• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.4
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• pp.39-45
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• 2011

Application log files contain error messages; operational data and usage information that can help manage applications and servers. Log analysis system is software that read and parse log files, extract and aggregate information in order to generate reports on the application. In currently, the importance of log files of firewalls is growing bigger and bigger for the forensics of cyber crimes and the establishment of security policy. In this paper, we designed and implemented the SILAS(SysLog-based Integrated Log mAanagement System) in distribute network environments. It help to generate reports on the the log fires of firewalls - IP and users, and statistics of application usage.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.51-56
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• 2009

Flash memory has rapidly deployed as data storage. However, the flash memory has a major disadvantage that recorded data cannot be dynamically overwritten. In order to solve this "erase-before-write" problem, the log block buffer scheme used Flash memory file system. however, the current managements of the log buffer, in case random write pattern, BAST technique have problem of frequent merge operation, but FAST technique don't consider merge operation by frequently updated data. Previous methods not consider merge operation cost and frequently updated data. In this paper, we propose a new log buffer management scheme, called JBB. Our proposed method evaluates the worth of the merge of log blocks, so we conducts the merge operation between infrequently updated data and its data blocks, and postpone the merge operation between frequently updated data and its data blocks. Through the method, we prevent the unnecessary merge operations, reduce the number of the erase operation, and improve the utilization of the flash memory storage. We show the superiority of our proposed method through the performance evaluation with BAST and FAST.

• The Journal of the Korea Contents Association
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• v.21 no.10
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• pp.1-10
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• 2021

Log data accumulated in the Learning Management System (LMS) provide high-quality information for the learning process of students. Until now, various studies have been conducted to predict students' academic achievement using LMS log data. However, previous studies were based on relatively small sample sizes of students and courses, limiting the possibility of generalization. This study developed and validated a deep neural network model for the early prediction of academic achievement of college students using massive LMS log data. To this end, we used 78,466,385 cases of LMS log data and 165,846 cases of grade data. The proposed model predicted the excellent-grade students with a high level of accuracy from the beginning of the semester. Meanwhile, the prediction accuracy for the moderate and underachieving groups was relatively low, but the accuracy improved as the time points of the prediction were delayed. This study is meaningful in that we developed an early prediction model based on a deep neural network with sufficient accuracy for practical utilization by only using LMS log data.

• Water for future
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• v.29 no.1
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• pp.129-139
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• 1996

It is very important to select appropriate distributions for hydrological data in planning and designing hydraulic structures. Also, it is necessary to check whether the selected distribution reproduces the statistical characteristics of the real data. In this study, the parameters of the two- and three-parameter gamma, two- and three-parameter lognormal, Gumbel, two- and three-parameter log-Gumbel, GEV, log-Pearsonn type III, two- and three-parameter Weibull, four- and five-parameter Wakeby distributions were estimated for the rainfall data of 22 sites in Korea with 7 different durations based on the methods of moments, probability weighted moments, and maximum likelihood. And the validity conditions were checked for the estimated parameters. The separation effect for each distribution was examined throught 10,000 simulations using the estimated parameters. As results, the separation effect was the smallest: log-Pearson type III for moment method, log-Pearson type III and GEV for probability weighted moment method, and GEV for maximum likelihood method. However, it is large for the two-parameter distributions.

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.317-329
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• 2004

The Web log has caught much attraction for tracing of customer activity. So many researches have been carried on it. As a result, Web log analysis solutions has been developed and launched lately. It has been in the spotlight to the website administrators and people in practical marketing business. In this paper, we made an analysis on the various behavior patterns of customers in cooperation with SAS/AF and SCL modules, based on development of GUI from SAS package for disposal of statistical data.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1241-1250
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• 2011

In this paper, we develop noninformative priors for the log-normal distributions when the parameter of interest is the common mean. We developed Jeffreys' prior, th reference priors and the first order matching priors. It turns out that the reference prior and Jeffreys' prior do not satisfy a first order matching criterion, and Jeffreys' pri the reference prior and the first order matching prior are different. Some simulation study is performed and a real example is given.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1E
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• pp.73-82
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• 1995

This paper presents a data structure that implements a mergeable double-ended priority queue ; namely, an improved relaxed min-max-pair heap. It suggests a sequential algorithm to merge priority queues organized in two relaxed min-max heaps : kheap and nheap of sizes k and n, respecrively. This new data sturuture eliminates the blossomed tree and the lazying method used to merge the relaxed min-max heaps in [8]. As a result, the suggested method in this paper requires the time complexity of O(log(log(n/k))*log(k)) and the space complexity of O(n+), assuming that $k{\leq}{\lfloor}log(size(nheap)){\rfloor}$ are in two heaps of different sizes.

• Journal of Computing Science and Engineering
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• v.3 no.1
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• pp.15-26
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• 2009

Given an n-length text T over a $\sigma$-size alphabet, we present a compressed representation of T which supports retrieving queries of rank/select/access and updating queries of insert/delete. For a measure of compression, we use the empirical entropy H(T), which defines a lower bound nH(T) bits for any algorithm to compress T of n log $\sigma$ bits. Our representation takes this entropy bound of T, i.e., nH(T) $\leq$ n log $\sigma$ bits, and an additional bits less than the text size, i.e., o(n log $\sigma$) + O(n) bits. In compressed space of nH(T) + o(n log $\sigma$) + O(n) bits, our representation supports O(log n) time queries for a log n-size alphabet and its extension provides O(($1+\frac{{\log}\;{\sigma}}{{\log}\;{\log}\;n}$) log n) time queries for a $\sigma$-size alphabet.

• Water for future
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• v.26 no.4
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• pp.73-83
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• 1993

This study focuses on the separation effect analysis of rainfall data for 2-parameter log-normal, 3-parameter log-normal, type-extreme value, 2-parameter gamma, 3-parameter gamma, log-Pearson type-III, and general extreme value distribution functions. Difference in the relationship between the mean and standard deviation of skewness for historical data and relations derived from 7 distribution functions are analyzed suing the Monte Carlo experiment. The results show that rainfall data has the separation effect for 6 distribution functions except 3-parameter gamma distribution function.

• Korean Management Science Review
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• v.19 no.1
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• pp.29-38
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• 2002

Due to the rapid increase In the Internet traffic volume, ISPs are faced with the definite need of the expansion of server capacity. In order to Provide prompt services for customers and still prevent excessive facility cost, it is critical to determine the optimum level of internet server capacity. The purpose of this Paper is to provide a simple but effective strategy on the expansion of servers capacity according to the increase in internet traffic. We model an internet server as an M/G/m/m queueing system and derive an efficient method to compute the loss probability which, In turn, Is used as a basis to determine proper server capacity. The Process of estimating the traffic parameter values at each server based on log data analysis is also given. All the procedures are numerically demonstrated through the process of analyzing actual log data collected from a game company.