• 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.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.225-236
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• 2004

Every web server comprises a repository of all actions and events that occur on the server. Server logs can be used to quantify user traffic. Intelligent analysis of this data provides a statistical baseline that can be used to determine server load, failed requests and other events that throw light on site usage patterns. This information provides valuable leads on marketing and site management activities. In this paper, we propose a method of design for log analysis system using RTMA(realtime monitoring and analysis) technique.

• Journal of Intelligence and Information Systems
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• v.7 no.1
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• pp.97-121
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• 2001

Until now, we have believed that one of advantages of cyber market is that it can virtually display and sell goods because it does not necessary maintain expensive physical shops and inventories. But, in a highly competitive environment, business model that does away with goods in stock must be modified. As we know in the case of AMAZON, leading companies already consider merchandise management as a critical success factor in their business model. That is, a solution to compete against one's competitors in a highly competitive environment is merchandise management as in the traditional retail market. Cyber market has not only past sales data but also web log data before sales data that contains information of path that customer search and purchase on cyber market as compared with traditional retail market. So if we can correctly analyze the characteristics of before sales patterns using web log data, we can better prepare for the potential customers and effectively manage inventories and merchandises. We introduce a systematic analysis method to extract useful data for merchandise management - demand forecasting, evaluating & selecting - using web mining that is the application of data mining techniques to the World Wide Web. We use various techniques of web mining such as clustering, mining association rules, mining sequential patterns.

• 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 Information Technology Applications and Management
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• v.21 no.4
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• pp.51-64
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• 2014

The purpose of operating the main homepage of University is to provide the related information about University resources to site visitors. In this study, we analyze website browsing patterns and extract characteristics of users in order to improve its utilization. The access log files to main homepage were used to analyze the browsing patterns and converted to process log files adaptable to a process mining tool, ProM. Finally we provide useful information about user friendly homepage design and suggest plans for improving its utilization to website operators.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.945-954
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• 2002

Clickstream is the information which demonstrate users' path through web sites. Analysis of clickstream shows how web sites are navigated and used by users. Clickstream of online web sites contains effective information of web marketing and to offers usefully personalized services to users, and helps us understand how users find web sites, what products they see, and what products they purchase. In this paper, we present an extended web log system that add to module of collection of clickstream to understand users' behavior patterns In web sites. This system offers the users clickstream information to database which can then analyze it with ease. Using ADO technology in store of database constructs extended web log server system. The process of making clickstreaming into database can facilitate analysis of various user patterns and generates aggregate profiles to offer personalized web service. In particular, our results indicate that by using the users' clickstream. We can achieve effective personalization of web sites.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.443-446
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• 2002

In this research, we suggest a Web-based hybrid recommendation mechanism using CBR (Case-Based Reasoning) and web data mining. Data mining is used as an efficient mechanism in reasoning for relationship between goods, customers' preference and future behavior. CBR systems are normally used in problems for which it is difficult to define rules. We use CBR as an AI tool to recommend the similar purchase case. A Web-log data gathered in real-world Internet shopping mall was given to illustrate the quality of the proposed mechanism. The results showed that the CBR and web data mining-based hybrid recommendation mechanism could reflect both association knowledge and purchase information about our former customers.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.366-370
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• 2003

In this research, we suggest a hybrid intelligent Web recommendation systems based on Web data mining and case-based reasoning (CBR). One of the important research topics in the field of Internet business is blending artificial intelligence (AI) techniques with knowledge discovering in database (KDD) or data mining (DM). Data mining is used as an efficient mechanism in reasoning for association knowledge between goods and customers＇ preference. In the field of data mining, the features, called attributes, are often selected primary for mining the association knowledge between related products. Therefore, most of researches, in the arena of Web data mining, used association rules extraction mechanism. However, association rules extraction mechanism has a potential limitation in flexibility of reasoning. If there are some goods, which were not retrieved by association rules-based reasoning, we can＇t present more information to customer. To overcome this limitation case, we combined CBR with Web data mining. CBR is one of the AI techniques and used in problems for which it is difficult to solve with logical (association) rules. A Web-log data gathered in real-world Web shopping mall was given to illustrate the quality of the proposed hybrid recommendation mechanism. This Web shopping mall deals with remote-controlled plastic models such as remote-controlled car, yacht, airplane, and helicopter. The experimental results showed that our hybrid recommendation mechanism could reflect both association knowledge and implicit human knowledge extracted from cases in Web databases.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.91-95
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• 2007

Main goal of web recommendation system is to study how user behavior on a website can be predicted by analyzing web log data which contain the visited web pages. Many researches of the web recommendation system have been studied. To construct web recommendation system, web mining is needed. Especially, web usage analysis of web mining is a tool for recommendation model. In this paper, we propose web recommendation system using grid based support vector machines for improvement of web recommendation system. To verify the performance of our system, we make experiments using the data set from our web server.

• The KIPS Transactions:PartC
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• v.8C no.6
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• pp.847-854
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• 2001

The Web continues to grow fast rate in both a large aclae volume of traffic and the size and complexity of Web sites. Along with growth, the complexity of tasks such as Web site design Web server design and of navigating simply through a Web site have increased. An important input to these design tasks is the analysis of how a web site is being used. The is paper proposes a Page logging System(PLS) identifying reliably user sessions required in Web mining system PLS consists of Page Logger acquiring all the page accesses of the user Log processor producing user session from these data, and statements to incorporate a call to page logger applet. Proposed PLS abbreviates several preprocessing tasks which spends a log of time and efforts that must be performed in Web mining systems. In particular, it simplifies the complexity of transaction identification phase through acquiring directly the amount of time a user stays on a page. Also PLS solves local cache hits and proxy IPs that create problems with identifying user sessions from Web sever log.