CAREER: Generating Hierarchical Vector-Valued Data Summaries for Scalable Flow Data Processing, Analysis and Visualization

University Of Houston, Houston TX

Investigators

Abstract

Vector fields are a ubiquitous tool to describe the behaviors of various dynamical systems that dominate many important physical phenomena, especially fluids. Their analysis is indispensable for many applications ranging from medical data analysis such as blood circulation to tsunami simulations and many other problems in science and engineering. However, processing and interpreting very large scale vector field data defined in a high dimensional space has become the bottleneck of many critical scientific research tasks. More specifically, the analysis of vector field data, that is inherently complex and formidable in size, is particularly challenging especially when visualization is limited by the finite resolution and dimensions of modern displays, and the amount of information conveyed via the visualization is constrained by the limited bandwidth of the human visual perception channel. This problem cannot be solved without a comprehensive, summary representation of vector field flow data, which has not been well studied in the flow visualization community. The proposed research will fill this gap. Theoretical contributions of this research will impact methods in computational topology, fluid mechanics, and mathematics, while its applications will benefit a wide variety of disciplines including climate study, physics, chemistry, mechanical and civil engineering, and cardiovascular disease diagnosis. The results of this work will be incorporated into new courses in the area of vector field data processing and visualization at both the undergraduate and graduate levels that will benefit students of a broad range of disciplines. A vector field is a function that assigns any spatial point a vector value describing the displacement of objects. To develop an effective summary representation for vector fields, the proposed project will first study the relations between different flow characteristics and descriptors, aiming to reduce the redundancy in the extraction of the summary. Second, a novel link-graph hybrid representation will be developed with the goal of seamlessly integrating various flow information, characterized from different perspectives and in various scales, into a dimension-independent representation. Third, based on this intermediate representation, a new and scalable vector field analysis framework will be developed, from which a hierarchical summary for vector field data can be defined. The information theoretical framework will be adapted to evaluate the information loss in the summary representation. This summary will enable a number of applications for scientific discovery and education including the scalable and knowledge-assisted exploration of flow data, vector field comparison, and vector field synthesis gaming. The knowledge obtained during this project will be adapted to study the summary representation of more complex data, such as tensor field data. More importantly, this research represents one step towards a unified framework of knowledge discovery and integrity from heterogeneous data sources. The developed theory and algorithms will be published in peer-reviewed journal and conferences. The project webpage (http://www2.cs.uh.edu/~chengu/Hier_VVDSummary/Hier_VVDSummary.html) will provide brief description of the key outcome and links or pointers to the corresponding publications and generated datasets. The developed software, libraries, plug-ins, and open source code will be released on the on the project webpage and Github.