Variability in Data Visualization: a Software Product Line Approach

Abstract

Data visualization aims to effectively communicate quantitative information by understanding which techniques and displays work better for different circumstances and why. There are a variety of software solutions capable of generating a multitude of different visualizations of the same dataset. However, data visualization ex poses a large space of visual configurations depending on the type of data to be visualized, the different displays (e.g., scatter plots, line graphs, pie charts), the visual components to encode the data (e.g., lines, dots, bars), or the specific visual attributes of those com ponents (e.g., color, shape, size, length). Researchers and developers are not usually aware about best practices in data visualization, and they are required to learn about both the design practices that make communication effective and the low level details of the specific software tool used to generate the visualization. This paper pro poses a software product line approach to model and materialize the variability of the visualization design process, guided by fea ture models. We encode the visualization knowledge regarding the best design practices, resolve the variability following a step-wise configuration approach, and then evaluate our proposal for a spe cific software visualization tool. Our solution helps researchers and developers communicate their quantitative results effectively by assisting them in the selection and generation of the visualizations that work best for each case. We open a new window of research where data visualization and variability meet each other.