Variability in data visualization: a software product line approach

Rui Ming Xiong Rafael F. Toledo J. Atlee

6 Oktober 2024

Analyses of a software product line (SPL) model typically report variable results annotated with logical expressions to indicate the set of products for which each result holds. These expressions can be complicated and difficult to reason about when the SPL has lots of features and derivable products. In previous work, we introduced Neo4j Browser, a visualizer for graphical analysis results that highlights the results that apply to specific products. In this paper, we report on a controlled user study that evaluates the visualizer's effectiveness in helping the user (1) understand variable analysis results and (2) compare the analysis results of multiple products. Our findings suggest significant improvements in efficiency (30%), and correctness (22%), and significant reductions in cognitive load (42%).

Xinyi Liu Zhicheng Liu Hannah K. Bako + 1 lainnya

26 September 2022

Examples are useful for inspiring ideas and facilitating implementation in visualization design. However, there is little understanding of how visualization designers use examples, and how computational tools may support such activities. In this paper, we contribute an exploratory study of current practices in incorporating visualization examples. We conducted semi-structured interviews with 15 university students and 15 professional designers. Our analysis focus on two core design activities: searching for examples and utilizing examples. We characterize observed strategies and tools for performing these activities, as well as major challenges that hinder designers' current workflows. In addition, we identify themes that cut across these two activities: criteria for determining example usefulness, curation practices, and design fixation. Given our findings, we discuss the implications for visualization design and authoring tools and highlight critical areas for future research.

Haidong Zhang Dongmei Zhang Yun Wang + 5 lainnya

2 Februari 2021

Visualizations themselves have become a data format. Akin to other data formats such as text and images, visualizations are increasingly created, stored, shared, and (re-)used with artificial intelligence (AI) techniques. In this survey, we probe the underlying vision of formalizing visualizations as an emerging data format and review the recent advance in applying AI techniques to visualization data (AI4VIS). We define visualization data as the digital representations of visualizations in computers and focus on data visualization (e.g., charts and infographics). We build our survey upon a corpus spanning ten different fields in computer science with an eye toward identifying important common interests. Our resulting taxonomy is organized around WHAT is visualization data and its representation, WHY and HOW to apply AI to visualization data. We highlight a set of common tasks that researchers apply to the visualization data and present a detailed discussion of AI approaches developed to accomplish those tasks. Drawing upon our literature review, we discuss several important research questions surrounding the management and exploitation of visualization data, as well as the role of AI in support of those processes. We make the list of surveyed papers and related material available online at.

Zezhong Wang Lovisa Sundin Dave Murray-Rust + 1 lainnya

18 Januari 2020

This paper introduces the concept of 'cheat sheets' for data visualization techniques, a set of concise graphical explanations and textual annotations inspired by infographics, data comics, and cheat sheets in other domains. Cheat sheets aim to address the increasing need for accessible material that supports a wide audience in understanding data visualization techniques, their use, their fallacies and so forth. We have carried out an iterative design process with practitioners, teachers and students of data science and visualization, resulting six types of cheat sheet (anatomy, construction, visual patterns, pitfalls, false-friends and well-known relatives) for six types of visualization, and formats for presentation. We assess these with a qualitative user study using 11 participants that demonstrates the readability and usefulness of our cheat sheets.

Xinyue Xu F. Sun Zui Chen + 3 lainnya

23 Agustus 2021

Despite the rising popularity of automated visualization tools, existing systems tend to provide direct results which do not always fit the input data or meet visualization requirements. Therefore, additional specification adjustments are still required in real-world use cases. However, manual adjustments are difficult since most users do not necessarily possess adequate skills or visualization knowledge. Even experienced users might create imperfect visualizations that involve chart construction errors. We present a framework, VizLinter, to help users detect flaws and rectify already-built but defective visualizations. The framework consists of two components, (1) a visualization linter, which applies well-recognized principles to inspect the legitimacy of rendered visualizations, and (2) a visualization fixer, which automatically corrects the detected violations according to the linter. We implement the framework into an online editor prototype based on Vega-Lite specifications. To further evaluate the system, we conduct an in-lab user study. The results prove its effectiveness and efficiency in identifying and fixing errors for data visualizations.