• Journal of Digital Convergence
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• v.20 no.4
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• pp.791-797
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• 2022

Through recent metaverse marketing case studies, success factors and activation methods were analyzed from the perspective of content, platform, network, and device of the metaverse ecosystem in each industry. The importance of contents and platform of metaverse could be found in entertainment, fashion, office space and real estate, education, advertisement and commerce industries. In order to vitalize the metaverse, firstly, it is necessary to strengthen active participation and retention by providing a stable revenue model for market participants. Secondly, the importance of attractive content to expand subscribers is a key trigger for metaverse activation. Thirdly, it is necessary to increase the convenience of using metaverse service by using a light and simple device for the user. Fourthly, a win-win cooperation strategy should be supported in the value chain of the industry through ecosystem scalability. In addition, business opportunities for market participants and additional revenue models should be continuously provided.

• Journal of Convergence for Information Technology
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• v.9 no.3
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• pp.16-21
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• 2019

Small and medium-sized Korean companies are currently changing their industrial structure faster than in the past due to various environmental factors (such as securing competitiveness and developing excellent products). In particular, the importance of collecting and utilizing data produced in smart factory environments is increasing as diverse devices related to artificial intelligence are put into manufacturing sites. This paper proposes an artificial intelligence-based smart factory model to improve the process of products produced at the manufacturing site with the recent smart factory. The proposed model aims to ensure the increasingly competitive manufacturing environment and minimize production costs. The proposed model is managed by considering not only information on products produced at the site of smart factory based on artificial intelligence, but also labour force consumed in the production of products, working hours and operating plant machinery. In addition, data produced in the proposed model can be linked with similar companies and share information, enabling strategic cooperation between enterprises in manufacturing site operations.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.1
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• pp.9-18
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• 2018

Enhancing performance of big data analytics in distributed environment has been issued because most of the big data related applications such as machine learning techniques and streaming services generally utilize distributed computing frameworks. Thus, optimizing performance of those applications at Spark has been actively researched. Since optimizing performance of the applications at distributed environment is challenging because it not only needs optimizing the applications themselves but also requires tuning of the distributed system configuration parameters. Although prior researches made a huge effort to improve execution performance, most of them only focused on one of three performance optimization aspect: application design, system tuning, hardware utilization. Thus, they couldn't handle an orchestration of those aspects. In this paper, we deeply analyze and model the application processing procedure of the Spark. Through the analyzed results, we propose performance optimization schemes for each step of the procedure: inner stage and outer stage. We also propose appropriate partitioning mechanism by analyzing relationship between partitioning parallelism and performance of the applications. We applied those three performance optimization schemes to WordCount, Pagerank, and Kmeans which are basic big data analytics and found nearly 50% performance improvement when all of those schemes are applied.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.207-218
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• 2020

Recently, due to the increase of online reviews and the development of analysis technology, the interest and demand for online review analysis continues to increase. However, previous studies have not considered the emotions contained in each vocabulary may differ from one reviewer to another. Therefore, this study first classifies the customer group according to the customer's grade, and presents the result of analyzing the difference by performing review analysis for each customer group. We found that the price factor had a significant influence on the evaluation of products for customers with high ratings. On the contrary, in the case of low-grade customers, the degree of correspondence between the contents introduced in the mall and the actual product significantly influenced the evaluation of the product. We expect that the proposed methodology can be effectively used to establish differentiated marketing strategies by identifying factors that affect product evaluation by customer group.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1213-1216
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• 2017

Recently due to the increasing uncertainty of the disaster environment caused by climate change the effects of disasters have become larger due to the confluence and solidification diversification into disaster type and secondary damage. In this paper, we apply ICBMS through intelligent information technology and big data analysis to all processes of disaster safety management to minimize human, social, economic and environment damage from accidents or disasters, and prevention by control technology preparation by education and training expansion to remember by body, response by advanced technology of disaster response unmanned technology restoration by creation of local community environment ecosystem, investigation and analysis by intelligent information technology learn about disaster safety management 4.0. In addition, technical limitation and problems in the $4^{th}$ industrial revolution and the application of big data were analyzed and suggested alternatives and strategies to overcome.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.4
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• pp.53-63
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• 2013

Together with soaring interest on Big Data, now there are vigorous reports that unearth various social values lying underneath those data from a number of application areas. Among those reports many are using such data as Internet search histories from Google site, social relationships from Facebook, and transactional or locational traces collected from various ubiquitous devices. Many of those researches, however, are conducted based on the data sets that are accumulated over the North American and European areas, which means that direct interpretation and application of social values exhibited by those researches to the other areas like Korea can be a disturbing task. This research has started from a validation study against Korean environment of the former paper which says an investment strategy that exploits up and down of Google search volume on a carefully selected set of terms shows high market performance. A huge difference between North American and Korean environment can be eye witnessed via the distinction in profit rates that are exhibited by the corresponding set of search terms. Two sets of search terms actually presented low correlation in their profit rates over two financial markets. Even in an experiment which compares the profit rates with two different investment periods with the same set of search terms showed no such meaningful result that outperforms the market average. With all these results, we cautiously conclude that establishing an investment strategy that exploits Internet search volume over a specified word set needs more conscious approach.

• Journal of the Korea Convergence Society
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• v.11 no.7
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• pp.9-18
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• 2020

The research objectives investigates main research topics and trends in the information and communication technology(ICT) field, Korea using LDA(Latent Dirichlet Allocation), one of the topic modeling techniques. The experimental dataset of ICT research and development(R&D) project of 5,200 was acquired through matching with the EZone system of IITP after downloading R&D project dataset from NTIS(National Science and Technology Information Service) during recent five years. Consequently, our finding was that the majority research topics were found as intelligent information technologies such as AI, big data, and IoT, and the main research trends was hyper realistic media. Finally, it is expected that the research results of topic modeling on the national R&D foundation dataset become the powerful information about establishment of planning and strategy of future's research and development in the ICT field.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.111-116
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• 2019

New technologies such as the IoT, Big Data, and Artificial Intelligence, starting from the Web, mobile, and smart device, enable new business models that did not exist before, and various types of Internet companies based on these business models has been emerged. In this research, we examine the factors that influence the success and failure of Internet companies. To do this, we review the recent studies on business model and examine the variables affecting the success of Internet companies in terms of network effect, user interface, cooperation with actors, creating value for users. Using the five derived variables, we will select 14 Internet companies that succeeded and failed in seven commercial business model categories. We derive decision tree by applying inductive learning based on ID3 algorithm to the analysis result and derive rules that affect success and failure based on derived decision tree. With these rules, we want to present the strategic implications for actors to succeed in Internet companies.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.136-136
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• 2016

현재 전 세계적으로 지속적인 인구증가와 도시화 및 산업화 영향으로 물, 에너지, 식량의 수요증가와 공급부족이 예상되는 가운데, 급격한 기후변화와 자원고갈로 인해 이는 더욱 가속화되고 심화될 위기에 직면하였다. 특히 물의 수요는 증가하나 공급은 제한되어 있어 지속가능한 인류의 생존을 위하여 인간에게 꼭 필요한 에너지와 식량의 문제를 물을 중심으로 상관성을 찾고 효율적인 물활용 방법을 개발하고 정책화할 필요가 있다. 세계 물공급량 중 농업으로는 70%, 에너지로는 15%로 소비되고 있다. 또한 유럽과 미국은 발전용 냉각수로 각각 담수총량의 43%, 50%를 사용하고 있는 등, 세계는 물, 식량 그리고 에너지 수급의 불균형이 심화되고 상호 위기가 증폭되고 있다. 향후 인간의 삶에 절대적으로 필요한 세가지 자원이 복합적으로 연계되어 상호 위기가 증폭될 수 있는 실정이나, 우리나라는 개별자원에 대한 관리 및 운영기술은 상당한 수준이지만 아직까지 연계성을 고려하여 자원간의 효율성을 찾는 물-에너지-식량 연계(Water-Energy-Food Nexus, WEF Nexus)에 대한 관리 및 기술은 부족한 실정이다. 국제적으로 세자원의 연계상황이 선진국과 개도국간에 편차가 크고, 사막지대 등 자연조건이 열악한 지역에서 자원들간의 위기 연계성이 높은 상황이므로 우리나라도 물-에너지-식량의 연계위기 상황을 정확히 파악하고 미래 환경변화에 대한 대응방안 수립이 필요하나, 현재 국내 물관리에 있어 저수지나 수리시설 관리가 제대로 실행되지 못하고 있어 이를 방치할 경우 중장기적으로 식량 및 에너지생산에 있어 부족이 우려되고 있다. 또한 부처간의 행정적인 간격으로 물-에너지-식량의 연계성 연구 및 정책수립에 있어서 자료 등이 절대적으로 부족한 실정이기 때문에 체계적으로 빅데이터 기반의 DB구축 및 인벤토리 정의 등도 현재시점에서는 절대적으로 필요하다고 할 수 있다. WEF Nexus를 실현하기 위해서는 각 자원간의 효율성을 극대화하여야 한다. 과거에는 개별적 자원의 확보를 추구했다면 이제부터는 각 자원간의 상생을 통한 연계성을 확보하여 서로간의 자원 확보를 고려하여 개발하여야 한다. 이를 위해 현재의 자원확보에 대한 문제점들을 고려하여, 상호 연계를 통한 효율성을 확보하고 그 효율성이 각 자원에 영향을 주어 시너지효과를 발생시킬 수 있는 방안으로 진행되어야 할 것으로 판단된다.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.12
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• pp.329-342
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• 2021

The pharmaceutical industry is experiencing a productivity crisis with a low probability of success despite a long period of time and enormous cost. As a strategy to solve the productivity crisis, the use cases of Artificial Intelligence(AI) and Bigdata are increasing worldwide and tangible results are coming out. However, domestic pharmaceutical companies are taking a wait-and-see attitude to adopt AI platform for drug research. This study proposed a research model that combines the Value-based Adoption Model and the Innovation Resistance Model to empirically study the effect of value perception and resistance factors on adopting AI Platform. As a result of empirical verification, usefulness, knowledge richness, complexity, and algorithmic opacity were found to have a significant effect on perceived values. And, usefulness, knowledge richness, algorithmic opacity, trialability, technology support infrastructure were found to have a significant effect on the innovation resistance.