Monitoring and Forecasting of Temperature Sensor Data for Large-scale Computing Servers
| Project/Area Number |
24500138
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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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| Research Field |
Media informatics/Database
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| Research Institution | Kumamoto University (2013-2014)
NTT Communication Science Laboratories (2012) |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
YOSHIKAWA Masatoshi 京都大学, 情報学研究科, 教授 (30182736)
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| Project Period (FY) |
2012-04-01 – 2015-03-31
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| Project Status |
Completed (Fiscal Year 2014)
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| Budget Amount *help |
¥5,330,000 (Direct Cost: ¥4,100,000、Indirect Cost: ¥1,230,000)
Fiscal Year 2014: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2013: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2012: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | データマイニング / 時系列データ / センサデータ / 時系列解析 / センサデータ処理 / パターン検出 / モデル学習 / 時系列データ解析 / データストリーム / 情報予測 |
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| Outline of Final Research Achievements |
We developed time-series data mining techniques to predict the future CPU temperature for data-center monitoring. Time-series data analysis is a well-known topic that has attracted huge interest in various research communities (e.g., theory, databases, data mining, networking) for a few decades. The increasing volume of online, time-stamped activity represents a vital new opportunity for data analysis, and the most fundamental requirements are the efficient and effective mining of big time-series data with high variety information. We presented three new
directions for research on time-series analysis, which include: (1) automatic mining, (2) large-scale tensor analysis, (3) non-linear modeling, and we developed efficient and effective data mining techniques.
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Report
(4 results)
Research Products
(24 results)
