2012 Fiscal Year Final Research Report
Research on Knowledge Discovery on Documents in Spatiotemporal Document Streams
| Project/Area Number |
23700124
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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 |
Media informatics/Database
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| Research Institution | Hiroshima City University |
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Principal Investigator |
TAMURA Keiichi 広島市立大学, 情報科学研究科, 准教授 (80347616)
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| Project Period (FY) |
2011 – 2012
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| Keywords | テキストマイニング / 文書ストリーム / 情報検索 / ソーシャルメディア / 並列分散処理 |
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| Research Abstract |
This study has developed a novel method for extracting spatiotemporal social events and hot topics in a spatiotemporal document stream. In this study, a mathematical model for spatiotemporal document stream is defined. Spatiotemporal social events and hot topics can be extracted by using the location-based burst detection algorithm. To extract bursts for topics, the clustering-based burst detection algorithm is proposed. Moreover, the parallelization method for burst detection algorithm, which is developed in this study, archives the speed-up of extracting bursts on the large-scale spatiotemporal document streams.
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Research Products
(16 results)
