• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.121-122
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• 2021

본 연구의 목적은 Big Data를 활용하여 우리나라 아동학대에 영향을 미치는 경제적 불평등, 주거, 방임, 가정폭력 등의 생태체계적 요인을 규명하고자 한다. 이를 위해 Google 트랜드의 지난 5년간(2016.01.10.~ 2021.01.03.)의 경제적불평등-주거-방임-가정폭력-아동학대 등의 주제어 중심의 분석을 시도하였다. 분석결과, 경제적 불평등(B=.159, p<.05), 주거(B=.814, p<.01), 방임(B=.248, p<.001), 가정폭력(B=.151, p<.05)은 아동학대에 유의미한 영향을 미치는 것으로 나타났다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.5
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• pp.880-886
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• 2016

With lots of information occurring on the Internet, the search engine plays a role in gathering the scattered information on the Internet. Some search engines show not only search result pages including search keyword but also search result numbers of the keyword. The number of keyword searching result provided by the Google search engine can be utilized to identify overall trends for this search word on the internet. This paper is aimed designing and realizing the system which can efficiently manage the number of searching result provided by Google search engine. This paper proposed system operates by Web, and consist of search agent, storage node, and search node, manage keyword and search result, numbers, and executing search. The proposed system make the results such as search keywords, the number of searching, NGD(Normalized Google Distance) that is the distance between two keywords in Google area.

• Spatial Information Research
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• v.18 no.4
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• pp.141-149
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• 2010

One of recent trends in Web-based GIS is the system development using FOSS (Free and Open Source Software). Open Source software is independent from the technologies of commercial software and can increase the reusability and extensibility of existing systems. In this study, we developed a Web-based GIS for interactive visualization of geographic information using Google Earth and spatial DBMS(database management system). Google Earth Plug-in and Google Earth API(application programming interface) were used to embed a geo-browser in the Web browser. In order to integrate the Google Earth with a spatial DBMS, we implemented a KML(Keyhole Markup Language) generator for transmitting server-side data according to user's query and converting the data to a variety of KML for geovisualization on the Web. Our prototype system was tested using time-series of LAI(leaf area index), forest map, and crop yield statistics. The demonstration included the geovisualization of raster and vector data in the form of an animated map and a 3-D choropleth map. We anticipate our KML generator and system framework will be extended to a more comprehensive geospatial analysis system on the Web.

• Archives of Plastic Surgery
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• v.50 no.2
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• pp.210-212
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• 2023

• Industry Promotion Research
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• v.8 no.4
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• pp.139-146
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• 2023

The purpose of this study is to examine the research trends of overseas journals related to dream analysis over the five-year period between 2019 and 2023, and to draw implications for future domestic research directions related to dream analysis. For this purpose, 84 overseas journals searched on Google scholar from 2019 to 2023 were selected and analyzed for research trends, including publication year, journal by topic, theory of use, major research topics, and research methods. The results of the analysis showed that, first, the number of published studies increased from 2019 to 2021 and then decreased, and related studies were active in each country during the COVID-19 period. Second, there was a tendency to interpret the unconscious through dream analysis and apply it to various fields. Third, the trend of research methods was 53 studies using qualitative research, 24 studies using quantitative research, 5 mixed studies, and 2 meta-analyses. Finally, we discuss our findings and suggest further research in the field of dream analysis.

• Electronics and Telecommunications Trends
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• v.35 no.1
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• pp.80-88
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• 2020

This paper describes the development trends and service provision examples of disaster occurrence and spread prediction technology for various disasters such as tsunamis, floods, and fires. In terms of fires, we introduce the WIFIRE system, which predicts the spread of large forest fires in the United States, and the Metro21: Smart Cities Institute project, which predicts the risk of building fires. This paper describes the development trends in tsunami prediction technology in the United States and Japan using artificial intelligence (AI) to predict the occurrence and size of tsunamis that cause great damage to coastal cities in Japan, Indonesia, and the United States. In addition, it introduces the NOAA big data platform built for natural disaster prediction, considering that the use of big data is very important for AI-based disaster prediction. In addition, Google's flood forecasting system, domestic and overseas earthquake early warning system development, and service delivery cases will be introduced.

• Journal of the Korea Convergence Society
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• v.10 no.6
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• pp.59-64
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• 2019

This study used the Google Trends site to analyze selection information that users expect from prominent Toothbrushes and Toothpastes through related search keywords that users wanted to obtain. From 2006 to 2018(sep), searches for Toothbrushes and Toothpastes were arranged in the order of popularity of related searched words. The total number of searches words exposed was each 25, total 325 collected. The analysis was conducted using two methods, first, by search function. second, by a word network using a Big Data program. The study has shown that toothbrushes there are high expectations for brands, toothpaste there are high expectations in the function. In order to increase the motivation for oral health education, it is recommended to use and provide knowledge about the brand of toothbrushes and Toothpastes by the function.

• Journal of Oriental Neuropsychiatry
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• v.34 no.1
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• pp.13-21
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• 2023

Objectives: The aim of this study was to analyze correlations among searches of hwa-byung, depression, and suicide using big data. Methods: Keywords searches were performed using both Google Trends and Naver Data Lab on December 13, 2022. From 2016 to 2022, search results for keywords 'hwa-byung', 'depression', and 'suicide' were extracted with a score between 0 and 100 in terms of relative search popularity (RSP). Monthly time analysis, correlation analysis, and regional analysis were then conducted for these scores. Results: Regardless of the search period, RSP for both portal sites was in the order of 'suicide', 'depression', and 'hwa-byung'. Over time, search for 'depression' tended to increase in Google (slope: 0.0092), whereas search for 'hwa-byung' showed a slight increase in Naver (slope: 0.0024). Correlation coefficient for search terms 'depression' and 'suicide' was 0.3969 in Google Trends and 0.4459 in Naver Data Lab, showing clear positive correlations. On the other hand, there was little correlation between search results of 'hwa-byung' and 'depression' or between 'hwa-byung' and 'suicide'. However, compared to males, females showed higher positive associations between search results of 'hwa-byung' and 'depression' and between 'hwa-byung' and 'suicide'. Search terms 'depression' and 'suicide' showed high RSPs in most regions in South Korea. However, 'hwa-byung' had distinct regional differences in terms of RSP. Conclusions: Results of this study will help us understand Korean public's perception of the relevance of hwa-byung, depression, and suicide and plan future research in this topic. In addition, findings of this study may provide future public health implications for reducing the high suicide rate in Korea.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.12
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• pp.115-121
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• 2020

This study aims to investigate the effects of restrictions in economic activity on the spread of COVID-19 in the Philippines. This research employs daily time-series data of confirmed new COVID-19 cases, Apple mobility trends (i.e., use of public transport to destinations, volume of people driving, and amount of walking to destinations) and Google community mobility (i.e., visits to transit stations, visits to workplaces, and staying-at-home) indicators covering the period February 17 to September 11, 2020. The analysis starts by establishing the correlation pattern of new confirmed COVID-19 daily infections to each independent variable. The results show negative linear correlation of the number of new COVID-19 daily infections with less visit to transit station, increase stay-at-home, less use of public transport, and less amount of walking to destinations. Interestingly, the number of new COVID-19 daily infections indicates some form of positive linear correlation with visits to workplaces and volume of people driving. Moreover, employing robust least square regression via the method of MM-estimation, major findings reveal that across mobility measures, staying-at-home has the highest impact on reducing the spread of COVID-19, followed by visiting transit stations less, less use of public transport, less amount of walking, and less workplace visits.

• Electronics and Telecommunications Trends
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• v.32 no.5
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• pp.107-116
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• 2017

Artificial intelligence has recently been regarded as a key engine of future industry, and cloud computing and big data technologies have begun to receive significant attention. Major global vendors such as IBM, Microsoft, Google, and Amazon have been launching cloud-computing services for artificial intelligence. On the other hand, the situation domestically is now at an early stage. This report describes the industry trends both domestically and internationally regarding cloud computing for artificial intelligence. We also describe to significance of cloud computing ecosystem and data competitiveness for artificial intelligence.