• Journal of Digital Convergence
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• v.18 no.9
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• pp.39-47
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• 2020

This study was conducted with the aim of investigating the actual state of data utilization and the perception of big data utilization by officials of the provincial Office of Education and to derive implications for the establishment of strategies for big data utilization. An online survey of 440 people was conducted. As a result, the types and sources of data used for work varied, and data collection and refining were the most difficult parts. The infrastructure for data utilization was insufficient and the most necessary factor. The purpose of big data utilization was related to the establishment of educational policy agenda.

• Journal of Digital Convergence
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• v.13 no.10
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• pp.51-58
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• 2015

The utilization of Big data in the field of education has spread around the developed countries. However, in Korea, there are only experimental approaches related to Bigdata, yet for the related researches and services to appear. Therefore, it is the situation that needs to understand the reason for poor use of big data in the e-Learning industry, study and seek out alternatives to solve these problems. The result of this study shows that it was investigated that the high level of understanding of Bigdata has recognized large impact on e-Learning of Big Data and the more large-scale sales companies have recognized large impact on e-Learning of Big Data in the e-Learning industry. In conclusion, this study makes a proposal to expand the training and utilization policies of Bigdata relating to different sales scales.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.91-93
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• 2021

퍼스널 모빌리티를 비롯한 공유 모빌리티 시장이 국내에서 급격한 성장을 거두고 있다. 서울시에서는 2015년부터 공공자전거 서비스 '따릉이' 사업을 시작해 서울시민에게 주목받는 정책 중 하나로 자리매김했다. 그에 따라 매해 늘어나는 따릉이 수요를 맞추기 위해 서울시에서는 따릉이 대여소를 매해 증설하고 있으나, 자전거 부족, 거치대 부족으로 많은 불만이 나오고 있다. 본 논문에서는 따릉이 대여소의 이용률을 분석하여 사용이 집중되는 대여소와 그 시간대를 분석하고, 이를 통해 특정 대여소에 자전거가 필요 이상으로 반납되거나 부족해지는 현상을 해결할 방법을 제시한다.

• The Journal of Bigdata
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• v.6 no.1
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• pp.13-22
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• 2021

The purpose of this study is to develop and demonstrate a framework for evaluating the maturity of big data for effective data strategy establishment and efficient investment of companies. By supplementing the shortcomings of the evaluation developed so far, a framework was developed to evaluate the maturity of a company's big data in an integrated process. As a result, four evaluation areas of 'Vision and Strategy', 'Management', 'Analysis' and 'Utilization', assessment items for each area, detailed content, and criteria for each stage were derived. This was verified through a survey of entrepreneurs, and the maturity level of big data of domestic companies was confirmed. As a future research direction, it is proposed to develop detailed assessment factors according to the characteristics of each industry, to develop a data utilization framework according to the assessment results, and to improve validity and reliability through adjustment of verification targets.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.1-9
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• 2016

In accordance with the increased political and social utilization of social media, demands on online trend analysis and monitoring technologies based on social bigdata are also increasing rapidly. In this paper, we define 'risk' as issues which have probability of turn to negative public opinion among big social issues and classify their types in details. To define risk types, we conduct a complete survey on news documents and analyzed characteristics according to issue domains. We also investigate cross-medias analysis to find out how different public media and personalized social media. At the result, we define 58 risk types for 6 domains and developed automatic classification model based on machine learning algorithm. Based on empirical experiments, we prove the possibility of automatic detection for social issue risk in social media.

• The Journal of Bigdata
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• v.2 no.2
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• pp.1-7
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• 2017

Intelligent transportation system (ITS) has been introduced to maximize the efficiency of operation and utilization of the urban traffic facilities and promote the safety and convenience of the users. With the expansion of ITS, various traffic big data such as road traffic situation, traffic volume, public transportation operation status, management situation, and public traffic use status have been increased exponentially. In this paper, we derive structural characteristics of urban traffic according to the vehicle flow by using big data network analysis. DSRC (Dedicated Short Range Communications) data is used to construct the traffic network. The results can help to understand the complex urban traffic characteristics more easily and provide basic research data for urban transportation plan such as road congestion resolution plan, road expansion plan, and bus line/interval plan in a city.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.215-228
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• 2019

HDF5 data format is used to effectively store and distribute large volume of Geostationary Ocean Color Imager(GOCI) satellite data. The Korea Ocean Satellite Center has developed and provided a GOCI Data Processing System(GDPS) for general users who are not familiar with HDF5 format. Nevertheless, it is not easy to merge and process Hierarchical Data Format version5(HDF5) data that requires an understanding of satellite data characteristics, needs to learn how to use GDPS, and stores location and attribute information separately. Therefore, the open source R and rhdf5, data.table, and matrixStats packages were used to develop algorithm that could easily utilize satellite data in HDF5 format without the need for the process of using GDPS.

• The Journal of Bigdata
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• v.8 no.1
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• pp.1-10
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• 2023

This paper presents the design process of an agricultural data sharing platform intended to address major challenges faced by the domestic agricultural industry. The platform was designed with a user interface that prioritizes user requirements for ease of use and offers various analysis techniques to provide growth prediction for field environment, growth, management, and control data. Additionally, the platform supports File to DB and DB to DB linkage methods to ensure seamless linkage between the platform and farmhouses. The UI design process utilized HTML/CSS-based languages, JavaScript, and React to provide a comprehensive user experience from platform login to data upload, analysis, and detailed inquiry visualization. The study is expected to contribute to the development of Korean smart farm models and provide reliable data sets to agricultural industry sites and researchers.

• The Journal of Bigdata
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• v.8 no.2
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• pp.83-95
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• 2023

Big data analytics and AI play a critical role in the development of smart plants. This study presents a big data platform for plant data and an 'AutoML platform' for AI-based plant O&M(Operation and Maintenance). The big data platform collects, processes and stores large volumes of data generated in plants using Hadoop, Spark, and Kafka. The AutoML platform is a machine learning automation system aimed at constructing predictive models for equipment prognostics and process optimization in plants. The developed platforms configures a data pipeline considering compatibility with existing plant OISs(Operation Information Systems) and employs a web-based GUI to enhance both accessibility and convenience for users. Also, it has functions to load user-customizable modules into data processing and learning algorithms, which increases process flexibility. This paper demonstrates the operation of the platforms for a specific process of an oil company in Korea and presents an example of an effective data utilization platform for smart plants.

• The Journal of Bigdata
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• v.1 no.2
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• pp.45-63
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• 2016

According to changes of recent climate social structures, frequency of occurrence new or complex disasters are increasing. So the importance of disaster prevention is increasing. To provide useful information of disaster prevention activities, We use the "Safety Sinmungo" main processing practices included Facility safety management in Ministry of Public Safety and Security. Facility safety management is the most and common disaster prevention activities. We identified the keywords in the risk report and facilities to residents report and analyzed the seasonal and inter-regional facilities report distribution process. We also utilized social network analysis techniques to configure a 1-mode, 2-mode facilities around the keyword for differences.