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008181129s2017 ||| | | | eng d
020 ▼a 9780438098213
035 ▼a (MiAaPQ)AAI10901880
035 ▼a (MiAaPQ)OhioLINK:osu1512051705269695
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 004
1001 ▼a Wei, Tzu-Hsuan.
24510 ▼a Query-Driven Analysis and Visualization for Large-Scale Scientific Dataset using Geometry Summarization and Bitmap Indexing.
260 ▼a [S.l.] : ▼b The Ohio State University., ▼c 2017
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2017
300 ▼a 158 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Adviser: Han-Wei Shen.
5021 ▼a Thesis (Ph.D.)--The Ohio State University, 2017.
520 ▼a 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
520 ▼a 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
590 ▼a School code: 0168.
650 4 ▼a Computer science.
690 ▼a 0984
71020 ▼a The Ohio State University. ▼b Computer Science and Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0168
791 ▼a Ph.D.
792 ▼a 2017
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000306 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자