• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.109-122
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• 2016

Recently, big data analysis has developed into a field of interest to individuals and non-experts as well as companies and professionals. Accordingly, it is utilized for marketing and social problem solving by analyzing the data currently opened or collected directly. In Korea, various companies and individuals are challenging big data analysis, but it is difficult from the initial stage of analysis due to limitation of big data disclosure and collection difficulties. Nowadays, the system improvement for big data activation and big data disclosure services are variously carried out in Korea and abroad, and services for opening public data such as domestic government 3.0 (data.go.kr) are mainly implemented. In addition to the efforts made by the government, services that share data held by corporations or individuals are running, but it is difficult to find useful data because of the lack of shared data. In addition, big data traffic problems can occur because it is necessary to download and examine the entire data in order to grasp the attributes and simple information about the shared data. Therefore, We need for a new system for big data processing and utilization. First, big data pre-analysis technology is needed as a way to solve big data sharing problem. Pre-analysis is a concept proposed in this paper in order to solve the problem of sharing big data, and it means to provide users with the results generated by pre-analyzing the data in advance. Through preliminary analysis, it is possible to improve the usability of big data by providing information that can grasp the properties and characteristics of big data when the data user searches for big data. In addition, by sharing the summary data or sample data generated through the pre-analysis, it is possible to solve the security problem that may occur when the original data is disclosed, thereby enabling the big data sharing between the data provider and the data user. Second, it is necessary to quickly generate appropriate preprocessing results according to the level of disclosure or network status of raw data and to provide the results to users through big data distribution processing using spark. Third, in order to solve the problem of big traffic, the system monitors the traffic of the network in real time. When preprocessing the data requested by the user, preprocessing to a size available in the current network and transmitting it to the user is required so that no big traffic occurs. In this paper, we present various data sizes according to the level of disclosure through pre - analysis. This method is expected to show a low traffic volume when compared with the conventional method of sharing only raw data in a large number of systems. In this paper, we describe how to solve problems that occur when big data is released and used, and to help facilitate sharing and analysis. The client-server model uses SPARK for fast analysis and processing of user requests. Server Agent and a Client Agent, each of which is deployed on the Server and Client side. The Server Agent is a necessary agent for the data provider and performs preliminary analysis of big data to generate Data Descriptor with information of Sample Data, Summary Data, and Raw Data. In addition, it performs fast and efficient big data preprocessing through big data distribution processing and continuously monitors network traffic. The Client Agent is an agent placed on the data user side. It can search the big data through the Data Descriptor which is the result of the pre-analysis and can quickly search the data. The desired data can be requested from the server to download the big data according to the level of disclosure. It separates the Server Agent and the client agent when the data provider publishes the data for data to be used by the user. In particular, we focus on the Big Data Sharing, Distributed Big Data Processing, Big Traffic problem, and construct the detailed module of the client - server model and present the design method of each module. The system designed on the basis of the proposed model, the user who acquires the data analyzes the data in the desired direction or preprocesses the new data. By analyzing the newly processed data through the server agent, the data user changes its role as the data provider. The data provider can also obtain useful statistical information from the Data Descriptor of the data it discloses and become a data user to perform new analysis using the sample data. In this way, raw data is processed and processed big data is utilized by the user, thereby forming a natural shared environment. The role of data provider and data user is not distinguished, and provides an ideal shared service that enables everyone to be a provider and a user. The client-server model solves the problem of sharing big data and provides a free sharing environment to securely big data disclosure and provides an ideal shared service to easily find big data.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.113-125
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• 2013

Every company wants to know customer's requirement and makes an effort to meet them. Cause that, communication between customer and company became core competition of business and that important is increasing continuously. There are several strategies to find customer's needs, but VOC (Voice of customer) is one of most powerful communication tools and VOC gathering by several channels as telephone, post, e-mail, website and so on is so meaningful. So, almost company is gathering VOC and operating VOC system. VOC is important not only to business organization but also public organization such as government, education institute, and medical center that should drive up public service quality and customer satisfaction. Accordingly, they make a VOC gathering and analyzing System and then use for making a new product and service, and upgrade. In recent years, innovations in internet and ICT have made diverse channels such as SNS, mobile, website and call-center to collect VOC data. Although a lot of VOC data is collected through diverse channel, the proper utilization is still difficult. It is because the VOC data is made of very emotional contents by voice or text of informal style and the volume of the VOC data are so big. These unstructured big data make a difficult to store and analyze for use by human. So that, the organization need to automatic collecting, storing, classifying and analyzing system for unstructured big VOC data. This study propose an intelligent VOC analyzing system based on opinion mining to classify the unstructured VOC data automatically and determine the polarity as well as the type of VOC. And then, the basis of the VOC opinion analyzing system, called domain-oriented sentiment dictionary is created and corresponding stages are presented in detail. The experiment is conducted with 4,300 VOC data collected from a medical website to measure the effectiveness of the proposed system and utilized them to develop the sensitive data dictionary by determining the special sentiment vocabulary and their polarity value in a medical domain. Through the experiment, it comes out that positive terms such as "칭찬, 친절함, 감사, 무사히, 잘해, 감동, 미소" have high positive opinion value, and negative terms such as "퉁명, 뭡니까, 말하더군요, 무시하는" have strong negative opinion. These terms are in general use and the experiment result seems to be a high probability of opinion polarity. Furthermore, the accuracy of proposed VOC classification model has been compared and the highest classification accuracy of 77.8% is conformed at threshold with -0.50 of opinion classification of VOC. Through the proposed intelligent VOC analyzing system, the real time opinion classification and response priority of VOC can be predicted. Ultimately the positive effectiveness is expected to catch the customer complains at early stage and deal with it quickly with the lower number of staff to operate the VOC system. It can be made available human resource and time of customer service part. Above all, this study is new try to automatic analyzing the unstructured VOC data using opinion mining, and shows that the system could be used as variable to classify the positive or negative polarity of VOC opinion. It is expected to suggest practical framework of the VOC analysis to diverse use and the model can be used as real VOC analyzing system if it is implemented as system. Despite experiment results and expectation, this study has several limits. First of all, the sample data is only collected from a hospital web-site. It means that the sentimental dictionary made by sample data can be lean too much towards on that hospital and web-site. Therefore, next research has to take several channels such as call-center and SNS, and other domain like government, financial company, and education institute.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Journal of the Korean Geographical Society
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• v.38 no.2
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• pp.312-323
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• 2003

This writer absolutely agrees with the government that regional disequilibrium is severe enough to consider moving the administrative capital. Pursuing this course solely to establish a balanced development, however, is not a convincing enough reason. The capital city is directly related to not only the social and economic situation but, much more importantly, to the domestic political situation as well. In the mid-1970s, the proposal by the Third Republic to move the capital city temporarily was based completely on security reasons. At e time, the then opposition leader Kim, Dae-jung said that establishing a safe distance from the demilitarized zone(DMZ) reflected a typically military decision. His view was that retaining the capital city close to the DMZ would show more consideration for the will of the people to defend their own country. In fact, independent Pakistan moved its capital city from Karachi to Islamabad, situated dose to Kashmir the subject of hot territorial dispute with India. It is regrettable that no consideration has been given to the urgent political situation in the Korean peninsula, which is presently enveloped in a dense nuclear fog. As a person requires health to pursue his/her dream, a country must have security to implement a balanced territorial development. According to current urban theories, the fate of a country depends on its major cities. A negligently guarded capital city runs the risk of becoming hostage and bringing ruin to the whole country. In this vein, North Koreas undoubted main target of attack in the armed communist reunification of Korea is Seoul. For the preservation of our state, therefore, it is only right that Seoul must be shielded to prevent becoming hostage to North Korea. The location of the US Armed Forces to the north of the capital city is based on the judgment that defense of Seoul is of absolute importance. At the same time, regardless of their different standpoints, South and North Korea agree that division of the Korean people into two separate countries is abnormal. Reunification, which so far has defied all predictions, may be realized earlier than anyone expects. The day of reunification seems to be the best day for the relocation of the capital city. Building a proper capital city would take at least twenty years, and a capital city cannot be dragged from one place to another. On the day of a free and democratic reunification, a national agreement will be reached naturally to find a nationally symbolic city as in Brazil or Australia. Even if security does not pose a problem, the governments way of thinking would not greatly contribute to the balanced development of the country. The Chungcheon region, which is earmarked as the new location of the capital city, has been the greatest beneficiary of its proximity to the capital region. Not being a disadvantaged region, locating the capital city there would not help alleviate regional disparity. If it is absolutely necessary to find a candidate region at present, considering security, balanced regional development and post-reunification scenario of the future, Cheolwon area located in the middle of the Korean peninsula may be a plausible choice. Even if the transfer of capital is delayed in consideration of the present political conflict between the South and the North Koreas, there is a definite shortcut to realizing a balanced regional development. It can be found not in the geographical dispersal of the central government, but in the decentralization of power to the provinces. If the government has surplus money to build a new symbolic capital city, it is only right that it should improve, for instance, the quality of drinking water which now everyone eschews, and to help the regional subway authority whose chronic deficit state resoled in a recent disastrous accident. And it is proper to time the transfer of capital city to coincide with that of the reunification of Korea whenever Providence intends.

• Childhood Kidney Diseases
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• v.7 no.2
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• pp.142-149
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• 2003

Purpose : The urinary mass screening program for the detection of urinary abnormalities in school aged population has been performed in Seoul since 1981. Nation-wide urinary mass screening program was also performed since 1998. The aim of this study was to analyze the cause and nature of isolated proteinuria detected by chance on the urinary mass screening test in Busan and Kyungsangnam-do Province Methods : The medical records of 44 cases of isolated proteinuria detected by chance on the urinary mass screening test in Busan and Kyungsangnam-do Province, and evaluated for urinary abnormalities at the pediatrics outpatients renal clinics of Busan Paik Hospital from April 2002 to August 2003 were reviewed prospectively. Results : The cause and incidence of isolated proteinuria were as follows; transient proteinuria 4 cases(9.1%), orthostatic proteinuria 36 cases(81.8%) and persistent proteinuria 4 cases (9.1%). The total protein amount of the 24 hour urine were $121.0{\pm}136.4\;mg$ in transient proteinuria, $179.1{\pm}130.0\;mg$ in orthostatic proteinuria and $1532.8{\pm}982.5\;mg$ in persistent proteinuria. In the orthostatic proteinuria group, the total protein amount of the 24 hour urine was in the range of 40-616 mg. Spot urine protein/creatinine ratio(PCR) were $0.10{\pm}0.01$ in transient proteinuria, $0.61{\pm}0.61$ in orthostatic proteinuria and $4.35{\pm}4.04$ in persistent proteinuria. In the orthostatic proteinuria group, spot me PCR was in the range of 0.09-2.32. Renal biopsy was peformed in 4 children of the persisitent proteinuria group. They showed minimal change in 1 case, membranoproliferatiye glomerulonephritis in 2 cases and secondary renal amyloidosis in 1 case. Conclusion : The majority of isolated proteinuria which was detected by chance on school urinary mass screening were transient or orthostatic proteinuria. Even though the incidence of persistent proteinuria was much lower, it is necessary to take care of these children regularly and continuously, because persistent proteinuria itself is a useful marker of the progressive renal problems.

• Journal of the Korean Society of Food Science and Nutrition
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• v.36 no.9
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• pp.1172-1181
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• 2007

This research involved 226 Jeonbuk Province school dietitians as subjects to investigate intake and perceptions of the healthy functional foods. Sixty nine percent of the school dietitians didn't even know about the law enforcement concerning the health functional foods. Although 68.1% of the respondents said that they slightly knew about health functional foods, only 25% knew exactly what it was. As shown in the survey, most didn't have the cognitive understanding did not understand which should be obtained by education. Sixty two percent of the answerers said they had experience of taking health various functional food products of various kinds such as supplements (57.9%), red ginseng products (52.9%), and chlorella products (30.0%). The motive of intake was in the order of fatigue restoration (25.7%), sickness prevention (22.9%), and nutrient replenishment (22.9%). A fascinating fact from this study was that the reason for healthy functional product intake was different between groups that was primarily interested in the products and those that was not. For those who had interest, the reason for intake was for sickness prevention. On the other hand, for those who didn't have any interest, the reasons was primarily for fatigue restoration and they were mostly persuaded by close friends and relatives. Main concerns were in the order of side effects (4.72), efficacy after intake (4.59), cleanliness (4.51), reliability of the company (4.29), and price (4.23). In view of the study, it is clear that a lot of people are showing interest in healthy functional food products. However, dietitians who are experts in food and nutrition lacked knowledge and information on healthy functional food.

• The Journal of Engineering Geology
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• v.29 no.1
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• pp.51-68
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• 2019

This study performs to evaluate adaptability of seashore filtering type seawater-intake which adapts dua1 filter well alternative for direct seawater-intake. This study varies filter condition of seashore free surface aquifer which is composed of sand layer then installs real size dual filter well and single filter well to evaluate water permeability and proper pumping amount according to filter condition. According to result of step aquifer test, it is analysed that 110.3% synergy effect of water permeability coefficient is happened compare to single filter since dual filter well has better improvement. dual filter has higher water permeability coefficient compare to same pumping amount, this means dual filter has more improved water permeability than single filter. According to analysis result of continuous aquifer test, it is evaluated that dual filter well (SD1200) has higher water permeability than single filter well (SS800) by analysis of water permeability coefficient using monitoring well and gauging well, it is also analysed dual filter has 110.7% synergy effect of water permeability coefficient. As a evaluation result of pumping amount according to analysis of water level dropping rate, it is analysed that dual filter well increased 122.8% pumping amount compare to single filter well when water level dropping is 2.0 m. As a result of calculating proper pumping amount using water level dropping rate, it is analysed that dual filter well shows 136.0% higher pumping amount compare to single filter well. It is evaluated that proper pumping amount has 122.8~160% improvement compare to single filter, pumping amount improvement rate is 139.6% compare to averaged single filter. In other words, about 40% water intake efficiency can be improved by just installation of dual filter compare to normal well. Proper pumping amount of dual filter well using inflection point is 2843.3 L/min and it is evaluated that daily seawater intake amount is about $4,100m^3/day$ (${\fallingdotseq}4094.3m^3/day$) in one hole of dual filter well. Since it is possible to intake plenty of water in one hole, higher adaptability is anticipated. In case of intaking seawater using dual filter well, no worries regarding damages on facilities caused by natural disaster such as severe weather or typhoon, improvement of pollution is anticipated due to seashore sand layer acts like filter. Therefore, It can be alternative of environmental issue for existing seawater intake technique, can save maintenance expenses related to installation fee or damages and has excellent adaptability in economic aspect. The result of this study will be utilized as a basic data of site demonstration test for adaptation of riverside filtered water of upcoming dual filter well and this study is also anticipated to present standard of well design and construction related to riverside filter and seashore filter technique.

• (The)Study of the Eastern Classic
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• no.50
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• pp.55-88
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• 2013

Gwonwu Hong Chan-yu is one of the modern and contemporary Korean scholars of Sino-Korean literature and one of the literati of his era, so is respected as a guiding light by academic descendants. Gwonwu was a teacher of his era, who experienced all the turbulence of Korean society, such as the Japanese occupation by force, the Korean War, the military dictatorship, and the struggle for democracy, and who educated and led young scholars of his time. However, academia has not payed attention to his life and achievements since his death. This paper is to examine the poetry of Gwonwu Hong Chan-yu, one of the representative modern and contemporary scholar of Sini-Korean literature, which has not yet been discussed by academia. The minimal meaning of this paper is that it is a first work based on his anthology, which has not been discussed by academia, and a first full-scale study on Gwonwu Hongchan-yu. For the reason, this paper aims at the detailed inspection of his poetic pieces recorded in his anthology. Nonetheless, despite such intentions, some limits cannot be avoided here and there in this paper for the insufficient knowledge and academic capability of this paper's writer and for the lack of academic sources. Gwonwu's poetry examined through his anthology shows the characteristic which is that his poems focus on exposing his own internal emotions. Such a characteristic says that his idea of poetic literature payed attention more to individuality, that is exposition of private emotions, than to social utility of poems. Gwonwu's such an idea of poetic literature can be generally affirmed throughout his poetry. Accordingly, Gwonwu preferred classical Chinese poems to archaistic poems, and single poems to serial poems; and avoided writing poems within social relations such as farewell-poems, bestowal-poems, and mourning-poems. When the characteristics of Gwonwu's poetic literature get summarized as such, however, some questions remain. The preferential question is whether the poems in his anthology are the whole poetry of him. Although Gwonwu's poetic pieces that the writer of this paper have checked out till now are all in his anthology, it is very much questionable whether Gwonwu's poetry can be summed up only with these poems. The next question is what is the writing method for taking joy(spice), sentiment, and full-heart into his poems if Gwonwu's poems focus on exposing his internal emotions, and if poems exposing joy and poems exposing sentiment and full-heart appear coherently in various different spaces and circumstances of writing. The final question is what are the meanings of Gwonwu's poems if his poetry checked out through his anthology directly shows either the reality carried in his poems or the reality of a time in his life. The questions listed above are thought to be resolved by the synchronizing process of stereoscopic searches both for Gwonwu as an individual and for the era of his life. Especially, spurring deeper researches toward a new direction regarding Gwonwu's poetry has an important meaning for construction of a complete modern and contemporary history of Sino-Korean literature and for procurement of continuous research on Sino-Korean literature and its history. For the reason, it is thought that more efforts of researchers are required.