The Community for Technology Leaders
Visualization Symposium, IEEE Pacific (2014)
Yokohama, Japan Japan
Mar. 4, 2014 to Mar. 7, 2014
pp: 33-40
Hanqi Guo , Key Lab. of Machine Perception (Minist. of Educ.), Peking Univ., Beijing, China
Fan Hong , Key Lab. of Machine Perception (Minist. of Educ.), Peking Univ., Beijing, China
Qingya Shu , Key Lab. of Machine Perception (Minist. of Educ.), Peking Univ., Beijing, China
Jiang Zhang , Key Lab. of Machine Perception (Minist. of Educ.), Peking Univ., Beijing, China
Jian Huang , Dept. of Electr. Eng. & Comput. Sci., Univ. of Tennessee, Knoxville, TN, USA
Xiaoru Yuan , Key Lab. of Machine Perception (Minist. of Educ.), Peking Univ., Beijing, China
ABSTRACT
In this paper, we present a novel scalable approach for visualizing multivariate unsteady flow data with Lagrangian-based Attribute Space Projection (LASP). The distances between spatial temporal samples are evaluated by their attribute values along the advection directions in the flow field. The massive samples are then projected into 2D screen space for feature identification and selection. A hybrid parallel system, which tightly integrates a MapReduce-style particle tracer with a scalable algorithm for massive projection, is designed to support the large scale analysis. Results show that the proposed methods and system are capable of visualizing features in the unsteady flow, which couples multivariate analysis of vector and scalar attributes with projection.
INDEX TERMS
Data visualization, Spatiotemporal phenomena, Measurement, Complexity theory, Algorithm design and analysis, Feature extraction, Parallel processing,parallel processing, Flow visualization, attribute space projection
CITATION
Hanqi Guo, Fan Hong, Qingya Shu, Jiang Zhang, Jian Huang, Xiaoru Yuan, "Scalable Lagrangian-Based Attribute Space Projection for Multivariate Unsteady Flow Data", Visualization Symposium, IEEE Pacific, vol. 00, no. , pp. 33-40, 2014, doi:10.1109/PacificVis.2014.15
94 ms
(Ver 3.3 (11022016))