• Journal of Internet Computing and Services
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• v.17 no.2
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• pp.109-118
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• 2016

Big data, thanks to the development of data sciences, has been key words for economic development and governmental policies. This study reviewed how big data has been used with entertainment contents since the uses of big data in the fields have been more popular and the cases making successful business have been reported. To do ends, the changes in production, distribution, and consumption of entertainment contents have been characterized and prime cases have been introduced. Furthermore, Korean production companies of entertainment contents, Bloter and NEOTOUCHPOINT, were selected to investigate how big data has been utilized. It was found that the companies used big data to analyze consumers' behaviors and gain insights of content creation, such as identifying specific elements of enjoyments and conditions to share the contents with others. The domestic entertainment companies are preparing a full-scale of use of big data but, in order to take advantage of big data, collaboration between developers and experts in the field and specific goal-setting and model building are recommended.

• 한국디지털정책학회:학술대회논문집
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• 2007.06a
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• pp.233-248
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• 2007

최근 한국과 미국 기업에 있어서 회계법상 가장 큰 이슈 중 하나는 재무보고와 관련된 내부통제제도(ICFR: Internal Control over Financial Reporting)에 관한 것이다. 한국과 미국에서 각각 내부회계관리제도와 SOX(Sarbanes Oxley Act)라고 불려지고 있는 내부통제 법령에 의해 기업들은 자체적으로 내부통제를 구축, 운영하고, 이에 대해 매년 회계법인에 의해 검토 또는 감사를 받게 되었다. IT 부문도 기업 업무 영역에서 중요성을 더해가고 있기 때문에 내부통제 제도의 한 영역을 차지하게 되었다. 본 연구에서는 기업 특성과 IT 관리 특성이 기업의 IT 부문 내부통제 구축 시 어떤 영향을 미치는지에 대해 실제 기업 IT 내부통제 구축 데이터를 대상으로 하여 실증적인 분석을 실시하였다. 이를 위해 기업 특성 중 자산, 종업원 수, 업종과 IT 관리 특성 중 IT 기획팀 IT 아웃소싱, 주 시스템 플랫폼을 각각 독립변수로 선정하였고, 이들 6개 독립변수가 내부통제 구축 시 도출된 통제항목(Control Activities) 개수 대비 미비점(Deficiency) 비율, 통제항목 개수, 미비점 개수 각각에 대하여 영향을 미치는 지에 대해 검증하였다.

• Digital Contents
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• no.7 s.158
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• pp.52-55
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• 2006

일반적으로 검색엔진이라고하면 웹 포털이 불특정 다수를 대상으로 한 검색 서비스를 지칭하지만, 일반 기업이나 공공기관이 자사의 다양한 콘텐츠를 관리하기 위한 수단으로써 활용하는 경우가 있다. 바로 기업용 검색엔진(이하 검색엔진)이다. 지난해 국내 검색엔진 시장은 라이선스 기준으로 200억원 내외였다. 국내SW산업의 시장규모가 2조원을 훌쩍 넘기는 현실을 감안하면 보잘것 없는 수치이지만 검색엔진의 IT 인프라적성격을 이해하면 얘기는 달라진다. 폭발적으로 늘어나는 데이터에서 필요한 정보를 습득하기위해서는 검색엔진이 필수적이기때문. ‘작지만 큰 시장’인 국내검색엔진시장을 분석했다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.10
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• pp.2439-2446
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• 2013

Recently, SNS (Social Network Service) such as Twitter and Facebook has grown dramatically because of smart phones. Since development of IT has created massive information, social big data extremely increased. Competition between corporations is getting more intense, so they need customer feedback in order to fulfill an effective management. Because social big data plays an important role for getting customer feedback, a lot of corporations are interested in analyzing and applying of social big data. Collecting and analyzing social big data is operated by Buzz monitoring system. This paper demonstrates the research of buzz monitoring system that analyzes big data, and presents examples of customer reputation using buzz monitoring. In the paper, after all, it would analyze the result from the customer reputation, and research the implication.

• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.77-78
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• 2019

과거에서 지금까지 연예인의 로맨스 루머는 엔터테인먼트 기업의 측면에서 부정적인 요소였으며 이러한 모습은 기사로도 쉽게 접할 수 있다. 이 연구는 익명성 로맨스 루머가 엔터테인먼트 기업의 가치에 미치는 경제적 영향을 사건 연구 방법을 이용해 살펴보았다. 2013년부터 2017년까지 상장된 기업을 중심으로 총 112건의 로맨스 루머를 수집하였고 80건의 비 익명성 루머. 32건의 익명성 루머로 이루어져있다. 해당 엔터테인먼트의 주식 자료가 분석에 이용되었다. 연예인의 로맨스 정보가 유출된 시점 전후의 비정상 수익률 추정을 통해 연예인에 대한 로맨스 루머 언급에 기업의 가치 변화에 미치는 유의성을 실증적으로 검증하였고, 로맨스 루머에 있어서 익명성에 대한 영향이 비익명성의 영향과 차이가 있는지 비교분석을 하였다. 연구 결과 연예인의 로맨스 루머의 언급 및 유출은 해당 엔터테인먼트 기업의 주가 변동에 통계적으로 유의하지 않은 것으로 나타났으며, 추가적으로 연예인의 특성이 엔터테인먼트 기업의 주가에 어떠한 상이한 영향을 미치는지 또한 살펴보았다. 로맨스 루머의 특성은 '성별', '직업'으로 구분하였고, 이에 대한 분석 결과, 특징에 따라 기업의 가치에 큰 영향이 없는 것으로 관찰되었다. 결론적으로 본 연구는 사건연구 방법을 통해 연예인의 익명성 로맨스 루머가 엔터테인먼트 기업의 가치에 미치는 영향을 실제 시장 데이터를 활용하여 실증적으로 분석하였다는 것에 의미가 있다.

• The Journal of Society for e-Business Studies
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• v.23 no.1
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• pp.1-22
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• 2018

The value chain has been utilized as a strategic tool to improve competitive advantage, mainly at the enterprise level and at the industrial level. However, in order to conduct value chain analysis at the enterprise level, the client companies of the parent company should be classified according to whether they belong to it's value chain. The establishment of a value chain for a single company can be performed smoothly by experts, but it takes a lot of cost and time to build one which consists of multiple companies. Thus, this study proposes a model that automatically classifies the companies that form a value chain based on actual transaction data. A total of 19 transaction attribute variables were extracted from the transaction data and processed into the form of input data for machine learning method. The proposed model was constructed using the Random Forest algorithm. The experiment was conducted on a automobile parts company. The experimental results demonstrate that the proposed model can classify the client companies of the parent company automatically with 92% of accuracy, 76% of F1-score and 94% of AUC. Also, the empirical study confirm that a few transaction attributes such as transaction concentration, transaction amount and total sales per customer are the main characteristics representing the companies that form a value chain.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.3
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• pp.107-120
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• 2024

To study the relationship between various information sources and first-to-market product innovation, this research examined how the internal and external information sources relate to first-to-market product innovation and how government regulations (economic, social and administrative) affect first-to-market product innovations, applying the perspective of Institutional Theory. For the analysis, variables used were first-to-market product innovation, internal and external information sources, and economic, social, and administrative regulations. This study was conducted based on survey data from the Korean Innovation Survey 2020 by the Science and Technology Policy Institute of Korea (STEPI). The data analyzed included small and medium-sized enterprises (SMEs) in the manufacturing industry, and a total of 586 firms were used from 2017 to 2019. By analyzing the internal and external information source and government regulations that have been relatively underexplored in previous research, this study suggests new directions and provides practical and managerial implications based on the results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1544-1553
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• 2018

Recently internal information leakage in industries is severely increasing in spite of industry security policy. Thus, it is essential to prepare an information leakage prevention measure by industries. Most of the leaks result from the insiders, not from external attacks. In this paper, a real-time internal information leakage prevention system via both storage and network is implemented in order to protect confidential file leakage. In addition, a Hadoop-based user behavior analysis and statistics system is designed and implemented for storing and analyzing information log data in industries. The proposed system stores a large volume of data in HDFS and improves data processing capability using RHive, consequently helps the administrator recognize and prepare the confidential file leak trials. The implemented audit system would be contributed to reducing the damage caused by leakage of confidential files inside of the industries via both portable data media and networks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.211-213
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• 2013

In these days, the delusions of Big Data and apprehension about them are coming into the picture in many business fields. General techniques for preservation, analysis, and utilization of Big Data are falling short of useful techniques for the volume of fast-increasing data. However, there are some assertions that the power of analysis and prediction of Artificial Intelligence would intensify the power of Big Data analysis. This paper studies on business cases to try to graft the Artificial Intelligence technique onto Big Data analysis. We first research on various techniques of Artificial Intelligence and relations between Artificial Intelligence and Big Data. And then, we perform case studies of Big Data with using Artificial Intelligence and propose some roles of Big Data in the future.

• The Journal of Bigdata
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• v.7 no.2
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• pp.139-152
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• 2022

Engineering's work area for plants is a technical area that directly affects productivity, performance, and quality throughout the lifecycle from planning, design, construction, operation and disposal. Using the different types of data that occur to make decisions is important not only in the subsequent process but also in terms of cyclical cost reduction. However, there is a lack of systems to manage and analyze these integrated data. In this paper, we developed a knowledge base-based plant engineering analysis platform that can manage and utilize data. The platform provides a knowledge base that preprocesses previously collected engineering data, and provides analysis and visualization to use it as reference data in AI models. Users can perform data analysis through the use of prior technology and accumulated knowledge through the platform and use visualization in decision-support and systematically manage construction that relied only on experience.