We present a novel visual representation and interface named the matrix of isosurface similarity maps (MISM) for effective exploration of large time-varying multivariate volumetric data sets. MISM synthesizes three types of similarity maps (i.e., self, temporal, and variable similarity maps) to capture the essential relationships among isosurfaces of different variables and time steps. Additionally, it serves as the main visual mapping and navigation tool for examining the vast number of isosurfaces and exploring the underlying time-varying multivariate data set. We present temporal clustering, variable grouping, and interactive filtering to reduce the huge exploration space of MISM. In conjunction with the isovalue and isosurface views, MISM allows users to identify important isosurfaces or isosurface pairs and compare them over space, time, and value range. More importantly, we introduce path recommendation that suggests, animates, and compares traversal paths for effectively exploring MISM under varied criteria and at different levels-of-detail. A silhouette-based method is applied to render multiple surfaces of interest in a visually succinct manner. We demonstrate the effectiveness of our approach with case studies of several time-varying multivariate data sets and an ensemble data set, and evaluate our work with two domain experts.
is a fourth-yeard PhD candidate at the University of Notre Dame working with Dr. Chaoli Wang on High Performance Computing and Scientific Visualization. His main research focus is summarization and reconstruction of big data using GPU-acceleration and deep learning techniques. He has applied his research in isosurface selection for volume visualization and analysis, graph visualization, and is currently using deep learning techniques for analysis of unsteady flow simulations. He has completed a research internship at Argonne National Laboratory in summer 2018. He received his BSc (2014) and MSc (2016) in Software Engineering at the Vienna University of Technology. During his Master's program, he conducted research at the VRVis Research Center in Vienna and continued acquiring experience during a research internship at the University of California, Irvine.
Miss Wenqing Chang, Xi'an Jiaotong UniversityWenqing Chang is currently a senior student in Information Engineering from Xi'an Jiaotong University. In 2018, she joined NUS Summer Workshop, developing a 2D webpage game using WebGL and rendering 3D animation using OpenGL. From the fall of 2018 to present, she is a lab researcher in wireless communication, built ambient backscatter enabled secondary communication model and right now is involved in deep learning for joint source-channel coding.
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