• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.11-21
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• 2018

In this paper we propose a novel log template discovery algorithm which achieves high quality of discovered log templates through iterative log filtering technique. Log templates are the static string pattern of logs that are used to produce actual logs by inserting variable values during runtime. Identifying individual logs into their template category correctly enables us to conduct automated analysis using state-of-the-art machine learning techniques. Our technique looks at the group of logs column-wise and filters the logs that have the value of the highest proportion. We repeat this process per each column until we are left with highly homogeneous set of logs that most likely belong to the same log template category. Then, we determine which column is the static part and which is the variable part by vertically comparing all the logs in the group. This process repeats until we have discovered all the templates from given logs. Also, during this process we discover the custom patterns such as ID formats that are unique to the application. This information helps us quickly identify such strings in the logs as variable parts thereby further increasing the accuracy of the discovered log templates. Existing solutions suffer from log templates being too general or too specific because of the inability to detect custom patterns. Through extensive evaluations we have learned that our proposed method achieves 2 to 20 times better accuracy.

• Journal of KIISE:Databases
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• v.34 no.3
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• pp.179-192
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• 2007

This paper addresses an approach that recommends investment types for stock investors by discovering useful rules from past changing patterns of stock prices in databases. First, we define a new rule model for recommending stock investment types. For a frequent pattern of stock prices, if its subsequent stock prices are matched to a condition of an investor, the model recommends a corresponding investment type for this stock. The frequent pattern is regarded as a rule head, and the subsequent part a rule body. We observed that the conditions on rule bodies are quite different depending on dispositions of investors while rule heads are independent of characteristics of investors in most cases. With this observation, we propose a new method that discovers and stores only the rule heads rather than the whole rules in a rule discovery process. This allows investors to define various conditions on rule bodies flexibly, and also improves the performance of a rule discovery process by reducing the number of rules. For efficient discovery and matching of rules, we propose methods for discovering frequent patterns, constructing a frequent pattern base, and indexing them. We also suggest a method that finds the rules matched to a query issued by an investor from a frequent pattern base, and a method that recommends an investment type using the rules. Finally, we verify the superiority of our approach via various experiments using real-life stock data.

• Journal of Korean Academy of Nursing
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• v.36 no.4
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• pp.652-661
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• 2006

Purpose. The purposes of this study were to apply data mining tool to nursing specific knowledge discovery process and to identify the utilization of data mining skill for clinical decision making. Methods. Data mining based on rough set model was conducted on a large clinical data set containing NMDS elements. Randomized 1000 patient data were selected from year 1998 database which had at least one of the five most frequently used nursing diagnoses. Patient characteristics and care service characteristics including nursing diagnoses, interventions and outcomes were analyzed to derive the meaningful decision rules. Results. Number of comorbidity, marital status, nursing diagnosis related to risk for infection and nursing intervention related to infection protection, and discharge status were the predictors that could determine the length of stay. Four variables (age, impaired skin integrity, pain, and discharge status) were identified as valuable predictors for nursing outcome, relived pain. Five variables (age, pain, potential for infection, marital status, and primary disease) were identified as important predictors for mortality. Conclusions. This study demonstrated the utilization of data mining method through a large data set with stan dardized language format to identify the contribution of nursing care to patient's health.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.5
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• pp.442-450
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• 2014

Technology opportunity discovery (TOD) based on technological capability is a process which identifies new product and technology items that can be developed by utilizing or improving a firm's existing products or technologies. By taking into consideration the investment risk of R&D and its practicality, developing technological capability-based TOD methodology is considered to be important for both business and research. To this end, we propose a technological capability-based TOD method and its system using TOD knowledge base. The method can support four types of TOD cases, which are based on a firm's existing technologies and products, and TOD knowledge base is developed by using function information extracted from patent documents. In this paper, we introduce the overall framework of the method and provide application examples on the four TOD cases using the prototype system.

• Journal of Elementary Mathematics Education in Korea
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• v.15 no.3
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• pp.641-654
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• 2011

In order to grow students' rational and creative problem-solving ability which is one of the primary goals in mathematics education. students' proper understanding of mathematical concepts, principles, and rules must be backed up as its foundational basis. For the relevant teaching strategies. National Mathematics Curriculum advises that students should be allowed to discover and justify the concepts, principles, and rules by themselves not only through the concrete hands-on activities but also through inquiry-based activities based on the learning topics experienced from the diverse phenomena in their surroundings. Hereby, this paper, firstly, looks into both the meaning and the inductive reasoning process of mathematical principles and rules, secondly, suggest "learning through discovery teaching method" for the proper teaching of the mathematical principles and rules recommended by the National Curriculum, and, thirdly, examines the possible discovery-led teaching strategies using inductive methods with the related matters to be attended to.

• Interdisciplinary Bio Central
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• v.4 no.1
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• pp.2.1-2.7
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• 2012

Introduction: Histone deacetylases (HDAC) are a class of enzymes that remove acetyl groups from ${\varepsilon}$-N-acetyl lysine amino acids of histone proteins. Their action is opposite to that of histone acetyltransferase that adds acetyl groups to these lysines. Only few HDAC inhibitors are approved and used as anti-cancer therapeutics. Thus, discovery of new and potential HDAC inhibitors are necessary in the effective treatment of cancer. Materials and Methods: This study proposed a method using support vector machine (SVM) to classify HDAC8 inhibitors and non-inhibitors in early-phase virtual compound filtering and screening. The 100 experimentally known HDAC8 inhibitors including 52 inhibitors and 48 non-inhibitors were used in this study. A set of molecular descriptors was calculated for all compounds in the dataset using ADRIANA. Code of Molecular Networks. Different kernel functions available from SVM Tools of free support vector machine software and training and test sets of varying size were used in model generation and validation. Results and Conclusion: The best model obtained using kernel functions has shown 75% of accuracy on test set prediction. The other models have also displayed good prediction over the test set compounds. The results of this study can be used as simple and effective filters in the drug discovery process.

• Journal of Science Education
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• v.43 no.1
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• pp.157-172
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• 2019

The purpose of this study is to analyze the influence of number of targets on common knowledge generation and brain activity during the common life science discovery task performance. In this study, 35 preliminary life science teachers participated. This study was intentionally made a block designed for EEG recording. EEGs were collected while subjects were performing common discovery tasks. The sLORETA method and the relative power spectrum analysis method were used to analyze the brain activity difference and the role of activated cortical and subcortical regions according to the degree of difficulty of common discovery task. As a result of the study, in the case of the Theta wave, the activity of the Theta wave was significantly decreased in the frontal lobe and increased in the occipital lobe when the difficult difficulty task was compared with the easy difficulty task. In the case of Alpha wave, the activity of Alpha decreased significantly in the frontal lobe when performing difficult task with difficulty. Beta wave activity decreased significantly in the frontal lobe, parietal lobe, and occipital lobe when performing difficult task. Finally, in the case of Gamma wave, activity of Gamma wave decreased in the frontal lobe and activity increased in the parietal lobe and temporal lobe when performing the difficult difficulty task compared to the task of easy difficulty. The level of difficulty of the commonality discovery task is determined by the cingulate gyrus, the cuneus, the lingual gyrus, the posterior cingulate, the precuneus, and the sub-gyral where it was shown to have an impact. Therefore, the difficulty of the commonality discovery task is the process of integrating the visual information extracted from the image and the location information, comparing the attributes of the objects, selecting the necessary information, visual work memory process of the selected information. It can be said to affect the process of perception.

• Proceedings of the CALSEC Conference
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• 2004.02a
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• pp.112-116
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• 2004

· Dynamic Pricing vs. Fixed Pricing Auctions make both buyers and sellers engage in the price discovery process, Auctions of various kinds will replace the fixed pricing model that now pervades much of the web(pmitted)

• Proceedings of the Korean Society of Toxicology Conference
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• 2003.10b
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• pp.84-85
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• 2003

The process of drug development has seen major changes over the last two decades with the movement away from standard small molecule drug discovery programs, through computer-assisted drug design methodologies towards biotechnologically derived products. The aim of duplication of endogenously active materials to be administered exogenously has enormous impact on development practices and evaluation of safety.(omitted)

• Proceedings of the Korean Society of Applied Pharmacology
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• 2001.04a
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• pp.89-99
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• 2001

■ Cathepsin K is a attractive target for selectively and efficiently modulating the osteoclastic bone resorption. ■ OST-1857 is a lead compound which is specifically targeted to cathepsin K and showed efficacy in TPTX rats. ■ OST-compounds are in process of the preclinical study, joined by Yuhan research center.