Mining and Forecasting of Big Time-evolving Events

2016 Fiscal Year Final Research Report

• Project/Area Number: 26730060
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Multimedia database
• Research Institution: Kumamoto University
• Principal Investigator: Matsubara Yasuko 熊本大学, 大学院先端科学研究部(工), 助教 (00721739)
• 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
• Project Period (FY): 2014-04-01 – 2017-03-31
• 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

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.

Free Research Field

データベース，データマイニング