EDBT 2026 Demo / reviewers in the wild / expert
Yasumitsu Maejima
dblp:187/9753
· DBLP profile ↗
3ranked-venue papers
0as first author
2since 2021 · last 2025
0009-0006-2121-1044ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
2 papers |
High-performance computing · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Environmental and earth informatics · 72% Computational science and engineering · 28% |
Topics — the 4 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
High-performance computing › large-scale simulation
numerical weather prediction |
0.9 | 2 | 2023 | Big Data Assimilation: Real-time 30-second-refresh Heavy Rain Forecast Using Fugaku During Tokyo Olympics and Paralympics · SC 2023 "Big Data Assimilation" Toward Post-Petascale Severe Weather Prediction: An Overview and Progress · Proc. IEEE 2016 |
High-performance computing
performance optimization at scale |
0.7 | 1 | 2023 | Big Data Assimilation: Real-time 30-second-refresh Heavy Rain Forecast Using Fugaku During Tokyo Olympics and Paralympics · SC 2023 |
High-performance computing › scientific computing
data assimilation |
0.2 | 1 | 2016 | "Big Data Assimilation" Toward Post-Petascale Severe Weather Prediction: An Overview and Progress · Proc. IEEE 2016 |
Environmental and earth informatics
atmospheric science |
0.2 | 1 | 2023 | Big Data Assimilation: Real-time 30-second-refresh Heavy Rain Forecast Using Fugaku During Tokyo Olympics and Paralympics · SC 2023 |
Methods — techniques the papers use, named apart from their topics
single precision · 1.3parallel i/o · 1.3ensemble methods · 1.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Dimensionality Reduction-based Interactive Visual Analytics Approach for Investigating Ensemble Weather Simulations
Go Tamura, Sena Kobayashi, Naohisa Sakamoto, Yasumitsu Maejima, Jorji Nonaka |
HPC Asia | 4 |
| 2023 | Big Data Assimilation: Real-time 30-second-refresh Heavy Rain Forecast Using Fugaku During Tokyo Olympics and ParalympicsabstractReal-time 30-second-refresh numerical weather prediction (NWP) was performed with exclusive use of 11,580 nodes (~7%) of supercomputer Fugaku during Tokyo Olympics and Paralympics in 2021. Total 75,248 forecasts were disseminated in the 1-month period mostly stably with time-to-solution less than 3 minutes for 30-minute forecast. Japan's Big Data Assimilation (BDA) project developed the novel NWP system for precise prediction of hazardous rains toward solving the global climate crisis. Compared with typical 1-hour-refresh systems, the BDA system offered two orders of magnitude increase in problem size and revealed the effectiveness of 30-second refresh for highly nonlinear, rapidly evolving convective rains. To achieve the required time-to-solution for real-time 30-second refresh with high accuracy, the core BDA software incorporated single precision and enhanced parallel I/O with properly selected configurations of 1000 ensemble members and 500-m-mesh weather model. The massively parallel, I/O intensive real-time BDA computation demonstrated a promising future direction. Takemasa Miyoshi, Arata Amemiya, Shigenori Otsuka, Yasumitsu Maejima, Takumi Honda, Hirofumi Tomita, Seiya Nishizawa, Kenta Sueki, Tsuyoshi Yamaura, Yutaka Ishikawa, Shinsuke Satoh, Tomoo Ushio, Kana Koike, Atsuya Uno |
SC | 4 |
| 2016 | "Big Data Assimilation" Toward Post-Petascale Severe Weather Prediction: An Overview and ProgressabstractFollowing the invention of the telegraph, electronic computer, and remote sensing, “big data” is bringing another revolution to weather prediction. As sensor and computer technologies advance, orders of magnitude bigger data are produced by new sensors and high-precision computer simulation or “big simulation.” Data assimilation (DA) is a key to numerical weather prediction (NWP) by integrating the real-world sensor data into simulation. However, the current DA and NWP systems are not designed to handle the “big data” from next-generation sensors and big simulation. Therefore, we propose “big data assimilation” (BDA) innovation to fully utilize the big data. Since October 2013, the Japan's BDA project has been exploring revolutionary NWP at 100-m mesh refreshed every 30 s, orders of magnitude finer and faster than the current typical NWP systems, by taking advantage of the fortunate combination of next-generation technologies: the 10-petaflops K computer, phased array weather radar, and geostationary satellite Himawari-8. So far, a BDA prototype system was developed and tested with real-world retrospective local rainstorm cases. This paper summarizes the activities and progress of the BDA project, and concludes with perspectives toward the post-petascale supercomputing era. Takemasa Miyoshi, Guo-Yuan Lien, Shinsuke Satoh, Tomoo Ushio, Kotaro Bessho, Hirofumi Tomita, Seiya Nishizawa, Ryuji Yoshida, Sachiho A. Adachi, Jianwei Liao 0001, Balazs Gerofi, Yutaka Ishikawa, Masaru Kunii, Yasumitsu Maejima, Shigenori Otsuka, Michiko Otsuka, Kozo Okamoto, Hiromu Seko |
Proc. IEEE | 15 |