SalienTime: User-driven Selection of Salient Time Steps for Large-Scale Geospatial Data Visualization
<|reference_start|>SalienTime: User-driven Selection of Salient Time Steps for Large-Scale Geospatial Data Visualization: The voluminous nature of geospatial temporal data from physical monitors and simulation models poses challenges to efficient data access, often resulting in cumbersome temporal selection experiences in web-based data portals. Thus, selecting a subset of time steps for prioritized visualization and pre-loading is highly desirable. Addressing this issue, this paper establishes a multifaceted definition of salient time steps via extensive need-finding studies with domain experts to understand their workflows. Building on this, we propose a novel approach that leverages autoencoders and dynamic programming to facilitate user-driven temporal selections. Structural features, statistical variations, and distance penalties are incorporated to make more flexible selections. User-specified priorities, spatial regions, and aggregations are used to combine different perspectives. We design and implement a web-based interface to enable efficient and context-aware selection of time steps and evaluate its efficacy and usability through case studies, quantitative evaluations, and expert interviews.<|reference_end|>
@article{chen2024salientime:,
    title={SalienTime: User-driven Selection of Salient Time Steps for Large-Scale
  Geospatial Data Visualization},
    author={Juntong Chen, Haiwen Huang, Huayuan Ye, Zhong Peng, Chenhui Li,
  Changbo Wang},
    journal={arXiv preprint arXiv:2403.03449},
    year={2024},
    doi={10.1145/3613904.3642944},
    archivePrefix={arXiv},
    eprint={2403.03449},
    primaryClass={cs.HC cs.LG}
}