• Title/Summary/Keyword: 빅데이터 거버넌스

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Big Data Governance Model for Smart Water Management (스마트 물관리를 위한 빅데이터 거버넌스 모델)

  • Choi, Young-Hwan;Cho, Wan-Sup;Lee, Kyung-Hee
    • The Journal of Bigdata
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    • v.3 no.2
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    • pp.1-10
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    • 2018
  • In the field of smart water management, there is an increasing demand for strengthening competitiveness through big data analysis. As a result, systematic management (Governance) of big data is becoming an important issue. Big data governance is a systematic approach to evaluating, directing and monitoring data management, such as data quality assurance, privacy protection, data lifetime management, data ownership and clarification of management rights. Failure to establish big data governance can lead to serious problems by using low quality data for critical decisions. In addition, personal privacy data can make Big Brother worry come true, and IT costs can skyrocket due to the neglect of data age management. Even if these technical problems are fixed, the big data effects will not be sustained unless there are organizations and personnel who are dedicated and responsible for data-related issues. In this paper, we propose a method of building data governance for smart water data management based on big data.

The Study on Data Governance Research Trends Based on Text Mining: Based on the publication of Korean academic journals from 2009 to 2021 (텍스트 마이닝을 활용한 데이터 거버넌스 연구 동향 분석: 2009년~2021년 국내 학술지 논문을 중심으로)

  • Jeong, Sun-Kyeong
    • Journal of Digital Convergence
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    • v.20 no.4
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    • pp.133-145
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    • 2022
  • As a result of the study, the poorest keywords were information, big data, management, policy, government, law, and smart. In addition, as a result of network analysis, related research was being conducted on topics such as data industry policy, data governance performance, defense, governance, and data public. The four topics derived through topic modeling were "DG policy," "DG platform," "DG in laws," and "DG implementation," of which research related to "DG platform" showed an increasing trend, and "DG implementation" tended to shrink. This study comprehensively summarized data governance-related studies. Data governance needs to expand research areas from various perspectives and related fields such as data management and data integration policies at the organizational level, and related technologies. In the future, we can expand the analysis targets for overseas data governance and expect follow-up studies on research directions and policy directions in industries that require data-based future industries such as Industry 4.0, artificial intelligence, and Metaverse.

Big Data Governance Model for Effective Operation in Cyberspace (효과적인 사이버공간 작전수행을 위한 빅데이터 거버넌스 모델)

  • Jang, Won-gu;Lee, Kyung-ho
    • The Journal of Bigdata
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    • v.4 no.1
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    • pp.39-51
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    • 2019
  • With the advent of the fourth industrial revolution characterized by hyperconnectivity and superintelligence and the emerging cyber physical systems, enormous volumes of data are being generated in the cyberspace every day ranging from the records about human life and activities to the communication records of computers, information and communication devices, and the Internet of things. Big data represented by 3Vs (volume, velocity, and variety) are actively used in the defence field as well. This paper proposes a big data governance model to support effective military operations in the cyberspace. Cyberspace operation missions and big data types that can be collected in the cyberspace are classified and integrated with big data governance issues to build a big data governance framework model. Then the effectiveness of the constructed model is verified through examples. The result of this study will be able to assist big data utilization planning in the defence sector.

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A Study on the Data Collection and Storage of Big Data Systems (빅데이터 시스템의 데이터 수집 및 저장에 관한 연구)

  • Park, Jihun;Kim, Gyunghwan;Jung, Eunsu
    • Proceedings of the Korea Information Processing Society Conference
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    • 2017.11a
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    • pp.48-51
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    • 2017
  • 빅데이터는 저장되지 않았거나 저장되더라도 분석되지 못하고 버리게 되는 방대한 양의 데이터를 말한다. 실제로도 빅데이터는 페이스북, 트위터등의 소셜 네트워크에서 많이 발생하고 있는데, 이러한 방대한 데이터들을 어떻게 효율적으로 저장하고 분석하는지에 대한 관심이 많아지고 있다. 따라서 본 논문에서는 빅데이터의 개념, 빅데이터의 향후 동향과 이슈들에 대해 살펴보고, 빅데이터 시스템이 데이터를 수집하고 저장하는 것에 대한 고려할만한 사항들과 효율적인 해결방안에 대해 제시하였다.

The Effect of Data 3 on the Utilization of Medical Big Data for Early Detection of Dementia (데이터 3법이 치매 조기 예측을 위한 의료 빅데이터 활용에 미치는 영향 연구)

  • Kim, Hyejin
    • Journal of Digital Convergence
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    • v.18 no.5
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    • pp.305-315
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    • 2020
  • As the incidence and prevalence of dementia increases with our aging population, so does the social burden on our society, which calls for a special emphasis on need for early diagnosis. Thus, efforts are made to prevent dementia and early detection but with current diagnostic measures, these efforts appear futile. As a solution, it is crucial to integrate and standardize healthcare big data and analysis of each index. In order to increase use of large database, the Korea National Assembly passed the Data 3 Act focusing on open-access and sharing of database, but a follow-up legislation is needed a for safer utilization. In this study, we have identified number of foreign of foreign policies through review of prior researches on the topic leading to specific enforcement ordinances tailored to the Data 3 Act for safe access and utilization of database. We also aimed to establish secure process of data collection and disposal as well as governance at the national level to ensure safe utilization of healthcare big data.

A study on the Effect of Big Data Quality on Corporate Management Performance (빅데이터 품질이 기업의 경영성과에 미치는 영향에 관한 연구)

  • Lee, Choong-Hyong;Kim, YoungJun
    • Journal of the Korea Convergence Society
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    • v.12 no.8
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    • pp.245-256
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    • 2021
  • The Fourth Industrial Revolution brought the quantitative value of data across the industry and entered the era of 'Big Data'. This is due to both the rapid development of information & communication technology and the diversity & complexity of customer purchasing tendencies. An enterprise's core competence in the Big Data Era is to analyze and utilize the data to make strategic decisions for enterprise. However, most of traditional studies on Big Data have focused on technical issues and future potential values. In addition, these studies lacked interest in managing the quality and utilization levels of internal & external customer Big Data held by the entity. To overcome these shortages, this study attempted to derive influential factors by recognizing the quality management information systems and quality management of the internal & external Big Data. First of all, we conducted a survey of 204 executives & employees to determine whether Big Data quality management, Big Data utilization, and level management have a significant impact on corporate work efficiency & corporate management performance. For the study for this purpose, hypotheses were established, and their verifications were carried out. As a result of these studies, we found that the reasons that significantly affect corporate management performance are support from the management class, individual innovation, changes in the management environment, Big Data quality utilization metrics, and Big Data governance system.

A Study on the Policy Trends for the Revitalization of Medical Big Data Industry (의료 빅데이터 산업 활성화를 위한 정책 동향 고찰)

  • Kim, Hyejin;Yi, Myongho
    • Journal of Digital Convergence
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    • v.18 no.4
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    • pp.325-340
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    • 2020
  • Today's rapidly developing health technology is accumulating vast amounts of data through medical devices based on the Internet of Things in addition to data generated in hospitals. The collected data is a raw material that can create a variety of values, but our society lacks legal and institutional mechanisms to support medical Big Data. Therefore, in this study, we looked at four major factors that hinder the use of medical Big Data to find ways to enhance use of the Big Data based healthcare industry, and also derived implications for expanding domestic medical Big Data by identifying foreign policies and technological trends. As a result of the study, it was concluded that it is necessary to improve the regulatory system that satisfies the security and usability of healthcare Big Data as well as establish Big Data governance. For this, it is proposed to refer to the Big Data De-identification Guidelines adopted by the United States and the United Kingdom to reorganize the regulatory system. In the future, it is expected that it will be necessary to have a study that has measures of the conclusions and implications of this study and to supplement the institutional needs to play a positive role in the use of medical Big Data.

A study on data management policy direction for disaster safety management governance (재난안전관리 거버넌스 구축을 위한 데이터관리정책 방향에 관한 소고)

  • Kim, Young Mi
    • Journal of Digital Convergence
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    • v.17 no.12
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    • pp.83-90
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    • 2019
  • In addition to the proliferation of intelligent information technology, the field of disaster management is being approached from a multifaceted perspective. In particular, as the interest in establishing a disaster safety management system using data increases, there is an increasing need for a large amount of big data distribution generated in real time and a systematic management. Furthermore, efforts are being made to improve the quality of data in order to increase the prevention effect of disasters through data analysis and to make a system that can respond effectively and to predict the overall situation caused by the disasters. Disaster management should seek both precautionary measures and quick responses in the event of a disaster as well as a technical approach to establishing governance and safety. This study explores the policy implications of the significance and structure of disaster safety management governance using data.

A Study on the Public Interest of Collected Information (수집된 정보의 공익성에 관한 고찰)

  • Park, Kook-Heum
    • Informatization Policy
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    • v.26 no.1
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    • pp.25-45
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    • 2019
  • With the advent of the data economy, interest in using big data has increased, but conflicts with protecting personal information have been also steadily raised. In this regard, major countries are accelerating use of big data by exempting de-identified, pseudonymous personal information from protection. However, these policies have been made without the understanding that the economic value of personal information has been actually changing slowly. This paper presents the concept of 'collected information' and defines it as having public interest and therefore, not the exclusive property of the collector of such information. The paper shows the collected information has public interest in terms of personal information protection, connectivity, and universal service and public goods. It also specifies that the 'data governance' cannot be applied to the current data utilization framework that depends upon the holder's consent; rather, it raises the need to improve the practices of information provision consent or provide the beneficiary right of information use to the information holder in order to ensure the proper 'data governance' that will turn market failure into success.

Big Data Activation Plan for Digital Transformation of Agriculture and Rural (농업·농촌 디지털 전환을 위한 빅데이터 활성화 방안 연구)

  • Lee, Won Suk;Son, Kyungja;Jun, Daeho;Shin, Yongtae
    • KIPS Transactions on Software and Data Engineering
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    • v.9 no.8
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    • pp.235-242
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    • 2020
  • In order to promote digital transformation of our agricultural and rural communities in the wake of the fourth industrial revolution and prepare for the upcoming artificial intelligence era, it is necessary to establish a system and system that can collect, analyze and utilize necessary quality data. To this end, we will investigate and analyze problems and issues felt by various stakeholders such as farmers and agricultural officials, and present strategic measures to revitalize big data, which must be decided in order to promote digital transformation of our agricultural and rural communities, such as expanding big data platforms for joint utilization, establishing sustainable big data governance, and revitalizing the foundation for big data utilization based on demand.