• Industrial Engineering and Management Systems
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• v.11 no.1
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• pp.82-86
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• 2012

In For the past few years, query log data has been collected to find user's behavior in using the site. Many researches have studied on the usage of query logs to extract user's preference, recommend personalization, improve caching and pre-fetching of Web objects, build better adaptive user interfaces, and also to improve Web search for a search engine application. A query log contain data such as the client's IP address, time and date of request, the resources or page requested, status of request HTTP method used and the type of browser and operating system. A query log can offer valuable insight into web site usage. A proper compilation and interpretation of query log can provide a baseline of statistics that indicate the usage levels of website and can be used as tool to assist decision making in management activities. In this paper we want to discuss on the tasks performed of query logs in pre-processing of web usage mining. We will use query logs from an online newspaper company. The query logs will undergo pre-processing stage, in which the clickstream data is cleaned and partitioned into a set of user interactions which will represent the activities of each user during their visits to the site. The query logs will undergo essential task in pre-processing which are data cleaning and user identification.

• 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.

• 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.

• 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.

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.123-127
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• 2000

인터넷이 데이터 저장 및 서비스를 위한 도구로 폭넓게 활용되고 있으며, 이 과정에서 웹 서버 방문객에 대한 정보인 로그가 발생된다. 이러한 로그는 방문객 주소, 참조 페이지, 방문 시각 등의 정보를 포함하고 있다. 웹 로그에 대하여 패턴분석(pattern analysis), 군집분석(clustering), 판별분석(classification) 등의 통계적 분석을 통하여 방문객이 관심을 가지는 항목이나 항목간의 연관관계 등 새로운 정보를 생성하여 웹 디자인 또는 비즈니스에의 적용에 대한 연구가 활발히 논의되고 있다. 본 연구에서는 웹 로그 분석에 대하여 소개하고 웹 로그 분석을 위한 방안을 제시하고자 한다.

• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.107-120
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• 2003

In Web Based Instruction, as evaluation of learning process means individual student's learning activity, it demands data on learning time, pattern, participation, environment in a specific learning contents. The purpose of this paper is to reflect analysis results of individual student's learning status in achievement evaluation using the most suitable web log mining to settle evaluation problem of learning process, an issue in web based instruction. The contents and results of this study are as following. First, conformity item for learning status analysis is determined and web log data preprocessing is executed. Second, on the basis of web log data, I construct student's database and analyze learning status using data mining techniques.

• 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.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.11a
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• pp.29-32
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• 2006

In this paper, we propose a customer personalized system that presents the web pages to users which are customized to their individuality. It analyzes the action of users who visit the shopping mall, and preferentially supplies the necessary information to them. When they actually buy some items, it forecasts the users' access pattern to web site and their following purchasable items and improves their web pare on the bases of their individuality. It reasons the relation among the web documents and among the items by using the log data of web server and the purchase information of DB. For reasoning it employs Rough Set, which is a method that searches the association rule and offers most suitable cases by reduces cases. It reasons the web pages by considering the users' access pattern and time by using the web log and reasons the users' purchase pattern by using the purchase information of DB. On the basis of the relation among them, it appends the related web pages to link of users' web pages and displays the inferred goods on users' web pages.

• IE interfaces
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• v.18 no.2
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• pp.205-220
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• 2005

This study presents a web log analysis model for e-CRM, which combines the on-line customer's purchasing pattern data and transaction data between companies in B2B environment of make-to-order company. With this study, the customer evaluation and the customer subdivision are available. We can forecast the estimate demands with periodical products sales records. Also, the purchasing rate per each product, the purchasing intention rate, and the purchasing rate per companies can be used as the basic data for the strategy for receiving the orders in future. These measures are used to evaluate the business strategy, the quality ability on products, the customer's demands, the benefits of customer and the customer's loyalty. And it is used to evaluate the customer's purchasing patterns, the response analysis, the customer's secession rate, the earning rate, and the customer's needs. With this, we can satisfy various customers' demands, therefore, we can multiply the company's benefits. And we presents case of the 'H' company, which has the make-to-order manufacture environment, in order to verify the effect of the proposal system.