Decision-making has become a vital tool in any organization, evolving from a process based on experience and intuition to one increasingly established in data analysis. One type of specialized software for data analysis is that of visual representations for large data sets. Visual representations are critically important today as they enable effective exploration of a data set and facilitate the task of identifying patterns and drawing conclusions. Every day more decisions are made based on visual analysis through visual representations of large data sets. It is not only a quantitative but also a qualitative increase. Decisions are more critical and with more impact on society, the environment, and individuals. In this context, it is essential to develop new and better methodologies and tools that allow the visualization developer to ensure the correct functioning of visual representations and their interactions. To achieve this goal, we present a web platform that assists in visualization testing through user interactions. This platform is based on a previously published black box testing technique for information visualizations that uses regular expressions to represent the sequence of user interactions.
The result of a visualization process depends on the user’s decisions along it. With the intention of accelerating this process and guaranteeing an appropriate visualization of the data, we are looking to semi-automatize the process to help the users with the decision-making along it. To contribute to this semi-automation, it is useful to have metrics that characterize different important aspects of the visualization techniques, such as data representation visibility. Besides, scatterplots are a widely used technique to visualize scalar datasets. In this context, this work presents a metric that evaluates data representation visibility considering glyph visibility in scatterplots. We defined a metric that estimates the proportion of glyphs that will be visible regardless of the drawing order, and it depends on the number of items in the dataset, the size of the window, and the size of the glyphs that will represent the data. To define and approximate the metric, we experimented with several random datasets for which both dimensions followed a normal distribution. This metric constitutes an alternative to characterize scatterplots and collaborates in the semi-automation of the user’s decisions along the visualization process.
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