• It's Smart Media
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• v.2 no.3
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• pp.26-36
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• 2013

• Korea Information Processing Society Review
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• v.29 no.1
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• pp.43-50
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• 2022

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.80-82
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• 2020

전통적으로 기초 과학 분야의 대규모 워크로드 작업들은 슈퍼컴퓨터와 같은 대용량 클러스터 시스템을 이용하여 수행해왔다. 그러나 최근 빅데이터 및 머신 러닝과 같은 새로운 분야에서의 컴퓨팅 자원 요구가 증가하고 기존 사용자의 요구 사항도 다양해짐에 따라 기존의 클러스터 시스템 운영 환경에서는 많은 어려움이 나타나고 있다. 이러한 문제를 해결하기 위해 한국과학기술정보연구원(KISTI)에서는 지난 3 월부터 KI (KISTI Intelligent) Cloud 서비스를 개발하여 서비스를 제공하고 있다. KI Cloud 서비스는 다음과 같은 특징이 있다. 첫째, Jupyter 과 RStudio 와 같은 대화형 개발 환경을 웹을 통해 제공함으로써 사용자는 언제, 어디서나 손쉽게 서비스를 활용할 수 있다. 둘째, 컨테이너 기술을 활용하여 사용자가 요구하는 개발 및 실행 환경을 실시간으로 구성하여 제공한다. 셋째, 사용자의 서비스 환경을 동적으로 구성하여 제공함으로써 컴퓨팅 자원의 효율성을 높일 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.90-93
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• 2017

인터넷과 클라우드 서비스 사용이 증가하면서 패킷의 양과 사이버 위협이 증가하였다. 본 논문에서는 빅데이터를 처리하기 위해 사용되는 NoSQL을 보안이벤트의 신속한 처리를 위한 침입탐지시스템에 적용하였다. 다양한 데이터 모델 유형의 NoSQL 데이터베이스 중에서 빅데이터 보안이벤트를 처리하는데 가장 적합한 시스템을 찾기 위해 세 가지 유형의 Snort 룰 기반 보안이벤트 분산 처리 프로토타입 시스템들을 구축하였고 각 시스템의 성능을 평가하였다. 그 결과로 MongoDB 기반의 보안이벤트 분산 처리 시스템이 가장 속도가 빠른 것을 확인하였다.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.7
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• pp.371-378
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• 2017

Recently, with the spread of smart device and the introduction of web services, the data is rapidly increasing online, and it is utilized in various fields. In particular, the emergence of social media in the big data field has led to a rapid increase in the amount of unstructured data. In order to extract meaningful information from such unstructured data, interest in big data technology has increased in various fields. Big data is becoming a key resource in many areas. Big data's prospects for the future are positive, but concerns about data breaches and privacy are constantly being addressed. On this subject of big data, where positive and negative views coexist, the research of analyzing people's opinions currently lack. In this study, we compared the changes in peoples perception on big data based on unstructured data collected from the social media using a text mining. As a results, yearly keywords for domestic big data, declining positive opinions, and increasing negative opinions were observed. Based on these results, we could predict the flow of domestic big data.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.57-63
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• 2022

The purpose of this study is to provide basic data on policy development using big data analysis and machine learning algorithms as part of preparing measures to prevent child abuse. In order to analyze big data for developing machine learning algorithms to prevent child abuse, frequency analysis, related word analysis, and emotional analysis were performed after defining academic databases and social network service data as big data. related words, and emotional analysis were conducted. As a result of the study, a preventive child abuse algorithm can be developed by preparing a data collection and sharing network system to prevent child abuse from the perspective of children affected by child abuse, perpetrators, and government authorities. Although it will be possible by institutionalizing infant self-esteem, depression, and anxiety tests with clues that depression and anxiety appear due to a decrease in self-concept in the characteristics of children affected by child abuse. We suggest that continuous progress of big data collection and analysis and algorithm development research to prevent child abuse, and expects that effective policies to prevent child abuse will be realized to eradicate child abuse crimes.

• Journal of Information Technology and Architecture
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• v.10 no.2
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• pp.197-209
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• 2013

This study analyzes the nature of consumers and smart phones as well as its limitations that domestic mobile communication companies confront. According to the analysis results, emerging technologies such as 5G communication, pervasive computing, augmented reality, and big data seem to have significant effect on the ICT ecosystem in the near future. Based on the results, this study suggests four counterstrategies for domestic mobile communication companies: big data strategy, preparation of things acting as a main communication agent, new service platform development, and 'total life care service provider' strategy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.548-551
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• 2018

Most vehicles are in the form of maintenance when a problem occurs by the user himself or herself. During maintenance, users are not able to operate the car while it is being serviced, and if the target vehicle is a revenue-generating vehicle, they will have to bear economic losses. Collecting vehicle information in real time, identifying problems that could arise with a vehicle based on the collected big data and providing advance service rather than after-sales service can help secure vehicle operation and reduce economic loss. Thus, in this thesis, a platform was designed to design IoT-based gateways, collect real-time vehicle information, and organize big data to provide vehicle information in real time.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1179-1187
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• 2019

In today's technology environment, most big data-based applications and solutions are based on real-time processing of streaming data. Real-time processing and analysis of big data streams plays an important role in the development of big data-based applications and solutions. In particular, in the maritime data processing environment, the necessity of developing a technology capable of rapidly processing and analyzing a large amount of real-time data due to the explosion of data is accelerating. Therefore, this paper analyzes the characteristics of NiFi, Kafka, and Druid as suitable open source among various open data technologies for processing big data, and provides the latest information on external linkage necessary for maritime service analysis in Korean e-Navigation service. To this end, we will lay the foundation for applying open data framework technology for real-time data processing.

• The Journal of the Korea Contents Association
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• v.21 no.9
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• pp.184-197
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• 2021

Prior to the establishment of 'Employment-Related Big Data Center (tentative name)' to support the development of customized employment services. This Paper examines the current status and limitation of employment-related data in korea. Then, the implications were derived through foreign employment-related big data construction cases. Through the above analysis, I proposed measures to build and utilize employment-related big data at the individual level, focusing on the Transitional Labour Markets theory that emphasizes the implementation of individual labor force states. Finally, we presented future challenges such as massive maintenance of employment-related DB, increased representation of big data to be built around employment insurance DB, and increased reliability of DB presented.