Research

Last modified by Administrator on Thu, 02/27/2020, 10:35 AM

The Computing and Archiving Research Environment (COARE) is a service offered by the Department of Science and Technology’s Advanced Science and Technology Institute (DOST-ASTI) to provide a platform for high-performance computing, storage, analysis, and sharing of scientific data. As it enables collaborative research, the COARE aims to provide support to researchers from various fields and specializations to deliver data-driven solutions and scientific-based policy and decision-making

Research from COARE users

The COARE partners with researchers across a range of disciplines to enable multiple data integration and collaborative research. As research becomes more cross-disciplinary and interdependent, the COARE aims to address the need for a platform that provides data storage and high-performance computing to researchers from various research fields.

At present, the COARE collaborates with researchers and provides data storage and high-performance computing resources to users from different areas and specializations, which include the following:

  • Computational Material Sciences
  • Chemistry
  • Molecular Modeling
  • Computational Fluid Dynamics
  • Computational Mechanics
  • Bioinformatics
  • Genomics
  • Computational Biology
  • Oceanography
  • Weather Forecasting
  • Environmental Computing
  • Artificial Intelligence
  • Big Data Analytics.

Research from the COARE Team

Aside from maintaining the COARE and providing support to the COARE users, the COARE Team also contributes their own research as they engage in different collaborative projects, expand the COARE’s partnerships and participation in various research consortia, and explore collaborations with other R&D groups from the industry.

As of 2019, the following research have been conducted by some of the COARE Team members:

Author/sTitle
Rubio, Jessi Christa & Ramos, Manuel Jr.Data Center Heat Distribution Modeling Using Onboard Sensors. IEEE Explore, 2019.
Combinido, Jay Samuel, et al.A convolutional neural network approach for estimating tropical cyclone intensity using satellite-based infrared images. International Conference on Pattern Recognition, 2018.
Villapando, Aira, et al.Predicting ASTI automated weather station (AWS) failure based on data-forwarding behavior. Proceedings of the 36th SPP Physics Congress, 2018.
Mendoza, John Robert, et al.Efficient Feature Extraction for Internet Data Analysis using AS2Vec. 33rd Symposium on Applied Computing, 2018.
Mendoza, John Robert, et al.Peering into peering: Building better tools for better peering decisions. Telecommunication Networks and Applications Conference (ITNAC), 2016 26th International. IEEE, 2016.
Cayaco, Adrian & Rubio, Jessi ChristaOpenCL Parallel Processing Simulations in HPC, 2016.
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