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Data Infrastructure Lab
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A research group in data engineering on managing, analyzing, and using big data.
AboutAbout the lab
Turning large volumes of data into useful information
The Data Infrastructure group conducts research and development centered on data engineering, addressing how to obtain the data that is needed and use it as useful information.
It belongs to the Computational Informatics Division of the Center for Computational Sciences and is in charge of the data engineering fields related to the use of big data.
It works with divisions such as the Global Environmental Science Division, the Particle Physics Division, and the Life Sciences Division, and carries out applied research in each field of computational science.
The research page was last updated in December 2019, and its content reflects that time.
Key terms
- Big data
- Large-scale data with three characteristics: volume, variety, and velocity
- Data mining
- Technology for discovering knowledge and patterns buried in large-scale scientific data and social media
- GPV/JMA
- A database that archives the numerical weather data (GPV) published by the Japan Meteorological Agency and makes it available to researchers and others
ResearchResearch themes
In computational science, managing and using big data is an extremely important challenge. Taking an approach centered on data engineering, this group carries out research and development aimed at building the foundation of the next-generation information society.
*Illustrative imageFoundational technologies for big data
The group studies foundational technologies for processing and analyzing big data, which is large in volume, diverse in type, and fast in arrival. Its topics include an integration platform for stream data, high-performance analysis with GPUs and FPGAs, privacy and security, and processing infrastructure for open data.
*Illustrative imageData mining and knowledge discovery
The group studies methods for discovering knowledge and patterns in diverse data. Its work includes analysis algorithms for text, images, and graphs, analysis of social media, and analysis of biological data using machine learning.
*Illustrative imageManagement and use of scientific data
The group studies the management and use of rapidly growing volumes of scientific data. Its work includes operating and developing the large-scale weather database GPV/JMA, the JRA-55 archive, and the lattice QCD data grid JLDG/ILDG, as well as advancing the use of genome and biological data through machine learning.
MembersFaculty
The members of the Data Infrastructure group consist of faculty members, researchers, and collaborating researchers.
- ProfessorToshiyuki Amagasa
- ProfessorHiroaki Shiokawa
- Assistant ProfessorKazumasa Horie
- Assistant ProfessorSavong Bou
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Wikimedia Foundation Servers-8055 15” Victorgrigas / CC BY-SA 3.0 (cropped)
- Foundational technologies for big data: “Network cables in server room” ProjectManhattan / CC BY-SA 3.0 (cropped)
- Data mining and knowledge discovery: “SimpleSemanticDataMiningDiagram” HB181999 / CC BY-SA 4.0 (cropped)
- Management and use of scientific data: “Earth's City Lights by DMSP, (large)” Data: Marc Imhoff/NASA GSFC, Christopher Elvidge/NOAA NGDC; Image: Craig Mayhew and Robert Simmon/NASA GSFC / Public domain (cropped)
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- Related lab (高性能計算システム研究部門): “GIK Institute High Performance Computing Cluster” Me.hassan91 / CC BY-SA 3.0 (cropped)
- Related lab (生命科学研究部門 生命機能情報分野): “CSIRO ScienceImage 1349 Protein crystals” Health Science and Nutrition, CSIRO / CC BY 3.0 (cropped)
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