| Project/Area Number |
21K12034
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 61030:Intelligent informatics-related
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| Research Institution | The University of Aizu |
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Principal Investigator |
Rage Uday・Kiran 会津大学, コンピュータ理工学部, 准教授 (20874324)
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| Co-Investigator(Kenkyū-buntansha) |
是津 耕司 国立研究開発法人情報通信研究機構, ユニバーサルコミュニケーション研究所統合ビッグデータ研究センター, 研究センター長 (40415857)
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| Project Period (FY) |
2021-04-01 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2024: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2023: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2022: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2021: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
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| Keywords | Big data / high utility patterns / spatial information / data mining / Pattern mining / Air pollution analytics |
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| Outline of Research at the Start |
“Mining time series data” is one of the top-10 challenges in data mining. This research aims to tackle this challenging problem of great importance by proposing a mathematical model to uncover periodic spatial patterns in irregular spatiotemporal big data. We will deliver a mathematical model and software programs to uncover interesting patterns in spatiotemporal big data. Our deliverables will be “open-sourced” to foster R&D on data mining.
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| Outline of Annual Research Achievements |
We have developed three novel pattern mining algorithms to discover useful patterns in the air pollution data by modeling it as uncertain, fuzzy, and certain data. The discovered patterns were described in the publications.
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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
The work is going smoothly by collecting the data of 5+ years of air pollution data. The co-researcher from NICT helped us in speedup the task smoothly. The portion of the work carried in the previous year can be found at https://github.com/UdayLab/PAMI/blob/main/notebooks/knowledgeDiscoveryInData.ipynb
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| Strategy for Future Research Activity |
This year we plan to develop a real-world application for the air pollution data analytics. It involves developing a data warehouse and our algorithm to uncover hidden patterns in global air pollution data.
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