Spline-based Decomposition of Streamed Particle Trajectories for Efficient Transfer and Analysis

Abstract

We introduce an approach for distributed processing and efficient storage of noisy particle trajectories, and present visual analysis techniques that directly operate on the generated representation. For efficient storage, we decompose individual trajectories into a smooth representation and a high frequency part. Our smooth representation is generated by fitting Hermite Splines to a series of time windows, adhering to a certain error bound. This directly supports scenarios involving in situ and streaming data processing. We show how the individually fitted splines can afterwards be combined into one spline posessing the same mathematical properties, i.e. C1 continuity as well as our error bound. The fitted splines are typically significantly smaller than the original data, and can therefore be used, e.g., for an online monitoring and analysis of distributed particle simulations. The high frequency part can be used to reconstruct the original data, or could also be discarded in scenarios with limited storage capabilities. Finally, we demonstrate the utility of our smooth representation for different analysis queries using real world data.