Efficient search and discovery technology for processing massive data stream in the real world
| Project/Area Number |
15K12022
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Multimedia database
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| Research Institution | Hokkaido University |
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Principal Investigator |
Arimura Hiroki 北海道大学, 情報科学研究科, 教授 (20222763)
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| Co-Investigator(Kenkyū-buntansha) |
トーマス ツォイクマン 北海道大学, 情報科学研究科, 教授 (60374609)
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| Co-Investigator(Renkei-kenkyūsha) |
JORDAN Charles 北海道大学, 情報科学研究科, 助教 (60647577)
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| Project Period (FY) |
2015-04-01 – 2018-03-31
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| Project Status |
Completed (Fiscal Year 2017)
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| Budget Amount *help |
¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2015: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
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| Keywords | ストリームデータ処理 / 情報検索 / データマイニング / 大規模知識処理 / QBFソルバー / 多重性 / 文脈性 |
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| Outline of Final Research Achievements |
In this research, we study the algorithms and data structures for search, discovery, and analysis of complex combinatorial patterns as the base technology for massive stream data processing in the real world. Especially, we focus on the following key aspects of such algorithms: low-memory footprint, adaptivity, context-sensitivity, multiplicity. The followings are the research topics that we study: A1. Fast search algorithms based on bit-parallel techniques; A2. Ultra-fast counting technology based on stochastic testing; A3. pattern discovery using substructure enumeration; A4. Theory of knowledge discovery from massive high-speed streams; B1. Prototype implementation and preliminary evaluation.
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Report
(4 results)
Research Products
(46 results)
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[Journal Article] Non-CNF QBF Solving with QCIR2016
Author(s)
Charles Jordan, Will Klieber, Martina Seidl
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Journal Title
In Proc. Workshop Beyond NP 2016, The Workshops of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI Technical Report
Volume: WS-16-05
Pages: 320-326
Related Report
Peer Reviewed / Int'l Joint Research
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