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2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
ISSN: 2375-9356
ISBN: 978-1-4673-8795-8
pp: 157-162
Dongeon Lee , Department of Internet & Multimedia Engineering, Konkuk University, Seoul, Korea, The Republic of
HyungSeok Kim , Department of Internet & Multimedia Engineering, Konkuk University, Seoul, Korea, The Republic of
Mingyu Lim , Department of Internet & Multimedia Engineering, Konkuk University, Seoul, Korea, The Republic of
ABSTRACT
With the advance of biological research, it is possible and necessary to simulate metabolism on whole body scale such as MCMT (multi-component, multi-target) reaction mechanism which brings around 100millions to billions network data size. For this kind of data, it is essential to provide real-time visualization for analyzing the data. In this paper, we present a system that visualize body model having anatomical semantics for metabolism simulation data. We aim at real-time performance on massive scale network. The proposed method reconstruct hierarchical model from the massive reaction mechanism data. With the hierarchical model, the system filters and correlates the complex information to visualize in real-time and in optimized form for analysis. The proposed hierarchical model is composed of spatial information for navigation and the semantic information for analysis. We propose a zoom-able visualization interface by combining a set of structured classification of attributes of the data in a hierarchical structure. A prototype is implemented with real metabolism simulation data. The effectiveness of the proposed approach is illustrated through a performance analysis of the prototype implementation.
INDEX TERMS
Data visualization, Semantics, Biological systems, Real-time systems, Data models, Biochemistry
CITATION

Dongeon Lee, HyungSeok Kim and Mingyu Lim, "A hierarchical model for real-time massive reaction visualization based on anatomical semantics," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 157-162.
doi:10.1109/BIGCOMP.2016.7425815
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