Visualization of large scale time-varying scientific data

Abstract

New challenges for scientists have emerged in the past several years as the size of data generated from simulations has experienced an exponential growth. One major factor that is contributing to the growth of data size is the increasingly widespread ability to perform very large scale time-varying simulations. To analyze complex dynamic phenomena from a timevarying data set, it is necessary to navigate and browse the data in both the spatial and temporal domains, select data at different resolutions, experiment with different visualization parameters, and compute and animate selected features over a period of time. In this paper, we present several algorithms for visualizing large scale time-varying Scientific data including: (1) Lossless spatio-temporal data encoding and indexing schemes allowing for interactive visualization of time-varying data at arbitrary spatial and temporal scales. (2) Coherence based accelerated visulaization algorithms (3) Time-varying feature enhancement and tracking algorithms. Our goal is to minimize visualization computation cost, to minimize data transfer (network transmit and disk I/O) cost, to maximize the user's ability to interrogate time-varying data in different spatial and temporal scales, and to detect and track important time-varying features.