Study on Nonlinear Filtering Method for Streaming Computing under Edge Heavy Data Environment
| Project/Area Number |
26280010
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Partial Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | The Institute of Statistical Mathematics |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
中野 慎也 統計数理研究所, モデリング研究系, 助教 (40378576)
有吉 雄哉 統計数理研究所, データ同化研究開発センター, 特任研究員 (80735019)
齋藤 正也 東京大学, 医学系研究科, 特任助教 (00470047)
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥15,730,000 (Direct Cost: ¥12,100,000、Indirect Cost: ¥3,630,000)
Fiscal Year 2016: ¥5,720,000 (Direct Cost: ¥4,400,000、Indirect Cost: ¥1,320,000)
Fiscal Year 2015: ¥5,720,000 (Direct Cost: ¥4,400,000、Indirect Cost: ¥1,320,000)
Fiscal Year 2014: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
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| Keywords | 時系列解析 / 逐次データ同化 |
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| Outline of Final Research Achievements |
Increasing the rate of occurrence, which is one factor of the significant increase in the amount of big data, is a product brought about by improved sensor technology and lower cost. The frequency of occurrence of data at the site that is a contact point with the real world of information systems is ever increasing. This phenomenon is also called an edge heavy data problem. It is not realistic to transport big data as it is to the cloud, and online computation according to purpose is essential on the spot. In this research, we aim to develop a method combining the excellent points of nonlinear filtering with different characteristics. As a result, we extend the applicability of stream computing technology in machine learning technique.
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Report
(4 results)
Research Products
(46 results)
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[Presentation] 同床異夢のビッグデータ2014
Author(s)
樋口知之
Organizer
日経ビッグデータ創刊記念フォーラム
Place of Presentation
日経ホール（東京都千代田区）
Year and Date
2014-04-22
Related Report
Invited
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