Predicting the Visualization Intensity for Interactive Spatio-temporal Visual Analytics: A Data-driven View-dependent Approach
Compute unified device architecture (CUDA), Intensity prediction, Interactive visualization, Spatio-temporal data clustering
College of Natual Science and Mathematics, Geography and the Environment
The continually increasing size of geospatial data sets poses a computational challenge when conducting interactive visual analytics using conventional desktop-based visualization tools. In recent decades, improvements in parallel visualization using state-of-the-art computing techniques have significantly enhanced our capacity to analyse massive geospatial data sets. However, only a few strategies have been developed to maximize the utilization of parallel computing resources to support interactive visualization. In particular, an efficient visualization intensity prediction component is lacking from most existing parallel visualization frameworks. In this study, we propose a data-driven view-dependent visualization intensity prediction method, which can dynamically predict the visualization intensity based on the distribution patterns of spatio-temporal data. The predicted results are used to schedule the allocation of visualization tasks. We integrated this strategy with a parallel visualization system deployed in a compute unified device architecture (CUDA)-enabled graphical processing units (GPUs) cloud. To evaluate the flexibility of this strategy, we performed experiments using dust storm data sets produced from a regional climate model. The results of the experiments showed that the proposed method yields stable and accurate prediction results with acceptable computational overheads under different types of interactive visualization operations. The results also showed that our strategy improves the overall visualization efficiency by incorporating intensity-based scheduling.
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Li, Jing, et al. “Predicting the Visualization Intensity for Interactive Spatio-Temporal Visual Analytics: a Data-Driven View-Dependent Approach.” International Journal of Geographical Information Science : IJGIS, vol. 31, no. 1, 2017, pp. 168–189. doi: 10.1080/13658816.2016.1194424.