• The Journal of Bigdata
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• v.7 no.2
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• pp.235-244
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• 2022

The rental history data of public bicycles in Seoul were analyzed to examine how pandemic phenomena such as COVID-19 caused changes in people's micro mobility. Data for 2019 and 2021 were compared and analyzed by dividing them before and after COVID-19. Data were collected from public data portal sites, and data marts were created for in-depth analysis. In order to compare the changes in the two periods, the riding direction type dimension and the rental station type dimension were added, and the derived variables (rotation rate per unit, riding speed) were newly created. There is no significant difference in the average rental time before and after COVID-19, but the average rental distance and average usage speed decreased. Even in the mobility of Ttareungi, you can see the slow rhythm of daily life. On weekdays, the usage rate was the highest during commuting hours even before COVID-19, but it increased rapidly after COVID-19. It can be interpreted that people who are concerned about infection prefer Ttareungi to village buses as a means of micro-mobility. The results of data mart-based visualization and analysis proposed in this study will be able to provide insight into public bicycle operation and policy development. In future studies, it is necessary to combine SNS data such as Twitter and Instagram with public bicycle rental history data. It is expected that the value of related research can be improved by examining the behavior of bike users in various places.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.561-563
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• 2013

모바일 디바이스가 확산 된 이후 대다수 사용자는 PC의 웹브라우저 보다는 모바일 웹브라우저를 이용하고 있다. 모바일 환경에서 매시업은 최근 몇 년 동안 상당한 관심을 받고 있으며, 정부 3.0이 주도되면서 공공데이터의 공개가 활성화되고 민간기업도 API를 공개 하고 있다. 따라서 본 논문에서는 모바일환경에서 공공데이터와 공개API를 이용한 매시업 활용방안을 연구 및 분석한다.

• Journal of Industrial Convergence
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• v.17 no.4
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• pp.33-38
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• 2019

Regional tourism is an effective means of revitalizing the local economy and improving the image of the region. In order to revitalize this, efforts should be made to create regionally specialized tourism products and to preserve the unique culture and traditions. Among them, gathering information about visitors and securing the quality competitiveness of the contents of tourism contents are very important to increase the potential of cultural tourism festival. This paper collects, refines, and processes the festival-related data in a specific area in order to enhance the visitor's tourism needs and satisfaction. In particular, negative words and positive words raised during the festival were analyzed through big data visualization using word cloud.

• Journal of the Society of Disaster Information
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• v.16 no.4
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• pp.682-690
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• 2020

Purpose: This study builds a database by collecting and refining disaster occurrence data and real-time weather and atmospheric data. In conjunction with the public data provided by the API, we propose a service model for the Big Data-based Urban Safety Index. Method: The plan is to provide a way to collect various information related to disaster occurrence by utilizing public data and SNS, and to identify and cope with disaster situations in areas of interest by real-time dashboards. Result: Compared with the prediction model by extracting the characteristics of the local safety index and weather and air relationship by area, the regional safety index in the area of traffic accidents confirmed that there is a significant correlation with weather and atmospheric data. Conclusion: It proposed a system that generates a prediction model for safety index based on machine learning algorithm and displays safety index by sector on a map in areas of interest to users.

• Proceedings of the Korea Contents Association Conference
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• 2014.11a
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• pp.295-296
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• 2014

정부 3.0이 출범하면서 공공 데이터들의 개방이 활성화되기 시작했다. 공공 빅데이터로부터 다양한 분석을 수행하고 의미 있는 정보들을 도출하기 위해서는 빅데이터 분석 도구를 이용한 복잡한 분석들을 수행하여야한다. 본 논문에서는 빅데이터 분석을 이용한 식품 정보 분석 시스템을 설계하고 구현한다. 설계한 시스템은 식품에 대한 정보를 분석하여 특징에 따라 분류하고, 빅데이터 분석을 통해 실생활에서 구매자에게 유용한 서비스를 제공하는 것을 목적으로 하고 있다.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.453-454
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• 2015

본 논문에서는 공공 민원 빅데이터 분석을 통하여 민원추이 분석을 하고, 민원 추이 분석을 통하여 민원 통계 분석 서비스, 민원 분류 분석 서비스, 민원 정책(이슈) 분석 서비스에 대한 가시화 서비스 기능을 제안한다.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.257-262
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• 2021

Accidents caused by black ice occur frequently every winter, and the fatality rate is very high compared to other traffic accidents. Therefore, a systematic method is needed to predict the black ice formation before accidents. In this paper, we proposed a black ice prediction model based on heterogenous and multi-type data. To this end, 12,574,630 cases of 46 types of land, infrastructure, transport public data and meteorological public data were collected. Subsequently, the data cleansing process including missing value detection and normalization was followed by the establishment of approximately 600,000 refined datasets. We analyzed the correlation of 42 factors collected to predict the occurrence of black ice by selecting only 21 factors that have a valid effect on black ice prediction. The prediction model developed through this will eventually be used to derive the route-specific black ice risk index, which will be utilized as a preliminary study for black ice warning alart services.

• Informatization Policy
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• v.30 no.3
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• pp.29-48
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• 2023

This study aims to investigate empirically the structural relationships among the components of data governance and their impacts on data integration and data-based administration. Through literature review, various definitions, typologies, and case studies of data governance were examined, with the definition of data governance from a public policy perspective developed and applied. The study then analyzed the data from a survey conducted by the Korea Institute of Public Administration on the use of public data policies and confirmed that organizational factors play a mediating role between institutional and technical factors, and that institutional and technical factors have statistically significant positive relationships with data fusion and data-driven administration. Based on these results, interest and investment in the improvement and development of the legal system in data governance from the institutional, technical, and organizational perspective, clarification of means and purposes of data technology, interest in data organizations and human resources, and practical operation can be achieved. Policy implications such as the development of an effective mechanism were presented.

• Journal of the Korean Society for information Management
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• v.37 no.3
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• pp.25-50
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• 2020

Since big data platform services for the public library began January 1, 2016, libraries have used big data to improve their work performance. This paper aims to examine the use cases of library big data and attempts to draw improvement plan to improve the effectiveness of library big data. For this purpose, first, we examine big data used while utilizing the library big data platform, the usage pattern of big data and services/policies drawn by big data analysis. Next, the limitations and advantages of the library big data platform are examined by comparing the data analysis of the integrated library management system (ILUS) currently used in public libraries and data analysis through the library big data platform. As a result of case analysis, big data usage patterns were found program planning and execution, collection, collection, and other types, and services/policies were summarized as customizing bookshelf themes for the book curation and reading promotion program, increasing collection utilization, and building a collection based on special topics. and disclosure of loan status data. As a result of the comparative analysis, ILUS is specialized in statistical analysis of library collection unit, and the big data platform enables selective and flexible analysis according to various attributes (age, gender, region, time of loan, etc.) reducing analysis time. Finally, the limitations revealed in case analysis and comparative analysis are summarized and suggestions for improvement are presented.