• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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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.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.23 no.4
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• pp.6-11
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• 2015

In this study, we surveyed analysis of ADS-B ground trajectory data using non-aviation approval public data. For analysis used non-aviation public data and commercial ADS-B receiver. The study result is available using ADS-B ground trajectory data for airfield surveillance on limited range. Also, to confirmed of available using non-aviation public data for aviation research.

• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.305-313
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• 2018

Public open data is a database or electronic file produced by a public agency or government. The government is opening public data through the open data portals and individual agency websites. However, it is a reality that there is a limit to search and utilize desired public data from the perspective of data users. In particular, it takes a great deal of effort and time to understand the characteristics of data sets and to combine different data sets. This study suggests the possibility of interlinking between data sets by analyzing the common relationship of item names held by public data. The data sets are collected from the open data portal, and item names included in the data sets are extracted. The extracted item names consist of formal context and formal concept through formal concept analysis. The format concept has a list of data sets and a set of item name as extent and intent, respectively, and analyzes the common items of intent end to determine the possibility of data connection. The results derived from the formal concept analysis can be effectively applied to the semantic connection of the public data, and can be applied to data standard and quality improvement for public data release.

• Journal of KIBIM
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• v.13 no.3
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• pp.39-47
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• 2023

This study aims to introduce a model for enhancing community well-being through the utilization of public open data. To objectively assess abstract notions of residential satisfaction, text data from complaints is analyzed. By leveraging accessible public data, costs related to data collection are minimized. Initially, relevant text data containing civic complaints is collected and refined by removing extraneous information. This processed data is then combined with meaningful datasets and subjected to topic modeling, a text mining technique. The insights derived are visualized using Geographic Information System (GIS) and Application Programming Interface (API) data. The efficacy of this analytical model was demonstrated in the Godeok/Gangil area. The proposed methodology allows for comprehensive analysis across time, space, and categories. This flexible approach involves incorporating specific public open data as needed, all within the overarching framework.

• Asian Journal for Public Opinion Research
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• v.5 no.4
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• pp.285-301
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• 2018

There have been many studies that applied a data-driven analysis method to social media data, and some have even argued that this method can replace traditional polls. However, some other studies show contradictory results. There seems to be no consensus as to the methodology of data collection and analysis. But as social media-based election research continues and the data collection and analysis methodology keep developing, we need to review the key points of the controversy and to identify ways to go forward. Although some previous studies have reviewed the strengths and weaknesses of the social media-based election studies, they focused on predictive performance and did not adequately address other studies that utilized social media to address other issues related with public opinion during elections, such as public agenda or information diffusion. This paper tries to find out what information we can get by utilizing social media data and what limitations social media data has. Also, we review the various attempts to overcome these limitations. Finally, we suggest how we can best utilize social media data in understanding public opinion during elections.

• Health Policy and Management
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• v.23 no.1
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• pp.78-89
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• 2013

This study empirically analyze efficiency and productivity changes of public hospitals of Korea using data envelopment analysis/Window model and global Malmquist indices model. We use the ten-year data from 2001 to 2010 of 30 regional public hospitals listed database from the Association of Korean Regional Public Hospitals. The main focuses are to reveal whether the technical inefficiency are improved as time goes by, and efficiency and productivity are affected by environmental factors. The results can be summarized as follows. First, the efficiencies of public hospitals rise in trend as time passes. Second, regional public hospitals show the different average efficiencies according to their regional type, hospital type, operational type, medicaid type, and demand and supply conditions by Mann-Whitney U-tests. Third, technical efficiency changes mainly contribute to 4.4% annual average growth rate of productivity of regional public hospitals during that period. Our findings have some policy implications. It is confirmed that there exist some environmental inefficiencies, and those inefficiencies can not be overcome through just improving the inner management system. Thus, policy and institutional changes are necessary for regional public hospitals to improve efficiency and productivity overall.

• The Journal of Bigdata
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• v.8 no.1
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• pp.35-47
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• 2023

It is crucial to develop effective and efficient big data analytics methods for problem-solving in the field of business in order to improve the performance of data analytics and reduce costs and risks in the analysis of customer data. In this study, a big data-driven data analysis system using artificial intelligence techniques is designed to increase the accuracy of big data analytics along with the rapid growth of the field of data science. We present a key direction for big data analysis systems through missing value imputation, outlier detection, feature extraction, utilization of explainable artificial intelligence techniques, and exploratory data analysis. Our objective is not only to develop big data analysis techniques with complex structures of business data but also to bridge the gap between the theoretical ideas in artificial intelligence methods and the analysis of real-world data in the field of business.

• Nuclear Engineering and Technology
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• v.49 no.4
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• pp.850-854
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• 2017

Public acceptance of nuclear power is important for the government, the major stakeholder of the industry, because consensus is required to drive actions. It is therefore no coincidence that the governments of nations operating nuclear reactors are endeavoring to enhance public acceptance of nuclear power, as better acceptance allows stable power generation and peaceful processing of nuclear wastes produced from nuclear reactors. Past research, however, has been limited to epistemological measurements using methods such as the Likert scale. In this research, we propose big data analysis as an attractive alternative and attempt to identify the attitudes of the public on nuclear power. Specifically, we used common big data analyses to analyze consumer opinions via SNS (Social Networking Services), using keyword analysis and opinion analysis. The keyword analysis identified the attitudes of the public toward nuclear power. The public felt positive toward nuclear power when Korea successfully exported nuclear reactors to the United Arab Emirates. With the Fukushima accident in 2011 and certain supplier scandals in 2012, however, the image of nuclear power was degraded and the negative image continues. It is recommended that the government focus on developing useful businesses and use cases of nuclear power in order to improve public acceptance.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.305-316
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• 2020

This paper aims to examine the application of cost-volume-profit (CVP) analysis by public universities in Vietnam. In the context where Vietnam is gradually transferring financial autonomy to public universities, the conduct of a CVP analysis in relation to these public universities is particularly urgent. Research samples were collected in 2018 and 2019 by surveying Vietnamese public universities. After collection, the data is synthesized by excel file, conformity check, data cleansing and data analysis on SPSS software by tools such as Frequency statistics, price statistics, and means. The results show that: (1) universities used the CVP analysis in decision-making, (2) information related to the CVP analysis used for decision-making by administrators remained simplistic and lacked cost-control details, and (3) the application of the CVP analysis by university administrators for decision-making was neither comprehensive nor coordinated. The findings also show that, given the current conditions in Vietnam, increasing the governance in public universities is essential, as is contributing to reducing costs, increasing universities'income, providing the best service to students, and improving the quality of training. The study calls for the flexible application of the CVP analysis, which will provide information to help managers at Vietnamese public universities make the best decisions.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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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.