• Korean journal of communication and information
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• v.35
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• pp.250-291
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• 2006

This study examines the regulatory issues and introduction problems of TV-betting data broadcasts in Korea by in-depth interview with a panel group. TV-betting data broadcast services of card games and horse racing games are widely in use in Europe and other parts of the world. In order to carry out the study, a demo program of TV-betting data broadcast in the OCAP(OpenCableTM Application Platform Specification) system environment, which is the data broadcasting standard for digital cable broadcasts in Korea was exposed to the panel group and then they were interviewed after watching and using the program. The results could be summarized as below. First of all, while TV-betting data broadcasts have many elements of entertainment, the respondents thought that it would be difficult to introduce TV-betting in data broadcasts as in overseas countries largely due to social factors. In addition, in order to introduce TV-betting data broadcasts, they suggested that excessive speculativeness must be suppressed through a series of regulatory system devices, such as by guaranteeing credibility of the media based on safe security systems for transactions, scheduling programs with effective time constraints to prevent the games from running too frequently, limiting the betting values, and by prohibiting access to games through set-top boxes of other data broadcast subscribers. The general consensus was that TV-betting could be considered for gradual introduction within the governmental laws and regulations that would minimize its ill effects. Therefore, the government should formulate long-term regulations and policies for data broadcasts. Once the groundwork is laid for safe introduction of TV-betting on data broadcasts within the boundary of laws and regulations, interactive TV games are expected to be introduced in Korea not only for added functionality of entertainment but also for far-ranging development of data broadcast and new media industries.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.2
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• pp.201-219
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• 2014

The Republic of Korea in the real economy to a knowledge economy, and a center of creativity and imagination in the creative economy is changing the paradigm. As the core of creating economic, creative industries with the technology and information play an important role in the industry individuals. In order to solve the problem of the polarization of the economy and high youth unemployment rate of Korea, to recognize the role of the creative industries, as objection part, dimensions pan-national and one creative companies in industries of Mobile Apps various policies that support has been promoted. Support these policies to be able to contribute to the establishment of the success of mobile apps one-person creating company, we performed this study targeting one-person company that creates mobile apps area, we conducted a demonstration study of success factors, and thus more effective and efficient in an attempt to seek out support measures. In this study, we derive a research 4 hypothesis about the success factors of one creative enterprise through literature discussion, a study was made on the basis of empirical data of one-person company that creates mobile apps. The results of the analysis, first, if the development rate of the mobile application technology is fast and a new competition associated product is appeared, it was possible to find a tendency to be higher at the performance quantitative companies. Second, if the founder is a founding for the benefit and rewarding work and come to terms with the risk, it was possible to discover tends to be higher achievement quantitative. Third, if one-person company select a target market with capture intensively, it was possible to find a tendency for higher qualitative results. Fourth, it could be found that the reliability of the contact frequency of the network related performance business environment these characteristics enterprise management strategy and act as a significant modulatory effect. Provision of information relating to management and entrepreneurship education to be one creative enterprise is required, these results suggest that there is a provision continuing need for the opportunity to be able to meet and network and reliable variety have. In this study, to take advantage to promote the elimination measures that can increase the likelihood of success of the company of institutions to support one company that creates knowledge-based, such as in the field of mobile application.

• Korean journal of communication and information
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• v.73
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• pp.56-91
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• 2015

This study examined changes in the independent production environment in the perspectives from flexible specialization of labor and media routines since January 2011, when comprehensive programming television channels (JTBC, MBN, Channel A, TV Chosun) emerged in Korea. In-depth interviews were conducted with thirteen individuals, including producers from independent production companies, broadcast writers, and individuals involved with these TV channels. The interview results indicated that a flexible specialization production system had been established by the comprehensive programming channels. This means that they were heavily dependent on independent producers, except in relations to their own news programs. Moreover, it was identified that the production of diverse programs could be difficult due to absurd contract practices such as those related to TV ratings and performance systems. Second, these channels have implemented some positive changes such as the payment of higher production costs and an incentive system, compared to terrestrial TV stations. However, the incentive system also helps to aggravate internal competition in the channel and also instigate contract competitions among independent companies, which can eventually result in the channels for holding exclusive rights to certain content and, hence, unfair business practices. Third, as a result of the newspaper and broadcast cross-owenership system of the comprehensive programming channels, hierarchical independent production practices can be established under the influence of newspaper proprietors and executives or managers who have previously worked for newspapers. Lastly, as a result of interviews with independent producers and individuals involved with the TV channels concerning the awareness of comprehensive programming channels, it could not be ascertained whether it is difficult to produce programs dealing with diverse items and genres, because programming autonomy has been distorted by capital or the advertisement market. In this circumstance, it is not surprising that some comprehensive programming channels mentioned that they prioritize profit and performance in programming. In conclusion, it is absolutely imperative that complementary and legal measures be implemented institutionally in order to redress the existing systematic dysfunctional routines in the independent productions of the comprehensive programming TV channels in Korea.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.29-45
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• 2015

Response modeling is a well-known research issue for those who have tried to get more superior performance in the capability of predicting the customers' response for the marketing promotion. The response model for customers would reduce the marketing cost by identifying prospective customers from very large customer database and predicting the purchasing intention of the selected customers while the promotion which is derived from an undifferentiated marketing strategy results in unnecessary cost. In addition, the big data environment has accelerated developing the response model with data mining techniques such as CBR, neural networks and support vector machines. And CBR is one of the most major tools in business because it is known as simple and robust to apply to the response model. However, CBR is an attractive data mining technique for data mining applications in business even though it hasn't shown high performance compared to other machine learning techniques. Thus many studies have tried to improve CBR and utilized in business data mining with the enhanced algorithms or the support of other techniques such as genetic algorithm, decision tree and AHP (Analytic Process Hierarchy). Ahn and Kim(2008) utilized logit, neural networks, CBR to predict that which customers would purchase the items promoted by marketing department and tried to optimized the number of k for k-nearest neighbor with genetic algorithm for the purpose of improving the performance of the integrated model. Hong and Park(2009) noted that the integrated approach with CBR for logit, neural networks, and Support Vector Machine (SVM) showed more improved prediction ability for response of customers to marketing promotion than each data mining models such as logit, neural networks, and SVM. This paper presented an approach to predict customers' response of marketing promotion with Case Based Reasoning. The proposed model was developed by applying different weights to each feature. We deployed logit model with a database including the promotion and the purchasing data of bath soap. After that, the coefficients were used to give different weights of CBR. We analyzed the performance of proposed weighted CBR based model compared to neural networks and pure CBR based model empirically and found that the proposed weighted CBR based model showed more superior performance than pure CBR model. Imbalanced data is a common problem to build data mining model to classify a class with real data such as bankruptcy prediction, intrusion detection, fraud detection, churn management, and response modeling. Imbalanced data means that the number of instance in one class is remarkably small or large compared to the number of instance in other classes. The classification model such as response modeling has a lot of trouble to recognize the pattern from data through learning because the model tends to ignore a small number of classes while classifying a large number of classes correctly. To resolve the problem caused from imbalanced data distribution, sampling method is one of the most representative approach. The sampling method could be categorized to under sampling and over sampling. However, CBR is not sensitive to data distribution because it doesn't learn from data unlike machine learning algorithm. In this study, we investigated the robustness of our proposed model while changing the ratio of response customers and nonresponse customers to the promotion program because the response customers for the suggested promotion is always a small part of nonresponse customers in the real world. We simulated the proposed model 100 times to validate the robustness with different ratio of response customers to response customers under the imbalanced data distribution. Finally, we found that our proposed CBR based model showed superior performance than compared models under the imbalanced data sets. Our study is expected to improve the performance of response model for the promotion program with CBR under imbalanced data distribution in the real world.

• Korean Journal of Remote Sensing
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• v.37 no.6_2
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• pp.1849-1858
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• 2021

We analyzed the monthly and annual trends of the meteorological factors(wind speeds and directions and air temperatures) measured at an automated synoptic observation system (ASOS) and fine particle (PM₁₀ and PM_2.5) concentrations measured at the air quality monitoring systems(AQMSs) in Suwon. In addition, we investigated how the fine particle concentrations were related to the meteorological factors as well as urban morphological parameters (fractions of building volume and road area). We calculated the total volume of buildings and the total area of the roads in the area of 2 km × 2 km centered at each AQMS using the geographic information system and environmental geographic information system. The analysis of the meteorological factors showed that the dominant wind directions at the ASOS were westerly and northwesterly and that the average wind speed was strong in Spring. The measured fine particle concentrations were low in Summer and early Autumn (July to September) and high in Spring and Winter. In 2020, the annual mean fine particle concentration was lowest at most AQMSs. The fine particle concentrations were negatively and weakly correlated with the measured wind speeds and air temperatures (the correlation between PM_2.5 concentrations and air temperatures was relatively strong). In Suwon city, at least for 6 AQMSs except for the RAQMS 131116 and AQMS 131118, the PM₁₀ concentrations were affected mainly by the transport from outside rather than primary emission from mobile sources or wind speed decrease caused by buildings and, in the case of PM_2.5, vise versa.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.903-916
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• 2021

An area's topography refers to the shape of the earth's surface, described by its elevation, slope, and aspect, among other features. The topographical conditions determine energy flowsthat move water and energy from higher to lower elevations, such as how much solar energy will be received and how much wind or rain will affect it. Another common factor, the topographic wetness index (TWI), is a calculation in digital elevation models of the tendency to accumulate water per slope and unit area, and is one of the most widely referenced hydrologic topographic factors, which helps explain the location of forest vegetation. Analyses of topographical factors can be calculated using a geographic information system (GIS) program based on digital elevation model (DEM) data. Recently, a large number of free open source software (FOSS) GIS programs are available and developed for researchers, industries, and governments. FOSS GIS programs provide opportunitiesfor flexible algorithms customized forspecific user needs. The majority of biodiversity in island areas exists at about 20% higher elevations than in land ecosystems, playing an important role in ecological processes and therefore of high ecological value. However, island areas are vulnerable to disturbances and damage, such as through climate change, environmental pollution, development, and human intervention, and lacks systematic investigation due to geographical limitations (e.g. remoteness; difficulty to access). More than 4,000 of Korea's islands are within a few hours of its coast, and 88% are uninhabited, with 52% of them forested. The forest ecosystems of islands have fewer encounters with human interaction than on land, and therefore most of the topographical conditions are formed naturally and affected more directly by weather conditions or the environment. Therefore, the analysis of forest topography in island areas can be done more precisely than on its land counterparts, and therefore has become a major focus of attention in Korea. This study is focused on calculating the performance of different topographical factors using FOSS GIS programs. The test area is the island forests in Korea's south and the DEM of the target area was processed with GRASS GIS and SAGA GIS. The final slopes and TWI maps were produced as comparisons of the differences between topographic factor calculations of each respective FOSS GIS program. Finally, the merits of each FOSS GIS program used to calculate the topographic factors is discussed.

• The Korean Journal of Archival Studies
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• no.14
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• pp.153-190
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• 2006

Externally, business environment in public institution has being changed as government business reference model(BRM) appeared and business management systems for transparency of a policy decision process are introduced. After Records Automation System started its operation, dissatisfaction grows because of inadequacy in system function and the problems about authenticity of electronic records. With these backgrounds, National Archives and Records Service had carried out 'Information Strategy Planning for Reform to Records Management System' for 5 months from September, 2005. As result, this project reengineers current records management processes and presents the world-class system model. After Records and Archives Management Act was made, the records management in public institution has propelled the concept that paper records are handled by means of the electric data management. In this reformed model, however, we concentrates on the electric records, which have gradually replaced the paper records and investigate on the management methodology considering attributes of electric records. According to this new paradigm, the electric records management raises a new issue in the records management territory. As the major contents of the models connecting with electric records management were analyzed and their significance and bounds were closely reviewed, the aim of this paper is the understanding of the future bearings of the management system. Before the analysis of the reformed models, issues in new business environments and their records management were reviewed. The government's BRM and Business management system prepared the general basis that can manage government's whole results on the online and classify them according to its function. In this points, the model is innovative. However considering the records management, problems such as division into Records Classification, definitions and capturing methods of records management objects, limitations of Records Automation System and so on was identified. For solving these problems, the reformed models that has a records classification system based on the business classification, extended electronic records filing system, added functions for strengthening electric records management and so on was proposed. As regards dramatically improving the role of records center in public institution, searching for the basic management methodology of the records management object from various agency and introducing the detail design to keep documents' authenticity, this model forms the basis of the electric records management system. In spite of these innovations, however, the proposed system for real electric records management era is still in its beginning. In near feature, when the studies is concentrated upon the progress of qualified classifications, records capturing plans for foreign records structures such like administration information system, the further study of the previous preservation technology, the developed prospective of electric records management system will be very bright.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.1-25
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• 2023

ESG management is becoming a necessity of the times, but there are about 600 ESG evaluation indicators worldwide, causing confusion in the market as different ESG ratings were assigned to individual companies according to evaluation agencies. In addition, since the method of applying ESG was not disclosed, there were not many ways for companies that wanted to introduce ESG management to get help. Accordingly, the Ministry of Trade, Industry and Energy announced the K-ESG guideline jointly with the ministries. In previous studies, there were few studies on the comparison of evaluation grades by ESG evaluation company or the application of evaluation diagnostic items. Therefore, in this study, the ease of application and improvement of the K-ESG guideline was attempted by applying the K-ESG guideline to companies that already have ESG ratings. The position of the K-ESG guideline is also confirmed by comparing the scores calculated through the K-ESG guideline for companies that have ESG ratings from global ESG evaluation agencies and domestic ESG evaluation agencies. As a result of the analysis, first, the K-ESG guideline provide clear and detailed standards for individual companies to set their own ESG goals and set the direction of ESG practice. Second, the K-ESG guideline is suitable for domestic and global ESG evaluation standards as it has 61 diagnostic items and 12 additional diagnostic items covering the evaluation indicators of global representative ESG evaluation agencies and KCGS in Korea. Third, the ESG rating of the K-ESG guideline was higher than that of a global ESG rating company and lower than or similar to that of a domestic ESG rating company. Fourth, the ease of application of the K-ESG guideline is judged to be high. Fifth, the point to be improved in the K-ESG guideline is that the government needs to compile industry average statistics on diagnostic items in the K-ESG environment area and publish them on the government's ESG-only site. In addition, the applied weights of E, S, and G by industry should be determined and disclosed. This study will help ESG evaluation agencies, corporate management, and ESG managers interested in ESG management in establishing ESG management strategies and contributing to providing improvements to be referenced when revising the K-ESG guideline in the future.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• 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.