• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.175-183
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• 2023

In a broadcasting market environment that has a close interdependence between platform operators and content operators, problems such as conflicts over program usage fees, and home shopping transmission fees are intensifying. This study attempted to analyze the environment of the domestic broadcasting market and present implications, analyze the cause of user fee conflict between the platform and PP, and propose detailed alternatives to resolve user fee conflict disputes. The results of environmental analysis on the domestic broadcasting market are as follows. First, the growth engine of the broadcasting industry has changed to direct resources such as service usage fees and content fees, and commerce is increasing. Second, as hegemony in the domestic broadcasting market changes from terrestrial to paid broadcasting and OTT, monopolies in the entire broadcasting area are being dismantled by voluntary entry. Third, the need to overhaul the existing regulatory system is increasing due to the dismantling and reorganization of the existing broadcasting market. On the other hand, this study proposed a strategy to diversify the profit structure of PP, supply program after pre-contracting, and strengthen CPS bargaining power in order to resolve disputes between paid broadcasting platforms and PP sharply. In particular, as strategies to strengthen CPS bargaining power of small and medium-sized SOs, it proposed to jointly improve CPS-related systems through IPTV and individual SOs, to redefine fees for programs and to voluntarily use programs.

• Journal of Korea Entertainment Industry Association
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• v.13 no.2
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• pp.37-45
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• 2019

The This study aimed at clarifying that the cognitive interpretation code is essential for college students to read the correct culture while discussing the producer's story production system for creative advertising content and the process of interpreting the meaning of advertisers and the formation of principles and beliefs. The production of advertising content aimed at persuasion should first identify anachronistic reason system based on the target audience's perception principle. A concise analysis of the experiment found key clues that confirmed that a sample of the producer's intended story would be inconsistent with the clues of information that a college student could remember. I have tried to organize a semantic analysis tool that combines these key clues and as a tool for reading culture of the right time for college students. As a result, university student inmates were able to identify one side of positive communication: reading a new cognitive symbol culture based on their subjective experience and beliefs, rather than analyzing cross-sectional analysis of the primary language and non-verbal expressions of their advertising contents. In the future, if an advertising content story producer works to identify such a process in advance, it will help persuade inmates.

• Journal of the Korean Society of Safety
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• v.38 no.2
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• pp.96-103
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• 2023

Generally, human stampedes and crowd collapses occur when people press against each other, causing falls that may result in death or injury. Particularly, crowd accidents have become increasingly common since the 1990s, with an average of 380 deaths annually. For instance, in Korea, a stampede occurred during the Itaewon Halloween festival on October 29, 2022, when several people crowded onto a narrow, downhill road, which was 45 meters long and between 3.2 and 4 meters wide. Precisely, this stampede was primarily due to the excessive number of people relative to the road size. Essentially, stampedes can occur anywhere and at any time, not just at events, but also in other places where large crowds gather. More specifically, the likelihood of accidents increases when the crowd density exceeds a turbulence threshold of 5-6 /m². Meanwhile, festivals and events, which have become more frequent and are promoted through social media, garner people from near and far to a specific location. Besides, as cities grow, the number of people gathering in one place increases. While stampedes are rare, their impact is significant, and the uncertainty associated with them is high. Currently, there is no scientific system to analyze the risk of stampedes due to crowd concentration. Consequently, to prevent such accidents, it is essential to prepare for crowd disasters that reflect social changes and regional characteristics. Hence, this study proposes using digital topographic maps and crowd-density risk simulations to develop a 3D model of the region. Specifically, the crowd density simulation allows for an analysis of the density of people walking along specific paths, which enables the prediction of danger areas and the risk of crowding. By using the simulation method in this study, it is anticipated that safety measures can be rationally established for specific situations, such as local festivals, and preparations may be made for crowd accidents in downtown areas.

• Journal of Korea Entertainment Industry Association
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• v.13 no.8
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• pp.117-130
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• 2019

After Marx, the issues regarding human labour have been the alienation towards production means and the distributive justice. Fourth industrial revolution and development of AI(Artificial Intelligence) opened the possibility of a independent production and economy system absolutely excluding against human nature and labour. Using robots and AI will deepen demarcation between living things and one not having life, separating the intelligence from the consciousness. At present, so called pre-stage of post human, seeking interests for life, new social relationship and new community will be increased as well. We can understand that interests for small community, self-sufficiency, dailiness, food and body in this context is increasing too. Representative trend towards this cultural phenomena is called as the 'Kinfolk culture.' Work-life balance, 'Aucalme', 'Hygge', 'So-Hwak-Haeng'(a small but reliable happiness) are the similar culture trends as. Vital capitalism, presented by O-Yong Lee, seeks focusing onto living things principles, e.g. 'topophilia', 'neophilia', and 'biophilia' as the dynamics looking for the history substructure, not class struggle and conflicts. He also argues the 'Vital Capitalism' be regarded as a new methodology to anticipate a social system after post human era. G. Deleuze said "arts is another expression method for existential philosophy. It gives a vitality onto philosophy and gives a role to letting abstract concept into definite image." We can find a lot cases arts' imagination overcomes critical point of scientific prediction power in the future prediction. This paper reviews ideas and issues of 'vital capitalism' in detail and explorers imaginating initial ideas of vital capitalism in the film 'Little Forest.'

• Asia Marketing Journal
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• v.13 no.4
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• pp.213-226
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• 2012

As the Korean rental car industry turned into a mature stage, the competition level has become stronger than ever. In 2006, AJ Rent a Car declared customer satisfaction management as its vision to make a breakthrough. Through various service innovation efforts, AJ has been successfully offering meaningful and differentiated values to the customers. As results, the complaints rate has decreased, while service quality index has significantly increased. These service quality indicators have led to improved customer satisfaction level which was measured by re-purchase intention and customer satisfaction index, and AJ outran its major competitors in these dimensions of competition. The first key success factor of AJ is its effective service system. AJ manages the VOC, ERP, and CRM system in a well organized manner. AJ's another key success factor is a effective service process, which helps the organization share and respond to customer complaints in an efficient way. Finally, the management communicates the clear vision and strategic direction not only with the customers but also with the entire organization. With these three factors combined, AJ has created the service oriented corporate culture. Based on the culture. AJ has been able to develop a strong and sustainable competitive advantage in customer satisfaction management.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.35 no.2
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• pp.5-23
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• 2024

The seventh year of implementing online material deposit demands a systematic collection, legal and regulatory improvements, and the establishment of a long-term strategic plan for online material collection. In this study, we aimed to propose an online material collection strategy and a long-term roadmap for preserving online resources as national intellectual and cultural heritage for future generations. To achieve this, we analyzed the status of domestic and foreign libraries, related laws and regulations, and the types and collection status of online materials. Based on this analysis, we proposed practical collection standards and methods. Ultimately, a long-term roadmap and implementation plan were suggested. The long-term development plan for online material collection established a phased, concrete implementation strategy. This includes the foundation-building phase of online material collection, followed by the expansion phase, and finally reaching the maturity phase.

• Journal of Advanced Technology Convergence
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• v.3 no.1
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• pp.59-64
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• 2024

I believe that establishing the purpose of research on the MZ generation's communication is an important first step in understanding this generation's unique communication style and analyzing its influence. Research on the MZ generation's communication identifies the communication characteristics of the MZ generation and the communication tools and platforms used by the MZ generation, such as social media and messenger apps, and analyzes how they differ from the existing generation. I can understand. It has been shown that the MZ generation can live happily in modern society without a complicated philosophy or a clear philosophy of life. This does not mean that life is meaningless or confusing. The MZ generation can be satisfied with simple and concrete solutions to the meaning of life, and can live without the need to completely systematize everything. In other words, their lives are not as complicated as those of previous generations and can have a variety of meanings. In other words, it does not necessarily need to be defined as a philosophical system. Although this paper cannot clearly divide the lives of the MZ generation into one philosophical system, it was nevertheless possible to see that the lives of each member of the MZ generation can have many meanings, and this meaning includes the MZ generation's unique purposes, values, I could see that they were looking for a sense of ability and a sense of self-worth.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.73-92
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• 2014

An Adaptive Clustering-based Collaborative Filtering Technique was proposed to solve the fundamental problems of collaborative filtering, such as cold-start problems, scalability problems and data sparsity problems. Previous collaborative filtering techniques were carried out according to the recommendations based on the predicted preference of the user to a particular item using a similar item subset and a similar user subset composed based on the preference of users to items. For this reason, if the density of the user preference matrix is low, the reliability of the recommendation system will decrease rapidly. Therefore, the difficulty of creating a similar item subset and similar user subset will be increased. In addition, as the scale of service increases, the time needed to create a similar item subset and similar user subset increases geometrically, and the response time of the recommendation system is then increased. To solve these problems, this paper suggests a collaborative filtering technique that adapts a condition actively to the model and adopts the concepts of a context-based filtering technique. This technique consists of four major methodologies. First, items are made, the users are clustered according their feature vectors, and an inter-cluster preference between each item cluster and user cluster is then assumed. According to this method, the run-time for creating a similar item subset or user subset can be economized, the reliability of a recommendation system can be made higher than that using only the user preference information for creating a similar item subset or similar user subset, and the cold start problem can be partially solved. Second, recommendations are made using the prior composed item and user clusters and inter-cluster preference between each item cluster and user cluster. In this phase, a list of items is made for users by examining the item clusters in the order of the size of the inter-cluster preference of the user cluster, in which the user belongs, and selecting and ranking the items according to the predicted or recorded user preference information. Using this method, the creation of a recommendation model phase bears the highest load of the recommendation system, and it minimizes the load of the recommendation system in run-time. Therefore, the scalability problem and large scale recommendation system can be performed with collaborative filtering, which is highly reliable. Third, the missing user preference information is predicted using the item and user clusters. Using this method, the problem caused by the low density of the user preference matrix can be mitigated. Existing studies on this used an item-based prediction or user-based prediction. In this paper, Hao Ji's idea, which uses both an item-based prediction and user-based prediction, was improved. The reliability of the recommendation service can be improved by combining the predictive values of both techniques by applying the condition of the recommendation model. By predicting the user preference based on the item or user clusters, the time required to predict the user preference can be reduced, and missing user preference in run-time can be predicted. Fourth, the item and user feature vector can be made to learn the following input of the user feedback. This phase applied normalized user feedback to the item and user feature vector. This method can mitigate the problems caused by the use of the concepts of context-based filtering, such as the item and user feature vector based on the user profile and item properties. The problems with using the item and user feature vector are due to the limitation of quantifying the qualitative features of the items and users. Therefore, the elements of the user and item feature vectors are made to match one to one, and if user feedback to a particular item is obtained, it will be applied to the feature vector using the opposite one. Verification of this method was accomplished by comparing the performance with existing hybrid filtering techniques. Two methods were used for verification: MAE(Mean Absolute Error) and response time. Using MAE, this technique was confirmed to improve the reliability of the recommendation system. Using the response time, this technique was found to be suitable for a large scaled recommendation system. This paper suggested an Adaptive Clustering-based Collaborative Filtering Technique with high reliability and low time complexity, but it had some limitations. This technique focused on reducing the time complexity. Hence, an improvement in reliability was not expected. The next topic will be to improve this technique by rule-based filtering.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.