Data visualization guidance using a software product line approach

Abstract

Data visualization aims to convey quantitative and qualitative information effectively by determining which techniques and visualizations are most appropriate for different situations and why. Various software solutions can produce numerous visualizations of the same data set. However, data visualization encompasses a wide range of visual configurations that depend on factors such as the type of data being displayed, the different displays (e.g., scatter plots, line graphs, and pie charts), the visual components used to represent the data (e.g., lines, dots, and bars), and the specific visual attributes of those components (e.g., color, shape, size, and length). A similar problem arises when designing data tables, where the dimensionality of the data and its complexity influence the choice of the most appropriate structure (e.g., unidirectional, bidirectional). Often, this broad spectrum of configurations requires a visualization expert who knows which techniques are best for which type of data source and what is to be conveyed. Typically, researchers and developers lack knowledge of data visualization best practices and must learn the design principles that enable effective communication and the technical details of the specific software tool they use to generate visualizations. This paper proposes a software product line approach to model and realize the variability of the visualization design process, using feature models to encode knowledge about design best practices in graphs and charts. Our approach involves solving visualization design variability through a stepwise configuration process and evaluating the proposal for a specific software visualization tool. Our solution facilitates effective communication of quantitative results by helping researchers and developers select and generate the most effective visualizations for each case. This approach opens up new opportunities for research at the intersection of data visualization and variability.