• Journal of Information Technology Applications and Management
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• v.13 no.1
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• pp.1-16
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• 2006

This paper empirically analyzed consumer price search behavior using Web log data of a Korean web site for price comparison. Consumer click-stream data of the site was used to test the effects of price level, product category, third party certification, reputation of retailers on click behavior. According to the descriptive statistics, 67.4% of shopbot users clicked the offer which was the lowest price returned in a search. We found that third party certification and reputation of retailers were significant determinants of clicking the lowest priced offer from legit analysis. We also applied Tobit regression analysis to estimate the price premium of the two determinants, but only reputation of retailers was found to have price premium of 4.9%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.249-251
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• 2021

본 논문에서는 WEB대상으로 UI테스트를 최초 설계할 때, 웹로그를 분석하여 사용자들의 패턴을 파악하고 자동으로 테스트 시나리오와 케이스를 설계하여 제공하는 시스템을 제안한다. 이 시스템은 메세지큐를 활용하여 로그 데이터를 효율적으로 수집할 수 있고, 분석 시스템과 사용자들이 사용하는 웹서버를 분리하여 로그분석으로 인한 시스템 과부하 현상을 예방 할 수 있다. 또한 로그분석을 통해 추출된 데이터를 통해 사용자들이 실제로 자주 사용하거나 사용했던 사용 경로를 이용하여 자동으로 테스트 시나리오와 테스트 케이스에 대한 자료들을 접할 수 있어 테스트 분석, 설계 과정에서의 소요되는 시간이 감축되는 것을 기대할 수 있으며, 실제 사용자들이 자주 이용하는 패턴으로 테스트 대상을 정할 수 있기 때문에 후에 테스트를 통한 결함이 조치가 된다면 사용자들이 결함 경험을 줄일 수 있을 것이라 기대한다.

• The KIPS Transactions:PartD
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• v.10D no.2
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• pp.277-282
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• 2003

The knowledge discovery from web has been studied in many researches. There are some difficulties using web log for training data on efficient information predictive models. In this paper, we studied on the method to eliminate sparseness from web log data and to perform web user clustering. Using missing value imputation by Bayesian inference of MCMC, the sparseness of web data is removed. And web user clustering is performed using self organizing maps based on 3-D plot by principal component. Finally, using KDD Cup data, our experimental results were shown the problem solving process and the performance evaluation.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.5
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• pp.236-244
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• 2006

Many attempts have been made to track the web usage patterns and provide suggestions that might help web operators get the information they need. These tracking mechanisms rely on mining web log files for usage patterns. The purpose of this study is to verify a web agent prototype that was built for mining web log files. The web agent for this paper was made by Java and ASP and the agent came into being as part of a cookie for a short-term data storage. For long-term data storage, the agent used a My-SQL as a Data Base. This agent system could inform that if the data comes from the web data mining agent, it could be a rapid information providing method rather than the case of data coming into a data mining tool. Therefore, the developed tool in this study will be helpful as a new kind of decision making system and expert system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.341-344
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• 2004

웹 사용 마이닝(Web usage mining)은 웹 로그 파일(web log file)이나 웹 사용 데이터(Web usage data)에서 의미 있는 정보를 찾아내는 연구 분야이다. 웹 사용 마이닝에서 일반적으로 많이 사용하는 웹 로그 파일은 사용자들이 참조한 페이지의 단순한 리스트들이다. 따라서 단순히 웹 로그 파일만을 이용하는 방법만으로는 사용자가 참조했던 페이지의 내용을 반영하여 분석하는데에는 한계가 있다. 이러한 점을 개선하고자 본 논문에서는 페이지 위주가 아닌 웹 페이지가 포함하고 있는 내용(아이템)을 고려하는 새로운 퍼지 카테고리 기반의 웹 사용 마이닝 기법을 제시한다. 또한 사용자를 잘 파악하기 위해서 시간에 따라 관심의 변화를 파악하는 방법을 제시한다.

• 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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.192-197
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• 2001

A recommendation system tracks past actions of a group of users to make a recommendation to individual members of the group. The computer-mediated marketing and commerce have grown rapidly nowadays so the concerns about various recommendation procedures are increasing. We introduce a recommendation methodology by which e-commerce sites suggest new products of services to their customers. The suggested methodology is based on web log analysis, product taxonomy, and association rule mining. A product recommendation system is developed based on our suggested methodology and applied to a Korean internet shopping mall. The validity of our recommendation system is discussed with the analysis of a real internet shopping mall case.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1089-1098
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• 2007

Currently, user require is diverse on the Web. Furthermore, each web user is wishing to retrieve data or goods that hey want to look for more conveniently and more quickly. Because different search criteria and dispositions of web users, they lead to unnecessary repeated operations in order to use implemented by web designer. In this paper, we suggest the system that analyzes user patterns on the Web using the technique of log file analysis and transfers more effectively the information of web sites to users. And we analyze the log file for customer data in the system the proposed method are implemented by means of EC-Miner that is one of the tool of datamining, and aims to offer appropriate Layout corresponding with personalization by giving weight to each transport path.

• 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.

• The Journal of Korean Association of Computer Education
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• v.9 no.4
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• pp.55-61
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• 2006

The Personal Software Process(PSP) is a rigorous, measurements based process designed to help software developers produce high-quality codes. Learning PSP requires substantial and tedious record keeping including clerical computations with plenty of data occurred at every project. The required task reduces the educational effect and motive by time consuming characteristic and easy occurrence of errors. To alleviate these problems, a web-based PSP log system having streamlined mechanism of data manipulation is designed and implemented at this study. Three years of experiment shows that the system not only improves productivity and accuracy of the process but also allows students and instructors to focus on learning and teaching PSP, rather than PSP data collection.