Bridging the gap between theory and practice through data- and task-based visualization recommendation systems

Abstract

Data visualization plays a critical role in data analysis and information dissemination. An essential step in the data visualization pipeline is mapping data and task requirements to appropriate visualization techniques. However, this step is prone to errors because it requires visualization creators to be familiar with a substantial body of visualization design guidelines and best practices. This dissertation aims to mitigate the challenges associated with the visualization design process by building systems and curating design guidelines that can support visualization practitioners in choosing appropriate visualization techniques based on their data and task requirements. This dissertation is composed of four parts. The first part of the dissertation presents two visualization design studies: CerebroVis and Portola. These studies led to the creation of tree and network visualizations that solve critical domain problems in medical diagnosis and cybersecurity. Through these studies, the dissertation motivates the need for resources to help visualization creators in mapping data and task requirements to visual encodings. The second part of the dissertation contributes a task abstraction framework for tree visualizations which supports visualization designers to more specifically abstract the goals of their users and better understand their data analysis needs. The framework also enables visualization researchers to systematically and exhaustively curate task-based design guidelines for tree visualizations. The third part of the dissertation contributes visualization design guidelines for glyph visualizations, timelines, and tree visualization. In addition to the guidelines, this part also discusses the challenges of curating design guidelines from existing empirical research and potential ways to overcome the challenges. Finally, the last part of the dissertation presents three data- and task-based visualization recommendation systems which put the theory into practice: GenoREC, NESTED, and MEDLEY. GenoREC is a domain-specific visualization recommendation system designed to support genomics analysts. GenoREC uses a knowledge-based approach to recommend domain-specific genomics visualization based on the common data formats and analysis tasks in genomics. NESTED supports visualization creators by recommending the appropriate tree visualization technique, corresponding interaction, and supporting widgets to analyze hierarchical data. Finally, MEDLEY presents a mixed-initiative interface that assists in dashboard composition by recommending dashboard collections (i.e., a logically grouped set of views and filtering widgets) that map to specific analytical intents. Contributions of this thesis pave the way for future work that can extend information visualization theory and bridge the gap between theory and practice by providing users with visualization recommendation tools and systems.--Author's abstract