Salient time steps selection from large scale time-varying data sets with dynamic time warping

Empowered by rapid advance of high performance computer architectures and software, it is now possible for scientists to perform high resolution simulations with unprecedented accuracy. Nowadays, the total size of data from a large-scale simulation can easily exceed hundreds of terabytes or even petabytes, distributed over a large number of time steps. The sheer size of data makes it difficult to perform post analysis and visualization after the computation is completed. Frequently, large amounts of valuable data produced from simulations are discarded, or left in disk unanalyzed. In this paper, we present a novel technique that can retrieve the most salient time steps, or key time steps, from large scale time-varying data sets. To achieve this goal, we develop a new time warping technique with an efficient dynamic programming scheme to map the whole sequence into an arbitrary number of time steps specified by the user. A novel contribution of our dynamic programming scheme is that the mapping between the whole time sequence and the key time steps is globally optimal, and hence the information loss is minimum. We propose a high performance algorithm to solve the dynamic programming problem that makes the selection of key times run in real time. Based on the technique, we create a visualization system that allows the user to browse time varying data at arbitrary levels of temporal detail. Because of the low computational complexity of this algorithm, the tool can help the user explore time varying data interactively and hierarchically. We demonstrate the utility of our algorithm by showing results from different time-varying data sets.