Predicting the visualization intensity for interactive spatio-temporal visual analytics: a data-driven view-dependent approach

ABSTRACT

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.

KEYWORDS:

• Compute unified device architecture (CUDA)
• intensity prediction
• interactive visualization
• spatio-temporal data clustering

Acknowledgements

This work was supported by Faculty Research Fund Grant (FRF 2014), University of Denver, National Natural Science Foundation of China under Grant Number 91538102, 41271400, Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase) and 2014 Kunshan High-level Innovative and Entrepreneurial Talents Program under Grant Number 31.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the 2014 Kunshan High-level Innovative and Entrepreneurial Talents Program: [Grant Number 31], University of Denver Faculty Research Fund; Natural Science Foundation of China: [Grant Numbers 91538102 and 41271400], Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund;