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Query-Driven Analysis and Visualization for Large-Scale Scientific Dataset using Geometry Summarization and Bitmap Indexing
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Query-Driven Analysis and Visualization for Large-Scale Scientific Dataset using Geometry Summarization and Bitmap Indexing. |
| 개인저자 | Wei, Tzu-Hsuan. |
| 단체저자명 | The Ohio State University. Computer Science and Engineering. |
| 발행사항 | [S.l.]: The Ohio State University., 2017. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2017. |
| 형태사항 | 158 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438098213 |
| 학위논문주기 | Thesis (Ph.D.)--The Ohio State University, 2017. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Han-Wei Shen. |
| 요약 | The computational power of modern supercomputers grows rapidly, and it facilitates scientists to produce high-resolution datasets when simulating physical or weather models, which generate extreme scale data with multiple variables most of the t |
| 요약 | First, we focus on the problem of identifying salient features and evaluating selected features for creating a data summarization. To analyze a volumetric dataset, displaying isosurface is typically used to reveal the locations of values that th |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
