In Situ Adaptive Spatio-Temporal Data Summarization

Scientists nowadays use data sets generated from large-scale scientific computational simulations to understand the intricate details of various physical phenomena. These simulations produce large volumes of data at a rapid pace, containing thousands of time steps so that the spatiotemporal dynamics of the modeled phenomenon and its associated features can be captured with sufficient detail. Storing all the time steps into disks to perform traditional offline analysis will soon become prohibitive as the gap between the data generation speed and disk I/O speed continues to increase. In situ analysis, i.e., in-place analysis of data when it is being produced, has emerged as a solution to this problem. In this work, we present an information-theoretic approach for in situ reduction of large-scale time-varying data sets via a combination of key and fused time steps. We show that this approach can greatly minimize the output data storage footprint while preserving the temporal evolution of data features. A detailed in situ application study is carried out to demonstrate the in situ viability of our technique for efficiently summarizing thousands of time steps generated from a large-scale real-life computational simulation code.