• Journal of Digital Convergence
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• v.6 no.4
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• pp.157-163
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• 2008

Despite the increase internet use rate, there still appears a big difference in web usage rates depending on demographic variables, web usage environment variables. This study aimed to improve our understanding of perceived usage, attitude, affected factors related Web usage for schoolwork and research. This result will facilitate further understanding of perceived usage, attitude, affected factors related Web usage, thereby enabling researchers, practitioners, and policy makers to better design appropriate strategies to promote the Web usage.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.831-840
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• 2001

In this study, We introduce the process of web usage mining, which has lately attracted considerable attention with the fast diffusion of world wide web, and explain the web log data, which Is the main subject of web usage mining. Also, we illustrate some real examples of analysis for web log data and look into practical application of web usage mining for eCRM.

• Journal of Digital Convergence
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• v.7 no.2
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• pp.83-91
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• 2009

This study aimed at improving our understanding of education-related Web usage for schoolwork and research in universities. This study found out a list of factors that influence the usage of education-related Web sites by Korean and New Zealand university students and suggested the features of those factors. These findings will enhance further understanding of factors associated with Education-related Web usage and the relationship among attitude, intention to use, and performance, which could help web practitioners design and develop education-related Web sites to promote web usage and attract university students into their web sites.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.204-209
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• 2002

The interest of data mining in artificial intelligence with fuzzy logic has been increased. Data mining is a process of extracting desirable knowledge and interesting pattern ken large data set. Because of expansion of WWW, web data is more and more huge. Besides mining web contents and web structures, another important task for web mining is web usage mining which mines web log data to discover user access pattern. The goal of web usage mining in this paper is to find interesting user pattern in the web with user feedback. It is very important to find user's characteristic fer e-business environment. In Customer Relationship Management, recommending product and sending e-mail to user by extracted users characteristics are needed. Using our method, we extract user profile from the result of web usage mining. In this research, we concentrate on finding association rules and verify validity of them. The proposed procedure can integrate fuzzy set concept and association rule. Fuzzy association rule uses given server log file and performs several preprocessing tasks. Extracted transaction files are used to find rules by fuzzy web usage mining. To verify the validity of user's feedback, the web log data from our laboratory web server.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2008.10b
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• pp.301-306
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• 2008

The World Wide Web (WWW) has continued to grow at very rapid speed in both the sheer volume of traffic and size and the complexity of Web sites. Web Analytics Industry also has been growing rapidly. Web Analytics is to analyze web log files to discover accessing patterns of web pages. In this paper, we identify factors which can affect the continuous usage intention of a firm using services in web analytics services and empirically validate the relationships between the identified factors. For this purpose, we analyze 174 Korea firms. The analysis results show that the satisfaction is significantly associated with service quality and switching cost and the service usage period is not significantly associated with continuous service usage intention. We measure the service quality using SERVQUAL. It turn out that two dimensions of SERVQUAL, reliability and empathy are significantly associated with satisfaction, but another dimension of SERVQUAL, responsibility, is not. Finally, satisfaction is significantly associated with continuous service usage intention.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.143-146
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• 2004

With the rapid growth of e-commerce, marketers are able to observe not only purchasing behavior on what and when customers purchased, but also the individual Web usage behavior that affect purchasing. The richness of this information has the potential to provide marketers with an in-depth understanding of customer. Using commonly available Web log data, this paper examines Web usage behaviors at the individual level. By decomposing the buying process into a pattern of visits and purchase conversion at each visit, we can better understand the relationship between Web usage behavior and purchase decision. This allows us to more accurately forecast a shopper's future purchase decision at the site and hence determine the value of individual customers to the siteAccording to our research, not only information seeking behavior but also visiting duration of a customer and participative behavior such as participation in event should be considered as important predicators of purchase decision of customer in a cosmetic internet shopping mall.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.88-93
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• 2005

Web usage mining is a research field for searching potentially useful and valuable information from web log file. Web log file is a simple list of pages that users refer. Therefore, it is not easy to analyze user's current interest field from web log file. This paper presents web usage mining method for finding users' current interest based on fuzzy categories. We consider not only how many times a user visits pages but also when he visits. We describe a user's current interest with a fuzzy interest degree to categories. Based on fuzzy categories and fuzzy interest degrees, we also propose a method to cluster users according to their interests for user modeling. For user clustering, we define a category vector space. Experiments show that our method properly reflects the time factor of users' web visiting as well as the users' visit number.

• The Journal of Bigdata
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• v.2 no.2
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• pp.105-114
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• 2017

Today, many services are supported on the web sites. Analysis of usage patterns of web site visitors is very important to optimize the use and efficiency of the web sites. In this study, analysis of usage patterns and comparative analysis of user groups were conducted by analyzing web access log provided by BPI Challenge 2016. This data provides access logs to the web site in the IT system of a Dutch Employee Insurance Agency (UWV). The customer information, and the click data describing the customers' behavior when using the agency's web site. In this study, we use process mining techniques to analyze the usage patterns of customers and the characteristics of customer groups, and ultimately improve the service quality of customers using web services.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.389-390
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• 2002

It is important that acquire information about if customer has some habit in electronic commerce application of internet base that led in recommendation service for customer in dynamic web contents supply. Collaborative filtering that has been used as a standard approach to Web personalization can not get rapidly user's preference change due to static user profiles and has shortcomings such as reliance on user ratings, lack of scalability, and poor performance in the high-dimensional data. In order to overcome this drawbacks, Web usage mining has been prevalent. Web usage mining is a technique that discovers patterns from We usage data logged to server. Specially. a technique that discovers Web usage patterns and clusters patterns is used. However, the discovery of patterns using Afriori algorithm creates many useless patterns. In this paper, the enhanced method for the construction of dynamic user profiles using validated Web usage patterns is proposed. First, to discover patterns Apriori is used and in order to create clusters for user profiles, ARHP algorithm is chosen. Before creating clusters using discovered patterns, validation that removes useless patterns by Dempster-Shafer theory is performed. And user profiles are created dynamically based on current user sessions for Web personalization.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.14 no.4
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• pp.97-110
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• 2004

As modem web services become enormously complex, web attacks has become frequent and serious. Existing security solutions such as firewalls or signature-based intrusion detection systems are generally inadequate in securing web services, and analysis of raw web log data is simply impractical for most organizations. Visual display of "interpreted" web logs, with emphasis on anomalous web requests, is essential for an organization to efficiently track web usage patterns and detect possible web attacks. In this paper, we discuss various issues related to effective real-time visualization of web usage patterns and anomalies. We implemented a software tool named SAD (session anomaly detection) Viewer to satisfy such need and conducted an empirical study in which anomalous web traffics such as Misuse attacks, DoS attacks, Code-Red worms and Whisker scans were injected. Our study confirms that SAD Viewer is useful in assisting web security engineers to monitor web usage patterns in general and anomalous web sessions in particular.articular.