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IEEE International Conference on Big Data and Smart computing (BigComp)

作者： Ma, Mengyu Wu, Ye Chen, Luo Li, Jun Jing, Ning Du, Chun Natl Univ Def Technol Coll Elect Sci Changsha Peoples R China

ISBN: (纸本)9781728160344

In the big data era, real-time analysis of large-scale spatial data remains a challenging problem since the computational scales of conventional data-driven methods expand rapidly with data volumes. This paper presents display-driven computing (DisDC), a new computing model for interactive analysis of large-scale spatial vector data. The computing units in DisDC are screen pixels for display rather than spatial objects in traditional data-driven methods, and the core task of DisDC is to judge the spatial relationships between pixels and spatial objects. As the number of pixels for display is stable, DisDC is less sensitive to data volumes and can be used to provide real-time data analysis of large-scale spatial vector data. Experiments show that DisDC is capable of handling billion-scale spatial vector data, and an online demonstration is provided (https://***/MemoryMmy/DisDC).

关键词： display-driven computing real-time big data parallel computing

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HiVision: Rapid visualization of large-scale spatial vector data

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COMPUTERS & GEOSCIENCES 2021年 147卷 104665-104665页

作者： Ma, Mengyu Wu, Ye Ouyang, Xue Chen, Luo Li, Jun Jing, Ning Natl Univ Def Technol Coll Elect Sci Changsha 410073 Peoples R China

Rapid visualization of large-scale spatial vector data is a long-standing challenge in Geographic Information Science. In existing methods, the computation overheads grow rapidly with data volumes, leading to the incapability of providing real-time visualization for large-scale spatial vector data, even with parallel acceleration technologies. To fill the gap, we present HiVision, a display-driven visualization model for large-scale spatial vector data. Different from traditional data-driven methods, the computing units in HiVision are pixels rather than spatial objects to achieve real-time performance, and efficient spatial-index-based strategies are introduced to estimate the topological relationships between pixels and spatial objects. HiVision can maintain exceedingly good performance regardless of the data volume due to the stable pixel number for display. In addition, an optimized parallel computing architecture is proposed in HiVision to ensure the ability of real-time visualization. Experiments show that our approach outperforms traditional methods in rendering speed and visual effects while dealing with large-scale spatial vector data, and can provide interactive visualization of datasets with billion-scale points/segments/edges in real-time with flexible rendering styles. The HiVision code is open-sourced at https.//***/MemoryMmy/HiVision with an online demonstration.

关键词： Vector data visualization Big data display-driven computing Parallel computing Real-time

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