• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.3
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• pp.204-209
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• 2002

The interest of data mining in artificial intelligence with fuzzy logic has been increased. Data mining is a process of extracting desirable knowledge and interesting pattern ken large data set. Because of expansion of WWW, web data is more and more huge. Besides mining web contents and web structures, another important task for web mining is web usage mining which mines web log data to discover user access pattern. The goal of web usage mining in this paper is to find interesting user pattern in the web with user feedback. It is very important to find user's characteristic fer e-business environment. In Customer Relationship Management, recommending product and sending e-mail to user by extracted users characteristics are needed. Using our method, we extract user profile from the result of web usage mining. In this research, we concentrate on finding association rules and verify validity of them. The proposed procedure can integrate fuzzy set concept and association rule. Fuzzy association rule uses given server log file and performs several preprocessing tasks. Extracted transaction files are used to find rules by fuzzy web usage mining. To verify the validity of user's feedback, the web log data from our laboratory web server.

• 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.

• Journal of the Korean Society for Library and Information Science
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• v.45 no.2
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• pp.209-228
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• 2011

Web search behavior has evolved. People now search using many diverse information devices in various situations. To monitor these scattered and shifting search patterns, an improved way of learning and analysis are needed. Traditional web search studies relied on the server transaction logs and single query instance analysis. Since people use multiple smart devices and their searching occurs intermittently through a day, a bundled query research could look at the whole context as well as penetrating search needs. To observe and analyze bundled queries, we developed a proprietary research software set including a log catcher, query bundling tool, and bundle monitoring tool. In this system, users' daily search logs are sent to our analytic server, every night the users need to log on our bundling tool to package his/her queries, a built in web survey collects additional data, and our researcher performs deep interviews on a weekly basis. Out of 90 participants in the study, it was found that a normal user generates on average 4.75 query bundles a day, and each bundle contains 2.75 queries. Query bundles were categorized by; Query refinement vs. Topic refinement and 9 different sub-categories.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.223-232
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• 2017

Previous research on data recovery in Microsoft SQL Server has focused on restoring data based on in the transaction log that might have deleted records exist. However, there was a limit that was not applicable if the related transaction log did not exist or the physical database file was not connected to Server. Since the suspect in the crime scene may delete the data records using a different deletion statements besides "delete", we need to check the remaining data and a recovery possibility of the deleted record. In this paper, we examined the changes "Page Allocation information" of the table, "Unallocation deleted data", "Row Offset Array" in the page according to "delete", "truncate" and "drop" events. Finally it confirmed the possibility of data recovery and availability of management tools in Microsoft SQL Server digital forensic investigation.

• Journal of KIISE
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• v.42 no.4
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• pp.442-450
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• 2015

This paper proposes a method for reducing false conflicts in signature-based eager hardware transactional memory (HTM). The method tracks the information on all cache blocks that are accessed by a transaction. If the information provides evidence that there are no conflicts for a given transactional request from another core, the method prevents the occurrence of a false conflict by forcing the HTM to ignore the decision based on the signature. The method is very effective in reducing false conflicts and the associated unnecessary transaction stalls and aborts, and can be used to improve the performance of the multicore processor that implements the signature-based eager HTM. When running the STAMP benchmark on a 16-core processor that implements the LogTM-SE, the increased speed (decrease in execution time) achieved with the use of the method is 20.6% on average.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.1
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• pp.25-38
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• 2022

With the diversification of payment methods and games, related financial accidents are causing serious problems for users and game companies. Recently, game companies have introduced an Fraud Detection System (FDS) for game payment systems to prevent financial incident. However, FDS is ineffective and cannot provide major evidence based on judgment results, as it requires constant change of detection patterns. In this paper, we analyze abnormal transactions among payment log data of real game companies to generate related features. One of the unsupervised learning models, Autoencoder, was used to build a model to detect abnormal transactions, which resulted in over 85% accuracy. Using X-FDS (Explainable FDS) with XAI-SHAP, we could understand that the variables with the highest explanation for anomaly detection were the amount of transaction, transaction medium, and the age of users. Based on X-FDS, we derive an improved detection model with an accuracy of 94% was finally derived by fine-tuning the importance of features that adversely affect the proposed model.

• Journal of Internet Computing and Services
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• v.10 no.1
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• pp.111-121
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• 2009

In this paper, we propose a multimedia recommender system using user's playback time. Proposed system collects multimedia content which is requested by user and its user‘s playback time, as web log data. The system predicts playback time.based preference level and related contents from collected transaction database by fuzzy association rule mining. Proposed method has a merit which sorts recommendation list according to preference without user’s custom preference data, and prevents a false preference. As an experimental result, we confirm that proposed system discovers useful rules and applies them to recommender system from a transaction which doesn‘t include custom preferences.

• Management Science and Financial Engineering
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• v.11 no.3
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• pp.95-107
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• 2005

As online shopping malls have emerged as a substantial shopping channel, they have used various sales promotion strategies to acquire new customers. Most of these strategies have been applied by offline malls for years. One, loss-leader pricing, is a type of promotional pricing in which stores sell well known products below their marginal cost, in order to attract customers and induce them to purchase more goods through impulse buying. This strategy is based on the expectation that customers will factor transaction costs into their purchasing decisions. However, its application to online malls fails to recognize that transaction costs are lower online, and that customers will behave differently as a result. Our study predicts that loss-leader pricing will not work online because online malls entail lower searching and moving costs than offline malls The study examines the effectiveness of loss-leader pricing with empirical data from a survey as well as log data from a Korean online shopping mall. The results show that while loss-leader pricing does attract customers to online shopping malls, it encourages cherry-picking rather than impulse purchases of regular-price goods.

• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.949-960
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• 2003

Main memory database systems use the different implementation techniques to sturucture and organize the user dta and system catalogs, since traditional database systems are optimized for the characteristics of disk storage environment. We present, in this paper, the design considerations for our main memory database system $ALTIBASE^{TM}$ that is currently applied to the time-critical applications. We focus on the design issues of storage manager in $ALTIBASE^{TM}$. The major components are introduced, and features and characteristics of transaction management and recovery method are described. We also present the database replication mechanism and its conflict resolution mechanism for high availability and performance. In order to evaluate our transaction performance, we show various experimental reports as measured by the TPS.

• Journal of Digital Contents Society
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• v.15 no.5
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• pp.631-642
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• 2014

The main memory DBMS is operated which the contents of the table that resides on a disk at the same time as the drive is in the memory. However, because the main memory DBMS stores the data and transaction log file using the disk file system, there are a limit to the speed at which the CPU accesses the memory. In this paper, I evaluated the performance through analysis of the application side difference the technology that has been implemented in Altibase system of main memory DBMS and Sybase of disk-based DBMS. When the application performance of main memory DBMS is in comparison with the disk-based DBMS, the performance of main memory DBMS was outperformed 1.24~3.36 times in the single soccer game, and was outperformed 1.29~7.9 times in the soccer game / special soccer. The result of sale transaction response time showed a fast response time of 1.78 ~ 6.09 times.