• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.64-69
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• 2022

Commercialization of public technology is the transfer of government-led scientific and technological innovation and R&D results to the private sector, and is recognized as a key achievement driving economic growth. Therefore, in order to activate technology transfer, various machine learning methods are being studied to identify success factors or to match public technology with high commercialization potential and demanding companies. However, public technology commercialization data is in the form of a table and has a problem that machine learning performance is not high because it is in an imbalanced state with a large difference in success-failure ratio. In this paper, we present a method of utilizing CTGAN to resolve imbalances in public technology data in tabular form. In addition, to verify the effectiveness of the proposed method, a comparative experiment with SMOTE, a statistical approach, was performed using actual public technology commercialization data. In many experimental cases, it was confirmed that CTGAN reliably predicts public technology commercialization success cases.

• Journal of Preventive Medicine and Public Health
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• v.48 no.4
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• pp.203-215
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• 2015

Objectives: This study was performed to investigate the relationship between the incidence of national notifiable infectious diseases (NNIDs) and meteorological factors, air pollution levels, and hospital resources in Korea. Methods: We collected and stored 660 000 pieces of publicly available data associated with infectious diseases from public data portals and the Diseases Web Statistics System of Korea. We analyzed correlations between the monthly incidence of these diseases and monthly average temperatures and monthly average relative humidity, as well as vaccination rates, number of hospitals, and number of hospital beds by district in Seoul. Results: Of the 34 NNIDs, malaria showed the most significant correlation with temperature (r=0.949, p<0.01) and concentration of nitrogen dioxide (r=-0.884, p<0.01). We also found a strong correlation between the incidence of NNIDs and the number of hospital beds in 25 districts in Seoul (r=0.606, p<0.01). In particular, Geumcheon-gu was found to have the lowest incidence rate of NNIDs and the highest number of hospital beds per patient. Conclusions: In this study, we conducted a correlational analysis of public data from Korean government portals that can be used as parameters to forecast the spread of outbreaks.

• Journal of Internet Computing and Services
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• v.16 no.4
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• pp.131-139
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• 2015

With the request for the advent of new engine toward economic growth, the issue regarding public-owned data disclosure has been increasing. The Korean governments are forced to open public-owned data and to utilize them in solving the various social problems and in promoting the welfare for the people. In contrast, due to the distrust of the effectiveness for the policy, many public owned organizations hesitate to open the public-owned data. However, in spite of communication gap between the government and public organizations, Ministry of Government Administration and National Information Society Agency recently planned to accelerate the information disclosure. The study aims to analyze the perception of the public organization for public data utilization and to provide proper recommendations. This research identified mutual weights that the organization recognize in opening and sharing the public data, based on benefit, opportunity, cost, and risk. ANP decision making tool and BOCR model were applied to the analyses. The results show that there are significant differences in perceiving risk and opportunity elements between the government and public organizations. Finally, the study proposed the ideal alternatives based on four elements. The study will hopefully provide the guideline to the public organizations, and assist the related authorities with the information disclosure policy in coming up with the relevant regulations.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.10
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• pp.478-485
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• 2019

This is an exploratory study to examine factors affecting South Korean companies' intentions to use big data technology and services based on whether the companies use public data or not. This study, using R, conducted chi-squared tests and logistic regression analysis. As a result of the logistic regression analysis, cost reduction had a positive effect on the big data-use intentions in companies that use public data, whereas with companies that do not use public data, customer satisfaction had a positive impact, and support for decision-making had a negative impact on the intention to use big data. Recently, the South Korean government has focused on improving the utilization of public data and big data. However, as a result of this study, the use of public data and big data in South Korea is still insufficient. Yet, considering that the data utilized for this study was created in 2017, additional study using public data and big data is also required.

• Journal of Korean Society of Archives and Records Management
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• v.21 no.4
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• pp.1-18
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• 2021

Today, record management has become more important in management as records generated from administrative work and data production have increased significantly, and the development of information and communication technology, the working environment, and the size and various functions of the government have expanded. It is explained as an example in connection with the concept of public records with the characteristics of big data and big data characteristics. Social, Technological, Economical, Environmental and Political (STEEP) analysis was conducted to examine such areas according to the big data generation environment. The appropriateness and necessity of applying big data technology in the field of public record management were identified, and the top priority applicable framework for public record management work was schematized, and business implications were presented. First, a new organization, additional research, and attempts are needed to apply big data analysis technology to public record management procedures and standards and to record management experts. Second, it is necessary to train record management specialists with "big data analysis qualifications" related to integrated thinking so that unstructured and hidden patterns can be found in a large amount of data. Third, after self-learning by combining big data technology and artificial intelligence in the field of public records, the context should be analyzed, and the social phenomena and environment of public institutions should be analyzed and predicted.

• ETRI Journal
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• v.44 no.2
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• pp.300-312
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• 2022

Recently, public safety services have attracted significant attention for their ability to protect people from crimes. Rapid detection of dangerous situations (that is, abnormal situations where someone may be harmed or killed) is required in public safety services to reduce the time required to respond to such situations. This study proposes a novel danger detection technology based on multimodal data, which includes data from multiple sensors (for example, accelerometer, gyroscope, heart rate, air pressure, and global positioning system sensors), and multilog data, which includes contextual logs of humans and places (for example, contextual logs of human activities and crime-ridden districts) over time. To recognize human activity (for example, walk, sit, and punch), the proposed technology uses multimodal data analysis with an attitude heading reference system and long short-term memory. The proposed technology also includes multilog data analysis for detecting whether recognized activities of humans are dangerous. The proposed danger detection technology will benefit public safety services by improving danger detection capabilities.

• Journal of Korean Library and Information Science Society
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• v.54 no.3
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• pp.105-128
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• 2023

In 2020, the Busan metropolitan library took the lead in establishing an integrated library system (ILS) that integrates bibliographic data from 49 public libraries and 103 small public libraries. However, each library still builds bibliographic data individually and repeatedly, and the bibliographic data built by each library is only physically stored in an integrated DB. Therefore the improvement in work efficiency or data quality has not been achieved. This study aimed to analyze the construction processes and quality of bibliographic data in Busan public libraries and to suggest a new implementation strategy for an integrated environment. To this end, (1) the construction process of bibliographic data was investigated, (2) the quality of the constructed bibliographic data was objectively analyzed, and (3) four implementation strategies were suggested based on critical problems. The implementation strategy aims not only to improve the quality of bibliographic data, but also to increase work efficiency and build an infrastructure for data sharing.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.21-27
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• 2022

As society develops and becomes more complex, new and diverse types of disasters such as fine dust and infectious diseases are occurring. However, in the past, there was no PA(Public Address) system that provided accurate information to prepare for such a disaster. In this paper, we propose a public address system that automatically broadcasts an alarm by analyzing polluted air quality data collected from public data and IoT sensors. The warning level varies depending on the air quality, and the information provided by public data may show a significantly different result from the guide area due to various factors such as the distance from the measuring station or the wind direction. To compensate for this, we are going to propose a method for broadcasting by comparing and analyzing data obtained from public data and data from on-site IoT sensors.

• Journal of the Korean Society for information Management
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• v.33 no.1
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• pp.293-316
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• 2016

This study aims to improve bibliographic data of public libraries by linking service data, which are produced out of library service programs. Service data collected from the seven award-winning public libraries were selected and analyzed. A Bibliographic Framework is used for linking bibliographic data and service data. Interfaces are also suggested for the two-way data linking. The results can be used to obtain 1) selective and value-added bibliographic data, 2) bibliographic data updated continuously throughout the lifecycle, 3) structured service data for preservation and sharing, and 4) bibliographic data linked to the additional external linked data.