We introduce a conceptual model for scalability designed for visualization research. With this model, we systematically analyze over 120 visualization publications from 1990-2020 to characterize the different notions of scalability in these works. While many papers have addressed scalability issues, our survey identifies a lack of consistency in the use of the term in the visualization research community. We address this issue by introducing a consistent terminology meant to help visualization researchers better characterize the scalability aspects in their research. It also helps in providing multiple methods for supporting the claim that a work is "scalable". Our model is centered around an effort function with inputs and outputs. The inputs are the problem size and resources, whereas the outputs are the actual efforts, for instance, in terms of computational run time or visual clutter. We select representative examples to illustrate different approaches and facets of what scalability can mean in visualization literature. Finally, targeting the diverse crowd of visualization researchers without a scalability tradition, we provide a set of recommendations for how scalability can be presented in a clear and consistent way to improve fair comparison between visualization techniques and systems and foster reproducibility.
翻译:我们引入了一种为可视化研究设计的可视化概念模型。 通过这个模型,我们系统地分析了1990-2020年120多份可视化出版物,以说明这些作品的可扩缩性的不同概念。虽然许多论文讨论了可扩缩性问题,但我们的调查发现,在可视化研究界使用该术语时缺乏一致性。我们采用了一致的术语来解决这一问题,目的是帮助可视化研究人员更好地描述其研究中的可伸缩性方面。它还有助于提供多种方法来支持关于一项工作是“可扩缩性”的说法。我们的模式围绕一个带有投入和产出的工作函数进行。我们的模式是问题的规模和资源,而产出则是实际的努力,例如计算运行时间或视觉断层。我们选取有代表性的例子来说明可视化文献中可伸缩性的含义的不同方法和方面。最后,在没有可调缩放性传统的情况下,针对不同人群的可视化研究人员,我们就如何以明确和一致的方式展示可扩缩性提供了一整套建议,以改进可视化技术和系统之间的公平比较并促进可变化性。