• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.731-734
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• 2021

본 논문에서는 에너지 데이터 플랫폼에서의 데이터 거래 가이드라인을 연구 및 제안한다. 타 산업에서의 데이터 플랫폼들의 현황과 에너지 데이터 플랫폼 구조를 분석하여 에너지 데이터 거래에서의 이해관계자를 정의한다. 또한, 타 산업에서의 데이터 거래가이드를 분석하여 에너지 거래 가이드 구성요소와 거래 계약 거래 원칙을 정의한다. 에너지 데이터 거래 가이드의 구성 요소로는 데이터 거래 이해관계자 정의, 데이터 거래 유형, 데이터 거래 유형별 거래 계약 원칙, 데이터 구매 및 판매비용 산정 방안, 데이터 플랫폼 이용료 산정 방안, 데이터 거래시 법적 쟁점으로 구성된다. 본 연구에서는 데이터 거래 이해관계자 정의, 데이터 거래 유형, 데이터 거래 유형별 거래 계약 원칙에 대해 정의하였다.

• Proceedings of the Korea Technology Innovation Society Conference
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• 2017.05a
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• pp.69-69
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• 2017

${\Box}$ 연구목적: 4차 산업혁명의 주요 이슈 분석 ${\bullet}$ 4차 산업혁명시대에 인공지능, 자율주행, 무인운송, 3D 프린터, 스마트팩토리..등 다양한 이슈가 등장 ${\bullet}$ 어떠한 이슈들이 있는지 분석하고자 함 ${\Box}$ 연구방법론: 빅데이터 분석기법 중에서 토픽 모델링을 활용 ${\Box}$ 연구데이터: 2013년1월부터 2017년3월까지 4차 산업혁명 관련 신문 기사 활용.

• Review of KIISC
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• v.26 no.2
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• pp.31-34
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• 2016

본고에서는 개인정보보호를 위하여 제정된 여러 관련 법률과 제도로 인하여 활성화 되지 못하는 빅데이터 산업의 현황을 금융산업을 통해 구체적으로 검토하고 있다. 그리고 현 법제 하에서 제공 가능한 데이터의 항목과 수준에 대해 제시하고자 한다. 또한 정보보호를 위한 비식별화방법과 유의해야 할 실무관점의 사항들에 대해 제언하고 있다.

• Informatization Policy
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• v.28 no.3
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• pp.49-72
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• 2021

This study analyzes the major content, significances, and future outlook of Three Data Acts amendment enacted in August 2020 in South Korea, with the focus on their impact on the financial and data industries. It seems that the revision of the Credit Information Act will enable the specification of a business which had previously only been regulated as the business of credit inquiry, and also enable the domestic data industry to activate the MyData industry, data trading and platforms, and specify data pseudonymization and trading procedures. For the rational and efficient implementation of the amendments to the Three Data Acts, the Personal Information Protection Committee must be as transparent and lawful in its activities as possible, and fairness must be guaranteed. Even in the utilization of personal information, the development or complementation of the related data processing technologies is essential, and clear data processing methods and areas must be regulated. Furthermore, the amendments must be supported with guarantees and the systematization of a fair competitive system in the data market, stricter regulations on penalties for illegal acts related to data, establishment and strengthening of the related security systems, and reinforcement of the system of cooperation for data transfer.

• Journal of Digital Convergence
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• v.18 no.6
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• pp.17-32
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• 2020

This study analyzes the regulatory issues of stakeholders, the firm, the government, and the individual, in the data industry using the principal-agent theory. While the importance of data driven economy is increasing rapidly, policy regulations and restrictions to use data impede the growth of data industry. We applied descriptive case analysis methodology using principal-agent theory. From our analysis, we found several meaningful results. First, key policy actors in data industry are data firms and the government among stakeholders. Second, two major concerns are that firms frequently invade personal privacy and the global companies obtain monopolistic power in data industry. This paper finally suggests policy and strategy in response to regulatory issues. The government should activate the domestic agent system for the supervision of global companies and increase data protection. Companies need to address discriminatory regulatory environments and expand legal data usage standards. Finally, individuals must embody an active behavior of consent.

• The Journal of Bigdata
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• v.7 no.1
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• pp.125-137
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• 2022

The transportation industry is one of the important industries due to the geographical requirements surrounded by the sea on three sides of Korea and the problem of resource poverty, which relies on imports for most of its resource consumption. Among them, the proportion of the shipping industry is large enough to account for most of the transportation industry, and maintenance in the shipping industry is also important in improving the operational efficiency and reducing costs of ships. However, currently, inspections are conducted every certain period of time for maintenance of ships, resulting in time and cost, and the cause is not properly identified. Therefore, in this study, the proposed methodology, LSTM-AutoEncoder, is used to detect abnormalities that may cause ship failure by considering the time of actual ship operation data. In addition, clustering is performed through clustering, and the potential causes of ship main engine failure are identified by grouping outlier by factor. This enables faster monitoring of various information on the ship and identifies the degree of abnormality. In addition, the current ship's fault monitoring system will be equipped with a concrete alarm point setting and a fault diagnosis system, and it will be able to help find the maintenance time.

• Convergence Security Journal
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• v.22 no.4
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• pp.85-97
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• 2022

With the development of big data technology, data is rapidly entering a hyperconnected intelligent society that accelerates innovative growth in all industries. The convergence industry, which holds and utilizes various high-quality data, is becoming a new growth engine, and big data is fused to various traditional industries. In particular, in the medical field, structured data such as electronic medical record data and unstructured medical data such as CT and MRI are used together to increase the accuracy of disease prediction and diagnosis. Currently, the importance and size of unstructured data are increasing day by day in the medical industry, but conventional data security technologies and policies are structured data-oriented, and considerations for the security and utilization of unstructured data are insufficient. In order for medical treatment using big data to be activated in the future, data diversity and security must be internalized and organically linked at the stage of data construction, distribution, and utilization. In this paper, the current status of domestic and foreign data security systems and technologies is analyzed. After that, it is proposed to add unstructured data-centered de-identification technology to the guidelines for unstructured data and technology application cases in the industry so that unstructured data can be actively used in the medical field, and to establish standards for judging personal information for unstructured data. Furthermore, an object feature-based identification ID that can be used for unstructured data without infringing on personal information is proposed.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.149-156
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• 2020

In the Fourth Industrial Revolution, successful cases using big data in various industries are reported. This paper examines cases that successfully use big data in the medical industry to develop the service and draws implications in value that big data create. The related work introduces big data technology in the medical field and cases of eight innovative service in the big data service are explained. In the introduction, the overall structure of the study is mentioned by describing the background and direction of this study. In the literature study, we explain the definition and concept of big data, and the use of big data in the medical industry. Next, this study describes the several cases, such as technologies using national health information and personal genetic information for the study of diseases, personal health services using personal biometric information, use of medical data for efficiency of business processes, and medical big data for the development of new medicines. In the conclusion, we intend to provide direction for the academic and business implications of this study, as well as how the results of the study can help the domestic medical industry.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.1-9
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• 2014

This research describes strategies to promote the growth of the Big Data industry and the companies within the ecosystem. In doing so, we identify the roles and responsibilities of various objects of this ecosystem and Big Data concepts. We describe the five components of the Big Data ecosystem: governance, data holders, service users, service providers and infrastructure providers. Related to the Big Data industry, the paper discusses 13 business strategies between the five components in the ecosystem. These strategies directly respond to areas of research by the Big Data industry leading experts on its early development. These strategies focus on how companies can gain competitive advantages in a growing new business environment of Big Data. The strategy topics are as follows: 1) the government's long term policy, 2) building Big Data support centers, 3) policy support and improving the legal system, 4) improving the Privacy Act, 5) increasing the understanding of Big Data, 6) Big Data support excavation projects, 7) professional manpower education, 8) infrastructure system support, 9) data distribution and leverage support, 10) data quality management, 11) business support services development, 12) technology research and excavation, 13) strengthening the foundation of Big Data technology. Of the proposed strategies, establishing supportive government policies is essential to the successful growth of thee Big Data industry. This study fosters a better understanding of the Big Data ecosystem and its potential to increases the competitive advantage of companies.

• Annual Conference of KIPS
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• 2013.11a
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• pp.1199-1202
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• 2013

최근 증권, 센서, 기후, 의료 분야 등에서 수많은 시계열 데이터들이 쏟아져 나오고 있고, 이러한 시계열 빅 데이터를 통해 의미를 찾아내고자 하는 시계열 해석 및 분석, 예측 작업의 수요가 증가하고 있다. 시계열 해석 및 분석, 예측 작업을 하기 위해서 사용 될 수 있는 기초 작업은 유사한 시계열 시퀀스를 찾아내는 유사 시퀀스 매칭과 이러한 매칭을 통해 특정 시계열 데이터의 하나의 특징이 되는 빈발 시퀀스 추출 기술이 필요하다. 본 논문에서는 이러한 시계열 빅 데이터에서 유사 시퀀스 매칭을 이용한 빈발 시퀀스 추출 문제를 효율적으로 해결하는 빈발 시퀀스 추출기(Frequent Sequence Extractor)를 개발 및 구현하였다. 또한 분산처리 플랫폼인 하둡을 이용한 데이터 파싱을 사용하여, 각 분야별 시계열 데이터를 분석하는 전문가에게 효율적인 분산처리 효과를 제공한다.