Journal of the Korean Data and Information Science Society

한국데이터정보과학회 (The Korean Data and Information Science Society)
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자연재해 분석을 위한 빅데이터 마이닝 기술

Big data mining for natural disaster analysis

  • Kim, Young-Min (Disaster Information Service Lab., Korea Institute of Science and Technology Information) ;
  • Hwang, Mi-Nyeong (Disaster Information Service Lab., Korea Institute of Science and Technology Information) ;
  • Kim, Taehong (Disaster Information Service Lab., Korea Institute of Science and Technology Information) ;
  • Jeong, Chang-Hoo (Disaster Information Service Lab., Korea Institute of Science and Technology Information) ;
  • Jeong, Do-Heon (Disaster Information Service Lab., Korea Institute of Science and Technology Information)
  • 투고 : 2015.08.05
  • 심사 : 2015.09.16
  • 발행 : 2015.09.30
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초록

자연재해 빅데이터 분석은 현재 소셜 미디어 데이터 등 텍스트 데이터를 중심으로 시작되고 있으며 이는 재난관리의 네 단계인 예방, 대비, 대응, 복구에서 마지막 두 단계에 주로 해당된다. 반면 기상 데이터 자체에 대한 빅데이터 분석은 사전 관리에 해당하는 예방, 대비 단계에 활용될 수 있어 이와 관련한 연구 사례에 대한 체계적인 정리가 필요하다. 본 논문은 리뷰 논문으로서, 자연재해 영역에서 텍스트 데이터 외의 빅데이터를 다루는 분석 기술들에 대해 소개한다. 이를 위해 기상 관련 분야에서 사용되고 있는 데이터 마이닝 및 기계 학습 기술들을 살피고 각 기상 데이터의 특성에 맞춰 기존의 기술들이 어떻게 변형되는 지 밝힌다. 우선 2절에서 빅데이터, 데이터 마이닝, 기계 학습에 대한 기본 개념을 설명하고 3절에서 데이터 마이닝 및 기계 학습 기술의 실제 적용 사례를 상세히 정리한다. 4절에서는 자연재해 대응에 이러한 기술들이 직접 활용되는 예를 소개하고 마지막에 결론으로 마무리한다.

Big data analysis for disaster have been recently started especially to text data such as social media. Social data usually supports for the final two stages of disaster management, which consists of four stages: prevention, preparation, response and recovery. Otherwise, big data analysis for meteorologic data can contribute to the prevention and preparation. This motivated us to review big data technologies dealing with non-text data rather than text in natural disaster area. To this end, we first explain the main keywords, big data, data mining and machine learning in sec. 2. Then we introduce the state-of-the-art machine learning techniques in meteorology-related field sec. 3. We show how the traditional machine learning techniques have been adapted for climatic data by taking into account the domain specificity. The application of these techniques in natural disaster response are then introduced (sec. 4), and we finally conclude with several future research directions.

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