| Project/Area Number |
26730060
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Multimedia database
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| Research Institution | Kumamoto University |
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Principal Investigator |
Matsubara Yasuko 熊本大学, 大学院先端科学研究部(工), 助教 (00721739)
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| Research Collaborator |
SAKURAI Yasushi 熊本大学, 先端科学研究部, 教授 (30466411)
Christos Faloutsos Carnegie Mellon University, Dept. of Computer Science, Professor
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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 |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2015: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2014: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
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| Keywords | ソーシャルネットワーク / 非線形解析 / 特徴自動抽出 / テンソルデータ / 将来予測 / 大規模時系列データ / 非線形モデル |
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| Outline of Final Research Achievements |
Time-evolving event analysis is becoming of increasingly high importance, thanks to the decreasing cost of hardware and the increasing on-line processing capability. In such a situation, the most fundamental requirement is an efficient modeling and mining of event streams. This research project addresses three classes of tasks for time-evolving event analysis, namely, (1) automatic mining, (2) non-linear modeling and (3) large-scale tensor analysis. We developed powerful algorithms that provide efficient and effective mining of large-scale time evolving events.
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