Key Time Steps Selection for Large‐Scale Time‐Varying Volume Datasets Using an Information‐Theoretic Storyboard

Abstract

Key time steps selection is essential for effective and efficient scientific visualization of large-scale time-varying datasets. We present a novel approach that can decide the number of most representative time steps while selecting them to minimize the difference in the amount of information from the original data. We use linear interpolation to reconstruct the data of intermediate time steps between selected time steps. We propose an evaluation of selected time steps by computing the difference in the amount of information (called information difference) using variation of information (VI) from information theory, which compares the interpolated time steps against the original data. In the one-time preprocessing phase, a dynamic programming is applied to extract the subset of time steps that minimize the information difference. In the run-time phase, a novel chart is used to present the dynamic programming results, which serves as a storyboard of the data to guide the user to select the best time steps very efficiently. We extend our preprocessing approach to a novel out-of-core approximate algorithm to achieve optimal I/O cost, which also greatly reduces the in-core computing time and exhibits a nice trade-off between computing speed and accuracy. As shown in the experiments, our approximate method outperforms the previous globally optimal DTW approach [TLS12] on out-of-core data by significantly improving the running time while keeping similar qualities, and is our major contribution.

Streaming Approach to In Situ Selection of Key Time Steps for Time‐Varying Volume Data

“…The volume rendering images were produced using the VisIt [CBW * 12] ¶ In rare cases, the ZeroErrorSolution may have segments with > 2 time steps, if some volume data does not change over time, or is perfectly colinear. In-Core Data: Analysis of Efficiency Recall that our dynamic programming approach (called Our DP here) provides globally optimal solutions to the general key time steps selection problem, while the accurate dynamic programming method of [ZC18] gives globally optimal solutions to the restricted problem; we call the latter AR-DP (denoting accurate restricted DP). They are basically in-core algorithms: first read and keep the entire dataset in main memory, then perform computation without the need for additional I/O.…”

“…The dynamic time warping (DTW) method [TLS12] is an in-core DP technique to solve the restricted problem optimally, where constant interpolation is used for reconstruction. In [ZC18], the restricted problem is considered, where restricted linear interpolation is used for reconstruction. In that work, an accurate DP method is given to produce optimal solutions.…”

Diva: A Declarative and Reactive Language for In-Situ Visualization

“…Notably, recent works following this direction usually involve defining indicator functions known as "in situ triggers" for characterizing features. Those triggers can be domain-agnostic algorithms (e.g., data reductions and aggregations, statistical and machine learning techniques [6,25,29,33,54]), or domain-specific routines that require special knowledge from domain-experts [9,39,45,53]. This approach has the potential to automatically extract all interesting phenomena from the simulation if the triggers are welldesigned.…”

Memento: Localized Time‐Warping for Spatio‐Temporal Selection

“…The generated contours depict where and when non‐continuous changes occur or spatial bounds are present in a single summary overview, using colour to encode temporal information. Other recent work by Zhou and Chiang [ZC18] proposed a key time step selection mechanism for time‐varying volumetric datasets, based on information theoretical measures. Recent work by Schmidt et al .…”

“…Computer Graphics Forum published by Eurographics -The European Association for Computer Graphics and John Wiley & Sons Ltd single summary overview, using colour to encode temporal information. Other recent work by Zhou and Chiang [ZC18] proposed a key time step selection mechanism for time-varying volumetric datasets, based on information theoretical measures. Recent work by Schmidt et al [SFP*18] presented Popup-plots, a technique to warp temporal charts using 3D rotation.…”