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 article 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 conduct a user study to quantify and compare user performance for a value comparison task using four bar chart designs, where the bars show the mean values of data loaded progressively and updated every second (progressive bar charts). Progressive visualization divides different stages of the visualization pipeline-data loading, processing, and visualization-into iterative animated steps to limit the latency when loading large amounts of data. An animated visualization appearing quickly, unfolding, and getting more accurate with time, enables users to make early decisions. However, intermediate mean estimates are computed only on partial data and may not have time to converge to the true means, potentially misleading users and resulting in incorrect decisions. To address this issue, we propose two new designs visualizing the history of values in progressive bar charts, in addition to the use of confidence intervals. We comparatively study four progressive bar chart designs: with/without confidence intervals, and using near-history representation with/without confidence intervals, on three realistic data distributions. We evaluate user performance based on the percentage of correct answers (accuracy), response time, and user confidence. Our results show that, overall, users can make early and accurate decisions with 92% accuracy using only 18% of the data, regardless of the design. We find that our proposed bar chart design with only near-history is comparable to bar charts with only confidence intervals in performance, and the qualitative feedback we received indicates a preference for designs with history.

In the context of multiple views, coordination is essential to navigate and grasp the relationships lying behind the different juxtaposed views. Linked highlighting is a typical example of coordination where a subset of the data points is emphasized simultaneously on all views. The strength of this approach is that the selected data can be studied within its context. Other approaches have been used to implement coordination such as using varying levels of transparency or visual links. We propose to use spatial distortion to contribute a similar effect in multiple views. It is particularly suited to the context of multiple views since it alleviates the lack of screen space by reallocating it based on a certain definition of user interest. The proposed method targets coordination between views that represent the same entities and readily adapts to various visualization forms. It is based on a user degree-of-interest function, defined on these entities, that acts as a common ground for the distortion of all views. Views are distorted such that empty areas and areas holding entities of lesser interest are compressed to the benefit of areas holding entities of higher interest. To demonstrate its feasibility and versatility, we describe how to technically apply our approach to several common visualization techniques.