과학기술 빅데이터 공유·융합체제 구축

Abstract

본 연구과제의 목적은 과학데이터의 공동 활용체제를 구축하고 이의 확산을 통해 과학기술 혁신을 지원하는 것임. 이를 위해 본 연구과제는 당해 연도에 다음과 같이 수행하였음. ○ 국가 과학데이터 공동 활용 거버넌스 체제 구축 지원 - 출연(연) 중심의 과학데이터 생산 및 보유 실태 파악 - 해외 주요 국가의 과학데이터 체계 사례 조사 - 국가 과학데이터 공동 활용을 위한 제도 및 가이드라인 제안 - 클라우드 기반의 국가 과학데이터 공동 활용 플랫폼 구축 전략 및 상세 기능 정의 - 과학데이터 기반 사회 현안해결을 위한 이슈 도출 및 과제 기획 ○ 데이터 집중형 연구 환경 구축 및 지원 - 다차원 배열 데이터 분석 성능 향상 연구 - 위성 빅데이타 기반 적조 탐지 및 발생 경향 분석 연구 - 해색 원격탐사 빅데이터를 이용한 해양 환경 변화 연구 - 핵융합 연구 환경 분석 - 하둡 개발자를 위한 클러스터 환경 구축 시스템 개발 - 텐서 기반 다차원 데이터 분석기술 연구

Ⅳ. Results of the study □ Support for establishment of a national scientific data governance system ○ A study on scientific data production and retention of national research institutes - Research on the scientific data status from 22 national R&D projects of research institutes affiliated to the national research council of science and technology - Scientific data pattern analysis in perspective with production, management, and utilization - In-depth research on legal systems, policies, DMP, data management organization system and infrastructures of the UK, Australia, and the USA ○ Proposition of a legal system and guideline for scientific data co-utilization - A standard agreement model between a data owner and a user - A standard data manage plan(DMP) - A standard metadata model and guideline ○ Detail design specification of a national scientific data co-utilization platform - Concept model and detail design specification of a cloud-based platform for collecting, archiving, managing, and sharing scientific data ○ Project plan for resolving social issues with utilizing scientific data - Deduction of 55 social issues in 13 economic, international, political, S&T areas with the result of research on current overseas and domestic social problems and future issues - Proposition of a project plan to resolve a social issue □ Research on technologies for data-intensive science with scientific big data ○ Improving the performance of multi-dimensional data analysis - A revolutionary distributed in-situ data analysis technique that analyzes scientific data directly from RAW files without data loding procedure - In-situ analysis engine which is up to 75 times faster than conventional database systems and that consequently improves research productivity - An efficient technique that improves data loading procedure in SciDB database by up to 250% ○ A study on the detection and likelihood assessment of HAB outbreaks using big satellite databases - Spatial and temporal variations of red tide events based on red tide occurrence from 1998 to 2015 - Environmental characteristics in red tide areas based on in-situ measurement, satellite data and meteorological/hydrography data - Development of red tide detection algorithm - Establishment of big-data platform for red tide analysis ○ A study on ocean environmental change utlizing ocean color data - KISTI-KOPRI fast distributed data processing for ocean color data - Remote sensing data based spatio-temporal change of ocean ecology ○ Analysis of fusion energy research environments - A survey on the issues and limitations in the MDSPlus system currently deployed in KSTAR - Guidelines for moving MDSPlus data to HDF5 file formats to cope with the directions of ITER international fusion energy research activity ○ Development of Hadoop cluster automatic construction ○ A distribution version of the Hadoop framework enhanced to support iterations -A Tensor-based Multi-dimensional Data Analysis Technique - Development of a Tensor Decomposition Technique - Map-Reduce based Sparse Tensor Process Optimization ○ A survey on technology trends of assembly program techniques of hyperploidy complex plant genome - Analysis of de novo assembly algorithm and program for assembling plant genome data - Analysis of current genome databases ○ A survey on scientific data provenance techniques - Trend analysis for scientific data provenance modeling - Trend analysis for scientific data provenance management system