategies)?. Overall, younger adults were more successful finding the correct answer to the search tasks. However, this was not due to the number of strategies used, but instead was related to the type of strategy used. Older adults were more likely to use a top-down strategy (i.e., system tool) to find an answer to the search tasks. In general, unsuccessful searchers used significantly more top-down strategies than successful searchers. The implications for these findings are discussed.

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