• Annual Conference of KIPS
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• 2014.11a
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• pp.829-832
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• 2014

최근 사람 간 소통채널인 소셜미디어는 매스미디어 중심의 정보유통의 흐름을 바꿔놓으며 기업, 공공기관 등에서 가치를 찾는 핵심자원으로 관심을 받고 있다. 재난관리도 기존의 정부중심 대응에서 벗어나 소셜미디어, 즉 소셜 빅데이터를 활용한 국민 참여형 재난관리의 필요성이 대두되고 있다. 본 논문에서는 재난관리를 위해 실시간 소셜 빅데이터를 모니터링하는 시스템인 국립재난안전연구원의 소셜 빅보드(Social Big Board)를 소개하고, 이 시스템의 재난이슈 탐지의 정확성 향상을 위해 새롭게 개발된 재난유형별 관련도에 기반한 재난이슈 탐지기법을 설명하며 실험 및 평가결과를 제시하고자 한다.

• Annual Conference of KIPS
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• 2014.04a
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• pp.305-308
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• 2014

인간은 성격에 따라 이동패턴이 변화한다고 한다. 이런 점에서 인간의 성격 데이터를 이용하면, 인간의 행동 패턴을 유추해 낼 수 있다. 우리는 실제 실험자들의 GPS데이터와 BFI성격 데이터를 수집하고. Back Propagation Network를 이용하여, 새로운 위치 데이터를 추론하는 과정을 설명하였다. 논문의 내용은 다음과 같다. 첫 번째로 BFI(Big-Five Inventory) 성격평가에 대해 설명한다. 두 번째로 GPS데이터와 성격 데이터를 실험에 적절한 형태로 변환하는 방법에 대해 언급하고, 세 번째로 변환된 데이터를 이용하여 사람의 새로운 위치 정보를 추론할 것이다. 마지막으로 해당 실험의 결과 및 분석 그리고 앞으로의 연구 방향에 대해 언급할 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.2
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• pp.540-546
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• 2014

Recently a new government has announced an action plan known as the government 3.0, which aims to provide customized services for individual people, generate more jobs and support creative economy. Leading on from previous similar initiatives, the new scheme seeks to focus on open, share, communicate, and collaborate. In promoting Government 3.0, the crucial factor might be how to align the core services and policies of Government 3.0 with correspoding technologies. The paper describes the concepts and features of Government 3.0, identifies emerging Internet-based technologies and services toward the initiative, and finally provides improvement plans for Government 3.0. As a result, 10 issues to be brought together include: Smart Phone Applications and Service, Mobile Internet Computing and Application, Wireless and Sensor Network, Security & Privacy in Internet, Energy-efficient Computing & Smart Grid, Multimedia & Image Processing, Data Mining and Big Data, Software Engineering, Internet Business related Policy, and Management of Internet Application.

• Ecology and Resilient Infrastructure
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• v.6 no.4
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• pp.250-257
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• 2019

Recently, 357 water-friendly space were established near the main streams of the country through the Four Major Rivers Project, which was used as a resting and leisure space for the citizens, and the river environment and ecological health were improved. We are working hard to reduce the number of points and plan and manage the water-friendly space. In particular, attempts are being made to utilize mobile big data to make more scientific and systematic research on the number of users. However, when using mobile big data compared to the existing method of conducting field surveys, it is possible to easily identify spatial user movement patterns, but it is different from the actual amount of use, so various verifications are required to solve this problem. Therefore, this study evaluated the accuracy of estimating the number of users using mobile big data by comparing the number of visitors using mobile big data and the number of visitors using drone for Samrak ecological park located in the mouth of Nakdong River. As a result, in the river hydrophilic district, it was difficult to accurately estimating the usage pattern of each facility due to the low precision of pCELL, and it was confirmed that the usage patterns in the park could be distorted due to the signals stopped at roads and parking lots. Therefore, it is necessary to improve the number of pCELLs in the water-friendly space and to estimate the number of visitors excluding facilities such as roads and parking lots in future mobile big data processing.

• Journal of Information Technology Services
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• v.22 no.3
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• pp.29-47
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• 2023

With the recent advancement of information and communication technologies such as artificial intelligence, big data, cloud computing, and 5G, data is being produced and digitized in unprecedented amounts. As a result, data has emerged as a critical resource for the future economy, and overseas countries have been revising laws for data protection and utilization. In Korea, the 'Data 3 Act' was revised in 2020 to introduce institutional measures that classify personal information, pseudonymized information, and anonymous information for research, statistics, and preservation of public records. Among them, it is expected to increase the added value of data by combining pseudonymized personal information, and to this end, "the Expert Data Combination Agency" and "the Expert Data Agency" (hereinafter referred to as the Expert Data Processing Agency) system were introduced. In comparison to these domestic systems, we would like to analyze similar overseas systems, and it was recently confirmed that the Vermont government in the United States enacted the first "Data Broker Act" in the United States as a measure to protect personal information held by data brokers. In this study, we aim to compare and analyze the roles and functions of the "Expert Data Processing Agency" and "Data Broker," and to identify differences in designated standards, security measures, etc., in order to present ways to contribute to the activation of the data economy and enhance information protection.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1141-1155
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• 2019

Today, most approaches used in the recommendation system provide correct data prediction similar to the data that users need. The method that researchers are paying attention and apply as a model in the recommendation system is the communities' detection in the big social network. The outputted result of this approach is effective in improving the exactness. Therefore, in this paper, the personalized movie recommendation system that combines data mining for the k-clique method is proposed as the best exactness data to the users. The proposed approach was compared with the existing approaches like k-clique, collaborative filtering, and collaborative filtering using k-nearest neighbor. The outputted result guarantees that the proposed method gives significant exactness data compared to the existing approach. In the experiment, the MovieLens data were used as practice and test data.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.837-844
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• 2018

In order to increase the generation of renewable energy, it is necessary to increase or decrease the generation amount of existing generators. The generators that respond rapidly to increase / decrease the generation amount generally have high generation cost. Therefore, Cost effectiveness is affected. In this paper, we propose a PV remote control system with big data to minimize the uncertainty of solar power generation prediction.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.07a
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• pp.335-336
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• 2019

5G를 비롯한 무선 네트워크의 발달과 인터넷의 보급이 보편화되어 가고 있다. 또한, 스마트폰 등의 모바일 기기 등이 일상화됨에 따라 방대하고 다양한 유형의 데이터들이 발생되고 있다. 이와 같은 범람하기 시작한 정보와 데이터들을 연결하여 새로운 가치를 창출하는 초지능 연결의 4차 산업혁명 시대가 도래하였다. 이러한 4차 산업혁명은 ICBM(IoT, Cloud, Big data, Mobile) 기술이 발달함에 따라 가능했으며. 그중 빅데이터는 초지능 연결의 근간이 되고 있다. 하지만, 빅데이터에서의 데이터는 다양한 목적에 의해 다양한 유형의 데이터를 모두 포함하고 있음에도 데이터 포맷 및 데이터 셋 등의 불일치에 의해 즉각적인 연결은 불가능하다. 본 논문에서는 스마트 공장을 중심으로 서로 다른 형태의 이기종 데이터를 통합하여 처리할 수 있는 빅데이터 처리 플랫폼을 제안한다.

• Food Science and Industry
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• v.50 no.1
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• pp.16-25
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• 2017

There is no doubt that accumulation of big data using multi-omics technologies will be useful to solve human's long-standing problems such as development of personalized diet and medicine, overcoming diseases, and longevity. However, in the food industry, big data based on omics is scarcely accumulated. In particular, comprehensive analysis of molecular lipid metabolites directly associated with food quality, such as taste, flavor, and texture has been very limited. Moreover, most of food lipidomics studies are applied to analyze lipid components and discriminate authenticity and freshness of limited foods including vegetable and fish oil. However, if lipid big data through food lipidomics research of various foods and materials can be accumulated, lipidomics can be used in the optimization of food processing, production, delivery system, food safety, and storage as well as functional food.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.12
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• pp.287-296
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• 2019

Due to the development and increased usage of Internet services such as IoT and cloud computing, a large number of packets are being generated on the Internet. In order to create a safe Internet environment, malicious data that may exist among these packets must be processed and detected quickly. In this paper, we apply MongoDB, which is specialized for unstructured data analysis and big data processing, to intrusion detection system for rapid processing of big data security events. In addition, building the intrusion detection system(IDS) using some of the private cloud resources which is the target of protection, elastic and dynamic reconfiguration of the IDS is made possible as the number of security events increase or decrease. In order to evaluate the performance of MongoDB - based IDS proposed in this paper, we constructed prototype systems of IDS based on MongoDB as well as existing relational database, and compared their performance. Moreover, the number of virtual machine has been increased to find out the performance change as the IDS is distributed. As a result, it is shown that the performance is improved as the number of virtual machine is increased to make IDS distributed in MongoDB environment but keeping the overall system performance unchanged. The security event input rate based on distributed MongoDB was faster as much as 60%, and distributed MongoDB-based intrusion detection rate was faster up to 100% comparing to the IDS based on relational database.