We introduce a conceptual model for scalability designed for visualization research. With this model, we systematically analyze over 120 visualization publications from 1990 to 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.
We describe the workflow followed by historians when conducting a Historical Social Network Analysis (HSNA) with five steps: textual sources acquisition, digitization, annotation, network creation, and analysis/visualization. While most analysis and visualization tools only support the last step, we argue that addressing the 2-3 last steps would boost the humanists' analytical capabilities. We explain why the network modeling process is particularly challenging and can lead to distortions of the sources, biases, and traceability problems. We list three main properties that we believe the constructed network should satisfy: alignment with reality/documents (not only with concepts), traceability (from documents to analysis/visualization and back), and simplicity (understandable by most and not more complex than needed). We claim that the model of bipartite dynamic multivariate network with roles allows an effective annotation/encoding of historical sources while satisfying these properties. We provide real-world examples of how this model has been used to answer socio-historical questions using visual analytics tools.
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