Complexity Estimation for Feature Tracking Data

• Published in: 2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV) pp. 100 - 101
• Main Authors: Helmrich, Dirk N., Schnorr, Andrea, Kuhlen, Torsten W., Hentschel, Bernd
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2018
• Subjects: Analytical models; Complexity theory; Data models; Human-centered computing; Numerical models; Prediction algorithms; Predictive models; Silver; Visualization; Visualization design and evaluation methods
• DOI: 10.1109/LDAV.2018.8739231

Figure 1.Left: Synthetic Voronoi data set to mimick the behavior of dissipation elements. Points are seeded randomly inside this data set and each volume cell is labeled with its cell center. These are colored by cell center ID. Middle: Dissipation element data set, colored by ID Right: Dissipation element data set, colored by predicted complexity of the problemFeature tracking is a method of time-varying data analysis. Due to the complexity of the underlying problem, different feature tracking algorithms have different levels of correctness in certain use cases. However, there is no efficient way to evaluate their performance on simulation data since there is no ground-truth easily obtainable. Synthetic data is a way to ensure a minimum level of correctness, though there are limits to their expressiveness when comparing the results to simulation data. To close this gap, we calculate a synthetic data set and use its results to extract a hypothesis about the algorithm performance that we can apply to simulation data.