Multivariate Temporal Features in Scientific Data
Atlantic City, NJ, 19 Oct 2009
Jian Huang, University of Tennessee, Knoxville
Chaoli Wang, Michigan Technological University
Heike Jänicke, Swansea University
Jonathan Woodring, Los Alamos National Laboratory
A wave of large datasets amounting beyond terascale is now being produced by scientific applications on a daily basis. The ensuing challenge to manage and make sense of these data demands systematic breakthroughs in several areas of computer science. In this tutorial, we survey recent progresses made in addressing a particular difficulty of pressing user need. That is, the gap between users’ conceptual domain knowledge vs. the way features have to be specified in traditional scientific visualizations. This gap is particularly acute at terascale and beyond, where a large amount of parallel automation is necessary to study the data at full-scale. Interactive techniques alone cannot solve the whole problem. Algorithmic methods must be studied.
Recent research to visualize time-varying multivariate data has led to the study of several key technical hurdles. For instance, when visualization is used for exploratory purposes, it could be the case that even application scientists themselves do not fully understand the phenomena and are using visualization to start and refine their research. There are also general cases where a user has clear concepts of a pattern but the knowledge lacks specifics, as exemplified by the concept of “the start of a growing season” that is commonly studied in climate modeling. When a feature can be defined with rigor, there is still a common situation that the data cannot be viewed altogether, and that straightforward ways to generate hierarchical representations of the features do not lead to satisfactory results.
In an effort to survey recent progress in addressing the above set of diverse challenges, our tutorial covers the following topics: (i) Programming language interfaces for time-varying multivariate visualization; (ii) Purely mathematical ways to specify features for visualization; (iii) Importance-driven data analysis and visualization; (iv) Chronovolumes, comparative visualization, and time-varying transfer functions. The tutorial also demonstrates the applications of these techniques in highly visible recent application areas, such as modeling and simulation of climate, combustion, astrophysics, earthquake and hurricane. The goal of this tutorial is to inform visualization researchers and practitioners the state-of-the-art technologies that have greatly enriched the toolset for visualizing large-scale time-varying multivariate scientific data.
Introduction [PDF] (77KB)
Programming Language Interfaces for Time-Varying Multivariate Visualization [PDF] (5.1MB)
Information-Theoretic Methods for the Visual Analysis of Climate and Flow Data [PDF] (8.6MB)
Importance-Driven Data Analysis and Visualization [PDF] (18MB)
Comparative Visualization and Transfer Functions for Time-Varying Data [PDF] (24.9MB)