• Journal of Korea Society of Industrial Information Systems
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• v.18 no.2
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• pp.47-57
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• 2013

Today a lot of functional areas of a firm are operated on the Web. Online shopping malls analyze web log recording customers' activities on the web to connect them to business outcomes. Not only commercial websites, but online news sites also need to collect and analyze web logs to understand their news readers' interest. However, little research has been performed yet. In this research we mined the web access log of an online news website and conduct Market Basket Analysis to uncover the association rules among the categories of news articles. The research is composed of two stages: 1) Identifying the individual session of a visitor; 2) Mining association rule from news articles read by each session. We gather 7-day access logs two times. The results of log mining and meanings of association rules are suggested with managerial implications in conclusion section.

• 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.

• The Journal of the Korea Contents Association
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• v.8 no.5
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• pp.9-18
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• 2008

Web-services have originally been devised to share information as open services. In connection with it, hacking incidents have surged. Currently, Web-log analysis plays a crucial clue role in detecting Web-hacking. A growing number of cases are really related to perceiving and improving the weakness of Web-services based on Web-log analysis. Such as this, Web-log analysis plays a central role in finding out problems that Web has. Hence, Our research thesis suggests Web-DoS-hacking detective technique In the process of detecting such problems through SOM algorithm, the emergence frequency of BMU(Best Matching Unit) was studied, assuming the unit with the highest emergence frequency, as abnormal, and the problem- detection technique was recommended through the comparison of what's called BMU as input data.

• Journal of the Korean Society for Library and Information Science
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• v.41 no.1
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• pp.231-242
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• 2007

Transaction logs capture the interactions between online information retrieval systems and the users. Given the nature of the Web and Web users, transaction logs appear to be a reasonable and relevant method to collect and investigate information searching behaviors from a large number of Web users. Based on a series of research studies that analyzed Naver transaction logs, this study examines how transaction log analysis can be applied and contributed to the field of web searching and suggests future implications for the web searching field. It is expected that this study could contribute to the development and implementation of more effective Web search systems and services.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10a
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• pp.4-6
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• 2001

In this paper, we designed a system that can mine user's traversal patterns from web log files. The system cleans an input data, transactions of a web log file, and finds traversal patterns from the transactions, each of which consists of one user's access pages. The resulting traversal patterns are shown on a web browser, which can be used to analyze the patterns in visual form by a system manager or data miner. We have implemented the system in an IBM personal computer running on Windows 2000 in MS visual C++, and used the MS SQL Server 2000 to store the intermediate files and the traversal patterns which can be easily applied to a system for knowledge discovery in databases.

• Proceedings of the Korea Society of Design Studies Conference
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• 2001.10a
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• pp.12.2-12
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• 2001

• 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.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.681-689
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• 2019

Web usage pattern discovery is an advanced means by using web log data, and it's also a specific application of data mining technology in Web log data mining. In education Data Mining (DM) is the application of Data Mining techniques to educational data (such as Web logs of University, e-learning, adaptive hypermedia and intelligent tutoring systems, etc.), and so, its objective is to analyze these types of data in order to resolve educational research issues. In this paper, the Web log data of a university are used as the research object of data mining. With using the database OLAP technology the Web log data are preprocessed into the data format that can be used for data mining, and the processing results are stored into the MSSQL. At the same time the basic data statistics and analysis are completed based on the processed Web log records. In addition, we introduced the Apriori Algorithm of Web usage pattern mining and its implementation process, developed the Apriori Algorithm program in Python development environment, then gave the performance of the Apriori Algorithm and realized the mining of Web user access pattern. The results have important theoretical significance for the application of the patterns in the development of teaching systems. The next research is to explore the improvement of the Apriori Algorithm in the distributed computing environment.

• 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 Korea Society for Industrial Systems Conference
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• 2003.11a
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• pp.621-626
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• 2003

Mining association rules in web log files can be divided into two steps: 1) discovering frequent item sets in web data; 2) extracting association rules from the frequent item sets found in the previous step. This paper suggests an algorithm for finding frequent item sets efficiently The essence of the proposed algorithm is to transform transaction data files into matrix format. Our experimental results show that the suggested algorithm outperforms the Apriori algorithm, which is widely used to discover frequent item sets, in terms of scan frequency and execution time.