Streaming Approach to In Situ Selection of Key Time Steps for Time‐Varying Volume Data. (June 2022)

• Record Type: Journal Article
• Authors: Wu, Mengxi; Chiang, Yi‐Jen; Musco, Christopher
• Is Part Of: Computer graphics forum. Volume 41:Number 3(2022)
• Page Start: 309
• Page End: 320
• Publication Date: 2022-06
• Subjects: Algorithms -- Temporal Data -- Scalar Field Data -- Large‐Scale Data Techniques -- Key Time Steps Selection; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.14542
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: Key time steps selection, i.e., selecting a subset of most representative time steps, is essential for effective and efficient scientific visualization of large time‐varying volume data. In particular, as computer simulations continue to grow in size and complexity, they often generate output that exceeds both the available storage capacity and bandwidth for transferring results to storage, making it indispensable to save only a subset of time steps. At the same time, this subset must be chosen so that it is highly representative, to facilitate post‐processing and reconstruction with high fidelity. The key time steps selection problem is especially challenging in the in situ setting, where we can only process data in one pass in an online streaming fashion, using a small amount of main memory and fast computation. In this paper, we formulate the problem as that of optimal piece‐wise linear interpolation . We first apply a method from numerical linear algebra to compute linear interpolation solutions and their errors in an online streaming fashion. Using that method as a building block, we can obtain a global optimal solution for the piece‐wise linear interpolation problem via a standard dynamic programming (DP) algorithm. However, this approach needs to process the time steps in multiple passes and is too slow for the in situ setting. To address this issue, we introduce a novel approximation algorithm, which processes time steps in one pass in an online streaming fashion, with very efficient computing time and main memory space both in theory and in practice. The algorithm is suitable for the in situ setting. Moreover, we prove that our algorithm, which is based on a greedy update rule, has strong theoretical guarantees on the approximation quality and the number of time steps stored. To the best of our knowledge, this is the first algorithm suitable for in situ key time steps selection with such theoretical guarantees, and is the main contribution of this paper. Experiments demonstrate the efficacy of our new techniques.