• 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.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.23-34
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• 2008

The number of web service user is increasing steadily as web-based service is offered in various form. But, web service has a vulnerability such as SQL Injection, Parameter Injection and DoS attack. Therefore, it is required for us to develop Web IDS system and additionally to offer Rule-base intrusion detection/response mechanism against those attacks. However, existing Web IDS system didn't correspond properly on recent web attack mechanism because they didn't including suitable pre-processing procedure on huge web log data. Therfore, we propose an efficient web log pre-processing mechanism for enhancing rule based detection and improving the performance of web IDS base attack response system. Proposed algorithm provides both a field unit parsing and a duplicated string elimination procedure on web log data. And it is also possible for us to construct improved web IDS system.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.165-172
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• 2020

Recently, due to exponential growth of access information on the web, the importance of predicting a user's next web page use has been increasing. One of the methods that can be used for predicting user's next web page is deep learning. To predict next web page, web logs are analyzed by data preprocessing and then a user's next web page is predicted on the output of the analyzed web logs using a deep learning algorithm. In this paper, we propose a framework for web page prediction that includes methods for web log preprocessing followed by deep learning techniques for web prediction. To increase the speed of preprocessing of large web log, a Hadoop based MapReduce programming model is used. In addition, we present a web prediction system that uses an efficient deep learning technique on the output of web log preprocessing for training and prediction. Through experiment, we show the performance improvement of our proposed method over traditional methods. We also show the accuracy of our prediction.

• Journal of Korea Multimedia Society
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• v.6 no.4
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• pp.707-713
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• 2003

Convenience and promptness of the internet have been not only making the electronic commerce grow rapidly in case of website, analyzing a navigation pattern of the users has been also making personalization and customization techniques develop rapidly for providing service accordant to individual interestingness. Web personalization and customization skill has been utilizing various methods, such as web log mining to use web log data and web mining to use the transaction of users etc, especially e-CRM analyzing a navigation pattern of the users. In this paper, We measure exact duration time of the users in web page and web site, compute weight about duration time each page, and propose a way to comprehend e-loyalty through the computed weight.

• The KIPS Transactions:PartB
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• v.16B no.4
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• pp.309-318
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• 2009

The researches on the system that automatically records and retrieves one's everyday life is relatively actively worked recently. These systems, called personal memex or life log, usually entail dedicated devices such as SenseCam in MyLifeBits project. This research paid attention to the digital devices such as mobile phones, credit cards, and digital camera that people use everyday. The system enables a person to store everyday life systematically that are saved in the devices or the deviced-related web pages (e.g., phone records in the cellular phone company) and to refer this quickly later. The data collection agent in the proposed system, called MyMemex, collects the personal life log "web data" using the web services that the web sites provide and stores the web data into the server. The "file data" stored in the off-line digital devices are also loaded into the server. Each of the file data or web data is viewed as a memex event that can be described by 4W1H form. The different types of data in different services are transformed into the memex event data in 4W1H form. The memex event ontology is used in this transform. Users can sign in to the web server of this service to view their life logs in the chronological manner. Users can also search the life logs using keywords. Moreover, the life logs can be viewed as a diary or story style by converting the memex events to sentences. The related memex events are grouped to be displayed as an "episode" by a heuristic identification method. A result with high accuracy has been obtained by the experiment for the episode identification using the real life log data of one of the authors.

• 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.

• 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.

• Journal of Information Processing Systems
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• v.7 no.4
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• pp.707-716
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• 2011

It is important that desktop grids should be able to aggressively deal with the dynamic properties that arise from the volatility and heterogeneity of resources. Therefore, it is required that task scheduling be able to positively consider the execution behavior that is characterized by an individual resource. In this paper, we implement a log analysis system with REST web services, which can analyze the execution behavior by utilizing the actual log data of desktop grid systems. To verify the log analysis system, we conducted simulations and showed that the resource group-based task scheduling, based on the analysis of the execution behavior, offers a faster turnaround time than the existing one even if few resources are used.

• 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.

• 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.