• The Korea Journal of Herbology
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• v.36 no.5
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• pp.1-14
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• 2021

Objectives : From January 2020, when the first confirmed case of COVID-19 in Korea, the use of health information using the Internet is expected to increase. It is expected that there will be a significant change in the general public's interest in Korean herbal medicines for health care. Therefore, in this study, we tried to confirm the change in the search trend of Korean herbal medicines after the COVID-19 epidemic. Methods : Using the "Naver DataLab (http://datalab.naver.com)" service of a Korean portal site Naver, search volume was investigated with 606 Korean herbal medicines as keywords. The search period was from January 2020, right after the onset of COVID-19, to June 2021. The search results were sorted by the peak search volume and the total search volume. Results : 'Cheonsangap (천산갑, 穿山甲, Manitis Squama)' was the most searched Korean herbal medicine in the peak search volume and total search volume with least bias. Conclusions : The problem of supply and demand of Korean herbal medicines of high public interest was identified. Broadcasting and media exposure were the factors that had a big impact on the search volume for Korean herbal medicines. As it was confirmed that the search volume for Korean herbal medicines increased rapidly due to media exposure, it is necessary to provide correct information about Korean herbal medicines, improve public awareness, and manage stable supply and demand based on continuous search trend monitoring.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2305-2311
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• 2017

In recent years, there have been tries to open public data from various government agencies along with publicization of public information for the public interest. In other words, various kinds of electronic data generated and collected by the public institutions as a result of their work are opened in the public portal sites. However, users who use it are limited in their use of big data due to lack of understanding of data format, lack of data processing knowledge, difficulty in accessing and managing data, and lack of visualization data to understand collected and stored data. Therefore, in this study, we propose a big data collection, storing and visualization platform that can collect big data provided by various public sites using data set URL and API regardless of data format, re-process collected data using XML structure.

• The Journal of the Korea Contents Association
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• v.12 no.9
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• pp.500-511
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• 2012

Recently, use frequency for traffic information which provides shortest paths and traffic condition is increasing. Specially, in the survey, it is shown that users prefer internet portal sites which can be used the most easily among traffic information media. But, there are not many verification systems for traffic information contents of internet portal sites which collect and provide information than traffic information contents which are provided by public service. The purpose of this study is to investigate real accuracy and accuracy felt by users about information provided by portal sites. Therefore, in this research we verified accuracy of information by portal site with real field data and investigate real usage about contents and experienced accuracy by users through survey. Also, users' expectation and satisfaction were surveyed and the contents to be improved were selected by using IPA technique. By the result of accuracy verification by field data using portable DSRC(Dedicated Short Range Communication) devices, it is shown that average error was 14~32% and sometimes very high rate. Also, it is shown that 28.3 % of total respondents prefers the information by portal sites and 50 % of total respondents felt that contents of traffic information by portal sites are not accurate. Real-time traffic condition was selected as the most inaccurate one among all contents of traffic information and it was analyzed that intensive efforts for improving information about real-time traffic condition are needed.

• International Journal of Knowledge Content Development & Technology
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• v.12 no.4
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• pp.41-65
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• 2022

This study would understand the overall perception of our society about public libraries, analyzing the texts related to public libraries, utilizing the semantic connection network & sentiment analysis. For this purpose, this study collected data from the last five years with keywords, 'Library' and 'Lifelong Learning Center' from January 1, 2016 through November 30, 2020 through the blogs and cafés of major domestic portal sites. With the collected data, text mining, centrality of keywords, network structure, structural equipotentiality, and sensitivity analyses were conducted. As a result of the analysis, First, 'reading' and 'book' were identified as representative keywords that form the social perception of public libraries. Second, it turned out that there were keywords related to the use of the library and the untact service due to the recent spread of COVID-19. Third, in seeking a plan for the development of public libraries through the keywords drawn to have positive meanings, it is necessary to create continuous services that can form a new image of the library, breaking away from the existing fixed role and image of the library and increase the convenience of use. Fourth, facilities and facilities for library services were recognized from a neutral point of view. Fifth, the spread of infectious diseases, social distancing, and temporary closure and closure of libraries are negatively related to public libraries, and awareness of librarians has been identified as negative keywords.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.4
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• pp.35-55
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• 2017

The Master Data is constructed for the wide use within the institution, and it is mainly used in the enterprise. In this research, we have conducted research for the purpose of building master data on infrastructure that can be used by public institutions in the country. To do this, we analyzed individual attributes of the standard data set provided by the public data portal. Among these, we extracted standard elements that match the characteristics of the Master Data. Finally, the standardized elements are verified through the standardization system that is utilized in the country.

• The Journal of the Korea Contents Association
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• v.19 no.9
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• pp.26-36
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• 2019

Since more than 30 different information systems are collecting and providing construction related information, it is difficult for information users to figure out where and how to acquire the required information. Even if the user find the information, it is hard to meet users demand. Because the current systems accumulate the data just as administrative data and can do not connect the information from the different phases of the lifecycle for the specific facility. The information collected and managed in different information systems should be integrated in terms of lifecycle of the facility to improve money for value of public investment, the quality of life by improving quality of the facility and to provide the foundation for big data utilization in the construction industry. This paper suggested strategic planning for the development of the (assumed name) "The Lifecycle Integrated Construction Information Portal" as a foundation to use the data in construction industry, by investigating prerequisites and suggesting conceptual framework of the system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3966-3991
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• 2018

Due to the lack of visualization services and organic combinations between public and private buildings data, the usability of the basic map has remained low. To address this issue, this paper reports on a solution that organically combines public and private data while providing visualization services to general users. For this purpose, factors that can affect building prices first were examined in order to define the related data attributes. To extract the relevant data attributes, this paper presents a method of acquiring public information data and real estate-related information, as provided by private real estate portal sites. The paper also proposes a pretreatment process required for intelligent machine learning. This report goes on to suggest an intelligent machine learning algorithm that predicts buildings' value pricing and future value by using big data regarding buildings' spatial information, as acquired from a database containing building value attributes. The algorithm's availability was tested by establishing a prototype targeting pilot areas, including Suwon, Anyang, and Gunpo in South Korea. Finally, a prototype visualization solution was developed in order to allow general users to effectively use buildings' value ranking and value pricing, as predicted by intelligent machine learning.

• Korean Journal of Artificial Intelligence
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• v.6 no.1
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• pp.16-20
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• 2018

The development of modern information technology has increased the amount of big data about patients' information and diseases. In this study, we developed a prediction model of diabetes using the health examination data provided by the public data portal in 2016. In addition, we graphically visualized diabetes incidence by sex, age, residence area, and income level. As a result, the incidence of diabetes was different in each residence area and income level, and the probability of accurately predicting male and female was about 65%. In addition, it can be confirmed that the influence of X on male and Y on female is highly to affect diabetes. This predictive model can be used to predict the high-risk patients and low-risk patients of diabetes and to alarm the serious patients, thereby dramatically improving the re-admission rate. Ultimately it will be possible to contribute to improve public health and reduce chronic disease management cost by continuous target selection and management.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.301-302
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• 2020

The number of abandoned dogs were increasing every year in South Korea. However, many dogs are euthanized in the shelter because of the lack of budget. This project predicts euthanasia of abandoned dogs using machine learning algorithm. It collects data from the public data portal where Korea government provides a public dataset as a form of open API. This project uses recent three-year data 2017 to 2019 and 263371 cases were founded. This project implements random forest and logistic regression models. This project attained an average 72% of prediction accuracy.

• The Korean Journal of Health Service Management
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• v.7 no.3
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• pp.251-260
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• 2013

The purpose of this study was to make a comparative analysis of dental question and answer in portal sites. To achieve this, 4,212 questions were used for final analysis after connecting to Naver, Daum and Nate, which take first, second and third place in rank information of all sites at Rankey.com, to search dental information by keyword from January to late March. The results are as follows. Naver was the highest as a portal of Internet search engines. Questions on the use of dental clinics, the quality of dental services and the offer of dental services by types of dental clinics were very important. Dental clinics had to give dental patients customized services and information to please them through dental services and dental information services on the Internet, and questions and answers on this were increasing very explosively. Consequently, Dental clinics will have to give Internet users and health- and disease-related data searchers distinctive professional services by inquiring into factors affecting portal search and factors affecting health- and disease-related search, respectively.