• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.139-146
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• 2022

Recently, solving industrial and social problems and creating new values based on big data and AI is being discussed as the main policy goal. The new government also set the digital platform government as a national task in order to achieve new value creation based on big data and AI. However, studies that summarize and diagnose discussions over the past five years are insufficient. Therefore, this study diagnoses the discussions over the past 5 years using the 4th industrial revolution as a keyword. After collecting news editorials from 2017 to 2022 by applying the text mining technique, 9 major topics were discovered. In conclusion, this study provided implications for the government's task to prepare for the future society.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.13-20
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• 2020

One of the major issues surrounding big data is the availability of massive time-based or telemetry data. Now, the appearance of low cost capture and storage devices has become possible to get very detailed time data to be used for further analysis. Thus, we can use these time data to get more knowledge about the underlying system or to predict future events with higher accuracy. In particular, it is very important to define custom tailored contract offers for many households and businesses having smart meter records and predict the future electricity usage to protect the electricity companies from power shortage or power surplus. It is required to identify a few groups with common electricity behavior to make it worth the creation of customized contract offers. This study suggests big data transformation as a side effect and clustering technique to understand the electricity usage pattern by using the open data related to smart meter and KNIME which is an open source platform for data analytics, providing a user-friendly graphical workbench for the entire analysis process. While the big data components are not open source, they are also available for a trial if required. After importing, cleaning and transforming the smart meter big data, it is possible to interpret each meter data in terms of electricity usage behavior through a dynamic time warping method.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.28 no.4
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• pp.89-101
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• 2020

In order to enhance the safety of the international aviation industry, the International Civil Aviation Organization has recommended establishing an operational foundation for systematic and integrated collection, storage, analysis and sharing of aviation safety data. Accordingly, the Korea aviation industry also needs to comprehensively manage the safety data which generated and collected by various stakeholders related to aviation safety, and through this, it is necessary to previously identify and remove hazards that may cause accident. For more effective data management and utilization, a standard structure should be established to enable integrated management and sharing of safety data. Therefore, this study aims to propose the framework about how to manage and integrate the aviation safety data for big data-based aviation safety management and shared platform.

• Journal of the Korean Society of Earth Science Education
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• v.15 no.1
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• pp.47-61
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• 2022

In this study, We developed VR (Virtual Reality) geological resources based on the Geo Big Data of the Big Data platform that provided by the Korea Institute of Geoscience and Mineral Material (KIGAM). So students selected the theme of lessons by using these resources and we operated Remote classes using the materials that developed as to Virtual Reality. Therefore, the geological theme maps provided by the Geo Big Data Open Platform were reconstructed and produced materials were created for Study about Real Korean geological outcrops grounded in Virtual Reality. And Topographic information data was used to produce class materials for Remote classes. Twenty students were selected by Random sampling, and data were collected by conducting a survey including interviews to confirm the change in students' perception of remote classes in virtual reality geological data development and the effect of the classes, so data were analyzed through inductive categorization. The results of this study are as follows. First, students showed positive responses in terms of interest, utilization, and knowledge utilization as taking remote classes for developing geological data in virtual reality geological data. This is the result of showing the adaptability of diverse and flexible learning getting away from a fixed framework by motivating and encouraging students and inducing cooperation for communication. Second, students recognized distance education in the development of Virtual Reality geological data as 'Realistic hands-on learning process', 'Immersive learning process by motivation', and 'Learning process of acquiring knowledge in the field of earth science'.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.851-858
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• 2021

Due to the increase in renewable energy and distributed resources, not only traditional data but also various energy-related data are being generated in the new energy industry. In other words, there are various renewable energy facilities and power generation data, system operation data, metering and rate-related data, as well as weather and energy efficiency data necessary for new services and analysis. Energy big data processing technology can systematically analyze and diagnose data generated in the first half of the power production and consumption infrastructure, including distributed resources, systems, and AMI. Through this, it will be a technology that supports the creation of new businesses in convergence between the ICT industry and the energy industry. To this end, research on the data analysis system, such as itemized characteristic analysis of the collected data, correlation sampling, categorization of each feature, and element definition, is needed. In addition, research on data purification technology for data loss and abnormal state processing should be conducted. In addition, it is necessary to develop and structure NIFI, Spark, and HDFS systems so that energy data can be stored and managed in real time. In this study, the overall energy data processing technology and system for various power transactions as described above were proposed.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.311-313
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• 2023

자율운항선박, 스마트 해상물류 등 미래 해상환경의 패러다임 변화에 대응하여 항로표지 현장시설을 고도화하고 신해상교통인프라 지능화 및 정보서비스 개발을 위해 스마트 항로표지 및 연계기술 개발 사업을 수행하고 있다. 본 사업의 1단계 연구 성과로 스마트 항로표지 통합플랫폼과 항로표지 서비스 성능시험환경이 구축되는데, 본 연구에서는 스마트 항로표지 통합 플랫폼에 설치된 각종 센서에서 수집된 정보를 육상의 성능시험환경으로 연동 시험에 관한 시나리오를 설계하였다. 스마트 항로표지 통합 플랫폼 및 장착되는 센서에 사전에 설계된 해양자원명을 부여하고 항로표지 정보관리시스템의 등록하는 절차를 제시하였고, 실시간으로 수집되는 항로표지 정보를 연동하여 빅데이터 분석 플랫폼으로 저장하고, 저장한 정보를 항로표지 서비스로의 적용과 활용에 관한 시나리오 설계 결과를 검토하였다.

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.100-114
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• 2017

Most of the researchers establish research directions to conduct the study of new fields by getting advice from experts or through the papers of experts. The existing academic data search services provide paper information by field but do not provide experts by field. Therefore, users should decide experts by field using the searched papers by themselves. In this paper, we design and implement an expert search system by discipline through big data processing based on papers that have been published in the academic societies. The proposed system utilizes distributed big data storage systems to store and manage large papers. We also discriminate experts and analyze data related to the experts by using distributed big data processing technologies. The processed results are provided through web pages when a user searches for experts. The user can get a lot of helps for the research of a particular field since the proposed system recommends the experts of the corresponding research field.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.277-284
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• 2021

Recently, the smart home environment is expected to be a platform that collects, integrates, and utilizes various data through convergence with wireless information and communication technology. In fact, the number of smart devices with various sensors is increasing inside smart homes. The amount of data that needs to be processed by the increased number of smart devices is also increasing, and big data processing systems are actively being introduced to handle it effectively. However, traditional big data processing systems have all requests directed to cluster drivers before they are allocated to distributed nodes, leading to reduced cluster-wide performance sharing as cluster drivers managing segmentation tasks become bottlenecks. In particular, there is a greater delay rate on smart home devices that constantly request small data processing. Thus, in this paper, we design a Apriori-based big data system for effective data processing in smart home environments where frequent requests occur at the same time. According to the performance evaluation results of the proposed system, the data processing time was reduced by up to 38.6% from at least 19.2% compared to the existing system. The reason for this result is related to the type of data being measured. Because the amount of data collected in a smart home environment is large, the use of cache servers plays a major role in data processing, and association analysis with Apriori algorithms stores highly relevant sensor data in the cache.

• Journal of Internet Computing and Services
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• v.21 no.6
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• pp.95-104
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• 2020

In this paper, we propose the real time indoor air quality monitoring and controlling platform on cloud using IoT sensor data such as PM10, PM2.5, CO2, VOCs, temperature, and humidity which has direct or indirect impact to indoor air quality. The system is connected to air ventilator to manage and optimize the indoor air quality. The proposed system has three main parts; First, IoT data collection service to measure, and collect indoor air quality in real time from IoT sensor network, Second, Big data processing pipeline to process and store the collected data on cloud platform and Finally, Big data analysis and visualization service to give real time insight of indoor air quality on mobile and web application. For the implication of the proposed system, IoT sensor kits are installed on three different public day care center where the indoor pollution can cause serious impact to the health and education of growing kids. Analyzed results are visualized on mobile and web application. The impact of ventilation system to indoor air quality is tested statistically and the result shows the proper optimization of indoor air quality.

• Annual Conference of KIPS
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• 2020.11a
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• pp.45-46
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• 2020

최근 의료데이터의 표준화를 기반으로 다양한 임상연구가 국내외에서 활발하게 진행되고 있다. 그러나 대부분 개발기술이 임상현장에 적용되지 못하는 이유는 상이한 인프라로 인한 일관성있는 결가를 도출하지 못하는 문제점과 부족한 진단지표와 기준 그리고 충분하지 못한 기술적·임상적 검증이 문제가 되고 있다. 본 논문에서는 이러한 문제점을 해결하기위한 새로운 통합 플랫폼을 제안하고자 한다. 이를 위해서 임상데이터는 OHDSI의 OMOP-CDM으로 표준화되어야 하며, 이외에 의료영상 정보를 포함한다. 제안한 플랫폼은 표준화된 데이터를 통해 지속적인 자가 학습을 수행하며, 질환별 진단에 필요한 개발 도구와 분석 소프트웨어 도구를 통해 다양한 타겟 질환연구를 지원한다. 제안한 플랫폼은 질환에 대한 비침습적 진단을 위해 의료영상을 기반으로 데이터표준화을 기반으로하며, 이를통해 인공지능 기술을 개발하고 병원 정보시스템과 연계하여 임상현장에 실증을 통해 검증하고자 한다.