• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.12
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• pp.2049-2067
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• 2013

The purpose of this study is to present a more effective nutrition education activation plan. As a result of investigating the dietary education operating situation, 58.9% underwent direct education, and 89.5% underwent food life education through traditional food culture succeeding business operation. The results from investigating the recognition regarding dietary education are as follows. The activation level by education types was as low as 2.24 points, the necessity was as high as 4.54 points, the difficult point in performing food life education was 'overwork' with 4.43 points, and the teaching activity ability level was 'can effectively prepare a teaching guidance plan' at 2.96 points. As a result of investigating the nutrition consultation operating situations, 62.8% underwent it and all of the students as well as some parents and teachers performed it. The consumed time per consultation for effective nutrition consultation was 10~20 minutes, the required education equipment and data were 'consultation program' with 40.3%, and the important content during consultation was 'contents related to eating habits' with 70.5%, which was recognized as the most important.

• Journal of Chest Surgery
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• v.41 no.5
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• pp.610-618
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• 2008

Background: There are various treatment modalities for atherosclerotic arterial obstructive disease at the lower limbs, for example, conservative physical therapy, medication, operation etc. Yet it has been established that an arterial bypass operation is the most effective treatment. The aim of this study is to evaluate the effect of arterial bypass operation within our experience and to determine the indicators of treatment. Material and Method: Ninety six patients received arterial bypass operation for atherosclerotic arterial obstructive disease from June 2002 to April 2006. We evaluated the feasibility of arterial bypass operation based on the improvement of symptoms and the ankle-brachial index (ABI) and the surgical outcomes, as based on the complications, the amputation rates and the patency rates. We also assessed the possible risk factors such as gender, age, a smoking history, co-morbidities, the anastomotic sites, the graft size and the graft type. We retrospectively reviewed the medical records of the patients. The total mean follow-up period was $29.4{\pm}13.1$ months. Result: The mean age was $65.95{\pm}9.61$ and there were 88 male patients. The most common clinical manifestation was ischemic resting pain in the lower extremities. The underlying combined diseases were hypertension (61%), diabetes (43%), cardiac problems (35%) and smoking (91.7%). The most frequent site of arterial obstruction was the superficial femoral artery (44 cases, 40%). A femoropopliteal artery bypass operation with a Polytetrafluoroethylene(PTFE) synthetic graft was done in 44 cases (40%) and the great saphenous vein graft was used in 11 cases. The postoperative ABI increased significantly from $0.30{\pm}0.11$ preoperatively to $0.63{\pm}0.11$ (p<0.001) postoperatively. In 8 cases, amputations above the ankle level were necessary. The graft patency rates were 86.4% and 68.0% after 1 and 3 years, respectively. There were 29 cases (30.21%) of patency failure; the male gender, smokers and hypertension were significantly more frequent in the failure group. Of these, hypertension was the most powerful risk factor (p=0.042). Conclusion: The arterial bypass operation is an effective treatment modality for controlling the symptoms such as pain and claudication, and for preventing major amputations for the patients with atherosclerotic arterial obstructive disease. This study suggests quitting smoking, strict blood pressure control, selection of an appropriate graft, regular outpatient follow up and proper medication would offer higher patency rates and more favorable outcomes.

• Journal of Nutrition and Health
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• v.46 no.4
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• pp.346-356
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• 2013

This study investigated the daily physical activity level, energy expenditure, energy balance, and body composition and their relationship with university students. The participants were 130 male students ($19.5{\pm}0.5$ yrs) and 139 female students ($19.5{\pm}0.3$ yrs) at a university in Chungnam province. Physical activity level was evaluated by an equation based on 24 hr-activity record and dietary nutrient intake was evaluated using the food record method during a three-day period consisting of two week days and one weekend. Body composition was measured using Inbody 430 (Biospace Co., Cheonan, Korea). As a result, mean body mass index (BMI) of subjects indicated that they had normal weight, however mean body fat ratio was $19.1{\pm}5.4%$ for males and $28.4{\pm}5.0%$ for females, indicating that they had higher than normal weight. Daily mean physical activity level was 1.55 for males and 1.47 for females, which was regarded as 'low active', respectively. Females had more light activity than males (p<0.01). Daily mean energy expenditure was $2,803.5{\pm}788.9$ kcal/d for males and $1,915.4{\pm}510.2$ kcal/d for females (p<0.001). Daily mean dietary energy intake was $2,327.0{\pm}562.5$ kcal/d for males and $1,802.1{\pm}523.6/d$ for females (p<0.001), and daily mean energy balance was $-476.5{\pm}955.9$ kcal/d for males and $-113.3{\pm}728.1$ kcal/d for females (p<0.01). Daily mean dietary intake of protein, vitamins, and minerals, except Ca, satisfied recommended nutrient intake. Daily energy expenditure was positively related to body weight (p<0.01), BMI (p<0.01), and fat free mass ratio (p<0.05), but was negatively related to body fat ratio (p<0.01). In conclusion, subjects had a negative energy balance and low physical activity. They had a normal weight by BMI but had a more fat than normal weight by body fat ratio. This appears to be related to their low physical activity. Thus, nutrition education should be provided for university students in order to increase their physical activity for maintenance of normal weight by body composition and health promotion.

• Journal of Hospice and Palliative Care
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• v.4 no.1
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• pp.14-25
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• 2001

Purpose : To determine whether there exist gender differences in pain in Korean cancer patients and whether the depression and performance that are often expressed differently between men and women with cancer interact with pain. Method : The results of survey were collected from 140 in- and out-patients (78 male and 62 female) who had cancer treatment at one of the university hospital in Seoul for four months from February of 1999. The severity and interference of pain were examined with the self-reported survey based on Korean version of Brief Pain Inventory (BPI-K). Demographic and clinical information for all patient were compiled by reviewing their medical records, and the level of depression was examined with the Korean version of Beck Depression Inventory (BDI-K). Usual statistical methods, e.g., frequences, means and SDs were used to characterize the sample. The chi-square tests for categorical data and t-test for numerical data were used for group comparison. And the correlation between variables were performed using Pearson correlation coefficient. Resuts : 1) The mean scores of the worst pain for last 24-hours measured with the pain severity of BPI-K were 5.77 in male and 6.45 in female. The pain interference of BPI-K in men was in the order of mood (5.49), enjoy (5.36), and work (5.00), and in women were work (7.48), enjoy (7.16), and mood (6.53). 2) In pain severity, significant difference was found between men and women in the average pain for last 24-hours (t=-2.130, P=.035). In pain interference, significant difference was found between men and women in activity (t=-2.450, P=.015), mood (t=-2,321, P=.022), walk (t=-2.762, P=.007), work (t=-4.946, P=.000), relate (t=-2.595, P=.010), sleep (t=-2.071, P=.040), enjoy (t=-3.198, P=.001). 3) It was found that the items of pain and depression are significantly correlated in men but not in women. Men also exhibited higher correlation in the items of pain and performance status than women. Conclusions : Women report significantly greater average pain for last 24-hours and for all items of pain interference than men. Pain and depression are significantly correlated in men. The results of this study suggest that gender differences in pain should be considered for planning effective pain management program.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.