• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.682-685
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• 2017

클라우드 기반 채팅 봇 인프라와 Hadoop을 Mash-Up한 사용자 맞춤 병원 소개 서비스.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.8 no.4
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• pp.281-298
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• 2015

From the complexity system theory, City is dynamic system which has evolved through evolution and adaptation in initial conditions and different situation. So people's active should involve in decision-making processes in the urban planning. And this suggests that responding to the demands of its citizens are important factors influencing the process of urban planning. The implications of this study are following: using big data helps people understand current social phenomena. Specifically, it figured out latent needs of citizens that traditional survey methods could not before. we can make the most of new opportunities given by digital data and prevent potential dangers in advance. They are complementary and do not replace one another.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.221-228
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• 2020

As industrial technology advances, convenience is also being developed. Many people living in big cities are commuting using transportation such as buses, taxis, cars, etc. and enjoy leisure, so research is needed to reduce the damages caused by traffic accidents. This study deals with estimating road-level rainfall in real-time. A rainfall observation data and radar data provided by the Korea meteorological administration were collected in real-time to create an integrated database, which was estimated as road-level rainfall by universal kriging method. Besides, we conducted a study to interactively visualization of mash-up road traffic information in real-time with integrating rainfall information.

• Journal of KIISE
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• v.41 no.9
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• pp.686-698
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• 2014

Recently, large-scale streaming sensor data have emerged due to explosive supply of smart phones, diffusion of IoT and Cloud computing technology, and generalization of IoT devices. Also, researches on combination of semantic web technology are being actively pushed forward by increasing of requirements for creating new value of data through data sharing and mash-up in large-scale environments. However, we are faced with big issues due to large-scale and streaming data in the inference field for creating a new knowledge. For this reason, we propose the RDFS rule based parallel reasoning scheme to service by processing large-scale streaming sensor data with the semantic web technology. In the proposed scheme, we run in parallel each job of Rete network algorithm, the existing rule inference algorithm and sharing data using the HBase, a hadoop database, as a public storage. To achieve this, we implement our system and evaluate performance through the AWS data of the weather center as large-scale streaming sensor data.