• The Journal of Asian Finance, Economics and Business
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• 제8권11호
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• pp.87-96
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• 2021

Data-driven markets depend on access to data as a resource for products and services. Since the quality of information that can be drawn from data increases with the available amount and quality of the data, businesses involved in the data economy have a great interest in accessing data from other market players and sharing data with other stakeholders. Despite the growing need for access to data and evidence of the economic and social benefits, data access and sharing remains below its potential. Individuals, businesses, and governments often face barriers to data access, which may be compounded by the reluctance to share, including within and across sectors. To address these challenges, this paper focuses on finding possible solutions for a better data-sharing economy. This paper 1) Discusses opportunities and challenges of open data and the data-sharing economy, limitations of private sector data, and issues with open government data. 2) Introduces open government data initiatives and open governance networks initiatives. 3) Suggests possible solutions, including the governance and management, the legal and policy frameworks, and the technical standards for open data with proposing an open data governance model for the data-sharing economy.

• 디지털산업정보학회논문지
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• 제18권4호
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• pp.97-110
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• 2022

The purpose of this study is to analyze the level of public data convergence capabilities of administrative organizations and to explore important variables in data-based organizational capabilities. The theoretical background was summarized on public data and use activation, joint use, convergence, administrative organization, and convergence constraints. These contents were explained Public Data Act, the Electronic Government Act, and the Data-Based Administrative Act. The research model was set as the data-based organizational capabilities effect by a data-based administrative capability, public data operation capabilities, and public data operation constraints. It was also set whether there is a capabilities difference data-based on an organizational operation by the level of data convergence capabilities. This study analysis was conducted with hierarchical cluster analysis and multiple regression analysis. As the research result, First, hierarchical cluster analysis was classified into three groups. It was classified into a group that uses only public data and structured data, a group that uses public data on both structured and unstructured data, and a group that uses both public and private data. Second, the critical variables of data-based organizational operation capabilities were found in the data-based administrative planning and administrative technology, the supervisory organizations and technical systems by public data convergence, and the data sharing and market transaction constraints. Finally, the essential independent variables on data-based organizational competencies differ by group. This study contributed. As a theoretical implication, this research is updated on management information systems by explaining the Public Data Act, the Electronic Government Act, and the Data-Based Administrative Act. As a practical implication, the activity reinforcement of public data should be promoting the establishment of data standardization and search convenience and elimination of the lukewarm attitudes and Selfishness behavior for data sharing.

• International Journal of Internet, Broadcasting and Communication
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• 제11권4호
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• pp.18-25
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• 2019

The public data portal provides various public data created by the government in the form of files and open APIs. In order to increase the usability of public open data, a variety of information should be provided to users and should be convenient to use for users. This requires the structured data design plan of the public data. In this paper, we propose a data design method to improve the usability of the Seoul subway public data. For the study, we first identify some properties of the current subway public data and then classify the data based on these properties. The properties used as classification criteria are stored properties, derived properties, static properties, and dynamic properties. We also analyze the limitations of current data for each property. Based on this analysis, we classify currently used subway public data into code entities, base entities, and history entities and present the improved design of entities according to this classification. In addition, we propose data retrieval functions to increase the utilization of the data. If the data is designed according to the proposed design of this paper, it will be possible to solve the problem of duplication and inconsistency of the data currently used and to implement more structural data. As a result, it can provide more functions for users, which is the basis for increasing usability of subway public data.

• 지능정보연구
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• 제22권4호
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• pp.109-122
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• 2016

최근 빅데이터 분석은 기업과 전문가뿐만 아니라 개인이나 비전문가들도 큰 관심을 갖는 분야로 발전하였다. 그에 따라 현재 공개된 데이터 또는 직접 수집한 이터를 분석하여 마케팅, 사회적 문제 해결 등에 활용되고 있다. 국내에서도 다양한 기업들과 개인이 빅데이터 분석에 도전하고 있지만 빅데이터 공개의 제한과 수집의 어려움으로 분석 초기 단계에서부터 어려움을 겪고 있다. 본 논문에서는 빅데이터 공유를 방해하는 개인정보, 빅트래픽 등의 요소들에 대한 기존 연구와 사례들을 살펴보고 정책기반의 해결책이 아닌 시스템을 통해서 빅데이터 공유 제한 문제를 해결 할 수 있는 클라이언트-서버 모델을 이용해 빅데이터를 공개 및 사용 할 때 발생하는 문제점들을 해소하고 공유와 분석 활성화를 도울 수 있는 방안에 대해 기술한다. 클라이언트-서버 모델은 SPARK를 활용해 빠른 분석과 사용자 요청을 처리하며 Server Agent와 Client Agent로 구분해 데이터 제공자가 데이터를 공개할 때 서버 측의 프로세스와 데이터 사용자가 데이터를 사용하기 위한 클라이언트 측의 프로세스로 구분하여 설명한다. 특히, 빅데이터 공유, 분산 빅데이터 처리, 빅트래픽 문제에 초점을 맞추어 클라이언트-서버 모델의 세부 모듈을 구성하고 각 모듈의 설계 방법에 대해 제시하고자 한다. 클라이언트-서버 모델을 통해서 빅데이터 공유문제를 해결하고 자유로운 공유 환경을 구성하여 안전하게 빅데이터를 공개하고 쉽게 빅데이터를 찾는 이상적인 공유 서비스를 제공할 수 있다.

• 한국IT서비스학회지
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• 제21권3호
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• pp.27-42
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• 2022

Open Data is an essential resource for the data industry. 'Act On Promotion Of The Provision And Use Of Public Data', enacted on July 30, 2013, mandates public institutions to manage the quality of Open Data and provide it to the public. Via such a legislation, the legal basis for the public to Open Data is prepared. Furthermore, public institutions are prohibited from developing and providing open data services that are duplicated or similar to those of the private sector, and private start-ups using open data are supported. However, as the demand for Open Data gradually increases, the cases of refusal to provide or interruption of Open Data held by public institutions are also increasing. Accordingly, the 'Open Data Mediation Committee' is established and operated so that the right to use data can be rescued through a simple dispute mediation procedure rather than complicated administrative litigation. The main issues dealt with in dispute settlement so far are usually the rights of third parties, such as open data including personal information, private information such as trade secrets, and copyrights. Plus, non-open data cannot be provided without the consent of the information subject. Rather than processing non-open data into open data through de-identification processing, positive results can be expected if consent is provided through active rights processing of the personal information subject. Not only can the Public Mydata Service be used by the information subject, but Open Data applicants will also be able to secure higher quality Open Data, which will have a positive impact on fostering the private data industry. This study derives a plan to establish a rights processing platform to enhance the usability of Open Data, including private information such as personal information, trade secrets, and copyright, which have become an issue when providing Open Data since 2014. With that, the proposals in this study are expected to serve as a stepping stone to revitalize private start-ups through the use of wide Open Data and improve public convenience through Public MyData services of information subjects.

• 전기학회논문지
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• 제65권7호
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• pp.1277-1282
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• 2016

The term "Big Data" has been defined to encapsulate a broad spectrum of data sources and data formats. It is often described to be unstructured data due to its properties of variety in data formats. Even though the traditional methods of structuring data in rows and columns have been reinvented into column families, key-value or completely replaced with JSON documents in document-based databases, the fact still remains that data have to be reshaped to conform to certain structure in order to persistently store the data on disc. ETL processes are key in restructuring data. However, ETL processes incur additional processing overhead and also require that data sources are maintained in predefined formats. Consequently, data in certain formats are completely ignored because designing ETL processes to cater for all possible data formats is almost impossible. Potentially, these unconsidered data sources can provide useful insights when incorporated into big data analytics. In this project, using big data solution, Apache Spark, we tapped into other sources of data stored in their raw formats such as various text files, compressed files etc and incorporated the data with persistently stored enterprise data in MongoDB for overall data analytics using MongoDB Aggregation Framework and MapReduce. This significantly differs from the traditional ETL systems in the sense that it is compactible regardless of the data formats at source.

• 한국IT서비스학회지
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• 제21권1호
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• pp.145-161
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• 2022

The data is a key catalyst for the development of the fourth industry, and has been viewed as an essential element of the new industry, with technology convergence such as artificial intelligence, augmented/virtual reality, self-driving and 5 G. This will determine the price and value of the data as the user uses data in which the data is based on the context of the situation, rather than the data itself of the past supplier-centric data. This study began with, what factors will increase the value of data from a user perspective not a supplier perspective The study was limited to public data and users conducted research on users using data, such as analysis or development based on data. The study was designed to gauge the value of data that was not studied in the user's perspective, and was instrumental in raising the value of data in the jurisdiction of supplying and managing data.

• International Journal of Internet, Broadcasting and Communication
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• 제10권3호
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• pp.51-58
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• 2018

In order to utilize various data provided by Korea public open data portal, data should be systematically managed using a database. Since the range of open data is enormous, and the amount of data continues to increase, it is preferable to use a database capable of processing big data in order to analyze and utilize the data. This paper proposes data modeling and implementation method suitable for public data. The target data is subway related data provided by the public open data portal. Schema of the public data related to Seoul metro stations are analyzed and problems of the schema are presented. To solve these problems, this paper proposes a method to normalize and structure the subway data and model it in NoSQL database. In addition, the implementation result is shown by using MongDB which is a document-based database capable of processing big data.

• International Journal of Knowledge Content Development & Technology
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• 제14권3호
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• pp.115-140
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• 2024

With the advent of the Fourth Industrial Revolution and the era of big data, the ability to handle data has become essential. This has heightened the importance and necessity of data literacy competencies. The purpose of this study is to propose a framework for data literacy competencies. To achieve this goal, data literacy frameworks from eight countries and twelve pieces of literature on data literacy competencies were analyzed and synthesized, resulting in five categories and twenty-three competencies. The five categories are: data understanding and ethics, data collection and management, data analysis and evaluation, data utilization, and data governance and systems. It is hoped that the data literacy competency framework proposed in this study will serve as a foundational resource for policies, curricula, and the enhancement of individual data literacy competencies.

• ETRI Journal
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• 제28권2호
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• pp.191-204
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• 2006

Many previous studies of data quality have focused on the realization and evaluation of both data value quality and data service quality. These studies revealed that poor data value quality and poor data service quality were caused by poor data structure. In this study we focus on metadata management, namely, data structure quality and introduce the data quality management maturity model as a preferred maturity model. We empirically show that data quality improves as data management matures.