• Health Policy and Management
• /
• v.34 no.2
• /
• pp.120-128
• /
• 2024

From February to now 2024, there continues to be controversy over the expansion of admission number to medical school. Some of the controversy arises from a mix of present and future time points. In the present time point, the controversy over whether physicians are some shortages or not has various aspects. Some aspects are presented as evidence of the physician shortage and others as non-shortage. Also, the presenting evidence of shortage is being disputed, and so is the evidence of the contrary. This controversy over whether there is a shortage or not in the present time point makes it difficult to reach a consensus. In 10 years, the shortage of doctors will increase due to the rapid increase in the elderly population, so the admission number of medical schools will need to be increased. However, the increase must be such that there is minimal deterioration in the quality of medical education. More admission numbers should be allocated to medical schools with a high quality of medical education. This study suggests that large-scale medical schools increase the admission number by 20%-30%, and small-scale medical schools increase the admission number by 40%-50%, if so, the total increasing number is 760 to 1,066. If the 2,000-person increase is enforced, the quality of medical education must be carefully evaluated and the results should be reflected in adjusting the admission number of medical schools. In 20 years later, the admission number of medical schools will have to be reduced. This is because the physician supply is changing to a linear function and the physician demand (medical care demand) is changing to a quadratic function. Even if the current number is maintained, there will be an excess of doctors from 2048, so the medical school admission number must be reduced and its size will be reduced to about 2,000, a 30% reduction from the current number. Because the same reduction rate for all medical schools will result in many small-scale medical schools, the M&A (mergers and acquisitions) strategy should be considered with 40 medical schools and 12 Korean medical schools. In Korea, the main contributor to estimating physician demand is the change in population structure. Due to the rapid decrease in the total fertility rate, future population projections are uncertain. The recent rapid increase in healthcare utilization should be reexamined in the forecasting of physician demand. Since the various factors that affect the estimate of doctor supply and demand are unclear, the estimate of physician supply and demand must be continuously conducted every five years, and the Health Care Workforce Committee must be established and operated. The effects of increasing the admission number of medical schools should be evaluated and adjusted annually.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.15 no.1
• /
• pp.285-301
• /
• 2020

Looking at the problem of aging in the nation from a demographic perspective, it is not a problem of the overall population, but of the structure of the population. It is the baby boomer and post-baby boomers, the largest population in the country. Baby boomers were born between 1955 and 1963, and currently have a population of 7001,333, which is 13.6 percent (as of 2015). The Post-Baby Boomer generation was born between 1964 and 1974, with a total population of 9,567,171, accounting for 18.8 percent of the total population. In particular, baby boomers and post-baby boomers (32.4% of the total population) have begun to retire or will retire soon. The average life expectancy continues to increase due to the development of medical technology, and the falling birth rate of newborns and the declining population of the production population are darkening the domestic economy. In a policy proposal aimed at easing the nation's falling economic growth rate, women's participation rate is as high as Sweden and men's efforts to increase it as high as Japan's, while the elderly rate is desirable to maintain Korea's high level. This is because the expansion of the elderly generation's participation in economic activities could ease a sharp drop in economic growth and reduce the burden of supporting the elderly population. The study, based on this social problem awareness and problem solving plan, looks at the relationship between career orientation and satisfaction in start-up education based on the diverse career base of active seniors, and also suggests the importance of customized start-up education on the diversity of active seniors by clarifying the relationship between them, and suggests the desirable direction of senior start-up policy design, funding, and start-up education. Based on the theoretical background, the concept of five factors was defined: active senior, career-oriented, satisfaction level of start-up education, willingness to start a business, and the concept definition of an active senior, which is particularly key to the baby boomers in their 50s and 60s, is generally regarded as a source of consumption or welfare benefits, but in this study, the concept of active start-up is reflected in the domestic start-up market by young people in their 40s, 50s and 60s. As a result of a hypothesis test. Hypothesis 1 and Hypothesis 5: Career orientation has been verified to affect the willingness to start a business and the behavior of preparation for a start-up. Hypothesis 3: The willingness to start a business has been verified as having an effect between startup preparation actions. Hypothesis 4: The satisfaction level of start-up education has been verified to affect start-up preparation behavior. However, hypothesis 2: The satisfaction level of education for start-ups does not affect the willingness to start a business. Such results can be inferred that satisfaction in start-up education does not have a direct effect on the will to start a business and increases the will to start a business through the influence of personal career orientation.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.127-148
• /
• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Korean Security Journal
• /
• no.48
• /
• pp.9-33
• /
• 2016

The purpose of the present study was to find out cause-and-effect relationship between job requirements and job resources, with job burnout as a mediator variable, and the effects of these variables on organizational effectiveness. The population in the present study was private security guards employed by 13 private security companies in Seoul and Gyeonggi-do areas, and a survey was conducted on 500 security guards selected using purposive sampling technique. Out of 460 questionnaires distributed, 429 responses, excluding 31 outliers or insincere responses, were used for data analysis. For analysis, data were coded and entered into SPSS 18.0 and AMOS 18.0, which were used to analyze the data. Descriptive analyses were performed to find out sociodemographic characteristics of the respondents. The exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to test the validity of the measurement tool, and the Cronbach's Alpha coefficients were calculated to test the reliability. To find out the significance of relationships among variables, Pearson's correlation analysis was performed. Covariance Structure Analysis (CSA) was performed to test the relationship among latent factors of a model for job requirements, job resources, job burnout, and organizational effectiveness of the private security guards, and the fitness of the model analyzed with CSA was determined by the goodness-of-fit index ($x^2$, df, p, RMR, GFI, CFI, TLI, RMSEA). The level of significance was set at .05, and the following results were obtained. First, even though the effect of job requirements on job burnout was not statistically significant, it had a positive influence overall, and this result can be considered such that the higher the perception of job requirements by the member of the organization, the higher the perception of job burnout. Second, the influence of job resources on job burnout was negative, which can be considered that the higher the perception of job resources, the lower the perception of job burnout. Third, even though the influence of job requirements on organizational effectiveness was statistically nonsignificant, it had a negative influence overall, and this result can be considered that the higher the perception of job requirements, the lower the perception of organizational effectiveness. Fourth, job resources had a positive influence on organizational effectiveness, and it can be considered that the higher the perception of job resources, the higher the perception of organizational effectiveness. Fifth, the results of the analysis between job burnout and organizational effectiveness revealed that, even though the influence of job burnout on organizational effectiveness was statistically nonsignificant, it had partial negative influences on sublevels of organizational effectiveness, and this may suggest that the higher the perception of job burnout by the organization members, the lower the organizational effectiveness. Sixth, the analysis of mediating role in the relationship between job requirements and organizational effectiveness, job burnout was taking partial mediating role between job requirements and organizational effectiveness. These results suggest that reducing job burnout by managing job requirements, organizational effectiveness that leads to job satisfaction, organizational commitment, and turnover intention can be maximized. Seventh, the analysis of mediating role in the relationship among job requirements, job resources, and organizational effectiveness, job burnout was assuming a partial mediating role in the relationships among job requirements, job resources, and organizational effectiveness. These results suggest that organizational effectiveness can be maximized by either lowering job requirements or burnout management through reorganizing job resources.

• Journal of Global Scholars of Marketing Science
• /
• v.19 no.1
• /
• pp.37-46
• /
• 2009

The Census Bureau of the Department of Commerce announced in May 2008 that U.S. retail e-commerce sales for 2006 reached $ 107 billion, up from $ 87 billion in 2005 - an increase of 22 percent. From 2001 to 2006, retail e-sales increased at an average annual growth rate of 25.4 percent. The explosive growth of E-Commerce has caused profound changes in marketing channel relationships and structures in many industries. Despite the great potential implications for both academicians and practitioners, there still exists a great deal of uncertainty about the impact of the Internet channel introduction on distribution channel management. The purpose of this study is to investigate how the ownership of the new Internet channel affects the existing channel members and consumers. To explore the above research questions, this study conducts well-controlled mathematical experiments to isolate the impact of the Internet channel by comparing before and after the Internet channel entry. The model consists of a monopolist manufacturer selling its product through a channel system including one independent physical store before the entry of an Internet store. The addition of the Internet store to this channel system results in a mixed channel comprised of two different types of channels. The new Internet store can be launched by the independent physical store such as Bestbuy. In this case, the physical retailer coordinates the two types of stores to maximize the joint profits from the two stores. The Internet store also can be introduced by an independent Internet retailer such as Amazon. In this case, a retail level competition occurs between the two types of stores. Although the manufacturer sells only one product, consumers view each product-outlet pair as a unique offering. Thus, the introduction of the Internet channel provides two product offerings for consumers. The channel structures analyzed in this study are illustrated in Fig.1. It is assumed that the manufacturer plays as a Stackelberg leader maximizing its own profits with the foresight of the independent retailer's optimal responses as typically assumed in previous analytical channel studies. As a Stackelberg follower, the independent physical retailer or independent Internet retailer maximizes its own profits, conditional on the manufacturer's wholesale price. The price competition between two the independent retailers is assumed to be a Bertrand Nash game. For simplicity, the marginal cost is set at zero, as typically assumed in this type of study. In order to explore the research questions above, this study develops a game theoretic model that possesses the following three key characteristics. First, the model explicitly captures the fact that an Internet channel and a physical store exist in two independent dimensions (one in physical space and the other in cyber space). This enables this model to demonstrate that the effect of adding an Internet store is different from that of adding another physical store. Second, the model reflects the fact that consumers are heterogeneous in their preferences for using a physical store and for using an Internet channel. Third, the model captures the vertical strategic interactions between an upstream manufacturer and a downstream retailer, making it possible to analyze the channel structure issues discussed in this paper. Although numerous previous models capture this vertical dimension of marketing channels, none simultaneously incorporates the three characteristics reflected in this model. The analysis results are summarized in Table 1. When the new Internet channel is introduced by the existing physical retailer and the retailer coordinates both types of stores to maximize the joint profits from the both stores, retail prices increase due to a combination of the coordination of the retail prices and the wider market coverage. The quantity sold does not significantly increase despite the wider market coverage, because the excessively high retail prices alleviate the market coverage effect to a degree. Interestingly, the coordinated total retail profits are lower than the combined retail profits of two competing independent retailers. This implies that when a physical retailer opens an Internet channel, the retailers could be better off managing the two channels separately rather than coordinating them, unless they have the foresight of the manufacturer's pricing behavior. It is also found that the introduction of an Internet channel affects the power balance of the channel. The retail competition is strong when an independent Internet store joins a channel with an independent physical retailer. This implies that each retailer in this structure has weak channel power. Due to intense retail competition, the manufacturer uses its channel power to increase its wholesale price to extract more profits from the total channel profit. However, the retailers cannot increase retail prices accordingly because of the intense retail level competition, leading to lower channel power. In this case, consumer welfare increases due to the wider market coverage and lower retail prices caused by the retail competition. The model employed for this study is not designed to capture all the characteristics of the Internet channel. The theoretical model in this study can also be applied for any stores that are not geographically constrained such as TV home shopping or catalog sales via mail. The reasons the model in this study is names as "Internet" are as follows: first, the most representative example of the stores that are not geographically constrained is the Internet. Second, catalog sales usually determine the target markets using the pre-specified mailing lists. In this aspect, the model used in this study is closer to the Internet than catalog sales. However, it would be a desirable future research direction to mathematically and theoretically distinguish the core differences among the stores that are not geographically constrained. The model is simplified by a set of assumptions to obtain mathematical traceability. First, this study assumes the price is the only strategic tool for competition. In the real world, however, various marketing variables can be used for competition. Therefore, a more realistic model can be designed if a model incorporates other various marketing variables such as service levels or operation costs. Second, this study assumes the market with one monopoly manufacturer. Therefore, the results from this study should be carefully interpreted considering this limitation. Future research could extend this limitation by introducing manufacturer level competition. Finally, some of the results are drawn from the assumption that the monopoly manufacturer is the Stackelberg leader. Although this is a standard assumption among game theoretic studies of this kind, we could gain deeper understanding and generalize our findings beyond this assumption if the model is analyzed by different game rules.

• Journal of Intelligence and Information Systems
• /
• v.25 no.4
• /
• pp.141-154
• /
• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.1-25
• /
• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of Intelligence and Information Systems
• /
• v.24 no.4
• /
• pp.171-196
• /
• 2018

With the development of the web, two-way communication and evaluation became possible and marketing paradigms shifted. In order to meet the needs of consumers, web design trends are continuously responding to consumer feedback. As the web becomes more and more important, both academics and businesses are studying consumer emotions and satisfaction on the web. However, some consumer characteristics are not well considered. Demographic characteristics such as age and sex have been studied extensively, but few studies consider psychological characteristics such as regulatory focus (i.e., emotional regulation). In this study, we analyze the effect of web style on consumer emotion. Many studies analyze the relationship between the web and regulatory focus, but most concentrate on the purpose of web use, particularly motivation and information search, rather than on web style and design. The web communicates with users through visual elements. Because the human brain is influenced by all five senses, both design factors and emotional responses are important in the web environment. Therefore, in this study, we examine the relationship between consumer emotion and satisfaction and web style and design. Previous studies have considered the effects of web layout, structure, and color on emotions. In this study, however, we excluded these web components, in contrast to earlier studies, and analyzed the relationship between consumer satisfaction and emotional indexes of web-style only. To perform this analysis, we collected consumer surveys presenting 40 web style themes to 204 consumers. Each consumer evaluated four themes. The emotional adjectives evaluated by consumers were composed of 18 contrast pairs, and the upper emotional indexes were extracted through factor analysis. The emotional indexes were 'softness,' 'modernity,' 'clearness,' and 'jam.' Hypotheses were established based on the assumption that emotional indexes have different effects on consumer satisfaction. After the analysis, hypotheses 1, 2, and 3 were accepted and hypothesis 4 was rejected. While hypothesis 4 was rejected, its effect on consumer satisfaction was negative, not positive. This means that emotional indexes such as 'softness,' 'modernity,' and 'clearness' have a positive effect on consumer satisfaction. In other words, consumers prefer emotions that are soft, emotional, natural, rounded, dynamic, modern, elaborate, unique, bright, pure, and clear. 'Jam' has a negative effect on consumer satisfaction. It means, consumer prefer the emotion which is empty, plain, and simple. Regulatory focus shows differences in motivation and propensity in various domains. It is important to consider organizational behavior and decision making according to the regulatory focus tendency, and it affects not only political, cultural, ethical judgments and behavior but also broad psychological problems. Regulatory focus also differs from emotional response. Promotion focus responds more strongly to positive emotional responses. On the other hand, prevention focus has a strong response to negative emotions. Web style is a type of service, and consumer satisfaction is affected not only by cognitive evaluation but also by emotion. This emotional response depends on whether the consumer will benefit or harm himself. Therefore, it is necessary to confirm the difference of the consumer's emotional response according to the regulatory focus which is one of the characteristics and viewpoint of the consumers about the web style. After MMR analysis result, hypothesis 5.3 was accepted, and hypothesis 5.4 was rejected. But hypothesis 5.4 supported in the opposite direction to the hypothesis. After validation, we confirmed the mechanism of emotional response according to the tendency of regulatory focus. Using the results, we developed the structure of web-style recommendation system and recommend methods through regulatory focus. We classified the regulatory focus group in to three categories that promotion, grey, prevention. Then, we suggest web-style recommend method along the group. If we further develop this study, we expect that the existing regulatory focus theory can be extended not only to the motivational part but also to the emotional behavioral response according to the regulatory focus tendency. Moreover, we believe that it is possible to recommend web-style according to regulatory focus and emotional desire which consumers most prefer.

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

• Korean small business review
• /
• v.33 no.4
• /
• pp.77-93
• /
• 2011

Since 2000, the employment rate of small and medium enterprises (SMEs) has dwindled while the creation of new jobs and the emergence of healthy SMEs have been stagnant. The fundamental reason for these symptoms is that the economic structure is disadvantageous to SMEs. In particular, the greater gap between SMEs and large enterprises has resulted in polarization, and the resulting imbalance has become the largest obstacle to improving SMEs' competitiveness. For example, the total productivity has continued to drop, and the average productivity of SMEs is now merely 30% of that of large enterprises, and the average wage of SMEs' employees is only 53% of that of large enterprises. Along with polarization, rapid industrialization has also caused anti-enterprise consensus, the collapse of the middle class, hostility towards establishments, and other aftereffects. The general consensus is that unless these problems are solved, South Korea will not become an advanced country. Especially, South Korea is now facing issues that need urgent measures, such as the decline of its economic growth, the worsening distribution of profits, and the increased external volatility. Recognizing such negative trends, the MB administration proposed a win-win growth policy and recently introduced a new national value called "ecosystemic development." As the terms in such policy agenda are similar, however, the conceptual differences among such terms must first be fully understood. Therefore, in this study, the concepts of win-win growth policy and ecosystemic development, and the need for them, were surveyed, and their differences from and similarities with other policy concepts like win-win cooperation and symbiotic development were examined. Based on the results of the survey and examination, the study introduced a South Korean model of win-win growth, targeting the promotion of a sound balance between large enterprises and SMEs and an innovative ecosystem, and finally, proposing future policy tasks. Win-win growth is not an academic term but a policy term. Thus, it is less advisable to give a theoretical definition of it than to understand its concept based on its objective and method as a policy. The core of the MB administration's win-win growth policy is the creation of a partnership between key economic subjects such as large enterprises and SMEs based on each subject's differentiated capacity, and such economic subjects' joint promotion of growth opportunities. Its objective is to contribute to the establishment of an advanced capitalistic system by securing the sustainability of the South Korean economy. Such win-win growth policy includes three core concepts. The first concept, ecosystem, is that win-win growth should be understood from the viewpoint of an industrial ecosystem and should be pursued by overcoming the issues of specific enterprises. An enterprise is not an independent entity but a social entity, meaning it exists in relationship with the society (Drucker, 2011). The second concept, balance, points to the fact that an effort should be made to establish a systemic and social infrastructure for a healthy balance in the industry. The social system and infrastructure should be established in such a way as to create a balance between short- term needs and long-term sustainability, between freedom and responsibility, and between profitability and social obligations. Finally, the third concept is the behavioral change of economic entities. The win-win growth policy is not merely about simple transactional relationships or determining reasonable prices but more about the need for a behavior change on the part of economic entities, without which the objectives of the policy cannot be achieved. Various advanced countries have developed different win-win growth models based on their respective cultures and economic-development stages. Japan, whose culture is characterized by a relatively high level of group-centered trust, has developed a productivity improvement model based on such culture, whereas the U.S., which has a highly developed system of market capitalism, has developed a system that instigates or promotes market-oriented technological innovation. Unlike Japan or the U.S., Europe, a late starter, has not fully developed a trust-based culture or market capitalism and thus often uses a policy-led model based on which the government leads the improvement of productivity and promotes technological innovation. By modeling successful cases from these advanced countries, South Korea can establish its unique win-win growth system. For this, it needs to determine the method and tasks that suit its circumstances by examining the prerequisites for its success as well as the strengths and weaknesses of each advanced country. This paper proposes a South Korean model of win-win growth, whose objective is to upgrade the country's low-trust-level-based industrial structure, in which large enterprises and SMEs depend only on independent survival strategies, to a high-trust-level-based social ecosystem, in which large enterprises and SMEs develop a cooperative relationship as partners. Based on this objective, the model proposes the establishment of a sound balance of systems and infrastructure between large enterprises and SMEs, and to form a crenovative social ecosystem. The South Korean model of win-win growth consists of three axes: utilization of the South Koreans' potential, which creates community-oriented energy; fusion-style improvement of various control and self-regulated systems for establishing a high-trust-level-oriented social infrastructure; and behavioral change on the part of enterprises in terms of putting an end to their unfair business activities and promoting future-oriented cooperative relationships. This system will establish a dynamic industrial ecosystem that will generate creative energy and will thus contribute to the realization of a sustainable economy in the 21st century. The South Korean model of win-win growth should pursue community-based self-regulation, which promotes the power of efficiency and competition that is fundamentally being pursued by capitalism while at the same time seeking the value of society and community. Already existing in Korea's traditional roots, such objectives have become the bases of the Shinbaram culture, characterized by the South Koreans' spontaneity, creativity, and optimism. In the process of a community's gradual improvement of its rules and procedures, the trust among the community members increases, and the "social capital" that guarantees the successful control of shared resources can be established (Ostrom, 2010). This basic ideal can help reduce the gap between large enterprises and SMEs, alleviating the South Koreans' victim mentality in the face of competition and the open-door policy, and creating crenovative corporate competitiveness. The win-win growth policy emerged for the purpose of addressing the polarization and imbalance structure resulting from the evolution of 21st-century capitalism. It simultaneously pursues efficiency and fairness on one hand and economic and community values on the other, and aims to foster efficient interaction between the market and the government. This policy, however, is also evolving. The win-win growth policy can be considered an extension of the win-win cooperation that the past 'Participatory Government' promoted at the enterprise management level to the level of systems and culture. Also, the ecosystemic development agendum that has recently emerged is a further extension that has been presented as a national ideal of "a new development model that promotes the co-advancement of environmental conservation, growth, economic development, social integration, and national and individual development."