A Declarative Specification for Authoring Metrics Dashboards

Despite their ubiquity, authoring dashboards for metrics reporting in modern data analysis tools remains a manual, time-consuming process. Rather than focusing on interesting combinations of their data, users have to spend time creating each chart in a dashboard one by one. This makes dashboard creation slow and tedious. We conducted a review of production metrics dashboards and found that many dashboards contain a common structure: breaking down one or more metrics by different dimensions. In response, we developed a high-level specification for describing dashboards as sections of metrics repeated across the same dimensions and a graphical interface, Quick Dashboard, for authoring dashboards based on this specification. We present several usage examples that demonstrate the flexibility of this specification to create various kinds of dashboards and support a data-first approach to dashboard authoring.Comment: To appear at Visual Data Science (VDS) Symposium at IEEE VIS 202

mage: Fluid Moves Between Code and Graphical Work in Computational Notebooks

We aim to increase the flexibility at which a data worker can choose the right tool for the job, regardless of whether the tool is a code library or an interactive graphical user interface (GUI). To achieve this flexibility, we extend computational notebooks with a new API mage, which supports tools that can represent themselves as both code and GUI as needed. We discuss the design of mage as well as design opportunities in the space of flexible code/GUI tools for data work. To understand tooling needs, we conduct a study with nine professional practitioners and elicit their feedback on mage and potential areas for flexible code/GUI tooling. We then implement six client tools for mage that illustrate the main themes of our study findings. Finally, we discuss open challenges in providing flexible code/GUI interactions for data workers

Topic-Relevance Map: Visualization for Improving Search Result Comprehension

Peer reviewe

Immersive Insights: A Hybrid Analytics System for Collaborative Exploratory Data Analysis

In the past few years, augmented reality (AR) and virtual reality (VR) technologies have experienced terrific improvements in both accessibility and hardware capabilities, encouraging the application of these devices across various domains. While researchers have demonstrated the possible advantages of AR and VR for certain data science tasks, it is still unclear how these technologies would perform in the context of exploratory data analysis (EDA) at large. In particular, we believe it is important to better understand which level of immersion EDA would concretely benefit from, and to quantify the contribution of AR and VR with respect to standard analysis workflows. In this work, we leverage a Dataspace reconfigurable hybrid reality environment to study how data scientists might perform EDA in a co-located, collaborative context. Specifically, we propose the design and implementation of Immersive Insights, a hybrid analytics system combining high-resolution displays, table projections, and augmented reality (AR) visualizations of the data. We conducted a two-part user study with twelve data scientists, in which we evaluated how different levels of data immersion affect the EDA process and compared the performance of Immersive Insights with a state-of-the-art, non-immersive data analysis system.Comment: VRST 201

Augmenting Exploratory Data Analysis with Visualization Recommendation

Thesis (Ph.D.)--University of Washington, 2018Exploratory data analysis is one of the key activities for understanding and discovering new insights from data. As exploratory data analysis can involve both open-ended exploration and focused question answering, analysis tool should facilitate both exploration breadth and analysis depth. However, existing data exploration tools typically require manual chart specification, which can be tedious and prevent analysts from rapidly exploring different aspects of the data. Moreover, analysts may be blindsided by their own cognitive biases and prematurely fixate on specific questions or hypotheses. Without discipline and time, analysts may overlook important insights in the data, such as potentially confounding factors and data quality issues, and produce inaccurate results in their analyses. To help analyst perform rapid and systematic data exploration, this dissertation presents the design of mixed-initiative systems that complement manual chart specification with chart recommendation. To better understand the practice and challenges of exploratory data analysis, we first conduct an interview study with 18 data analysts. From the interview data, we characterize the goals, process, and challenges of exploratory data analysis. We then identify design opportunities for exploratory analysis tools. One major opportunity is facilitating rapid and systematic exploration with automation and guidance. The rest of the dissertation addresses this opportunity by contributing a stack of systems to augment exploratory analysis tools with chart recommendation. At the foundations of this stack, we introduce new formal languages for chart specification and recommendation. The Vega-Lite visualization grammar provides a formal representation for specifying and reasoning about charts. Building on Vega-Lite, the CompassQL query language combines partial chart specification with recommendation directives to provide a generalizable framework for chart recommendation via queries over the space of visualizations. Based on these foundations, we used the iterative design process to develop and study new recommendation-powered visual data exploration tools. Voyager enables data exploration via browsing of recommended charts, while allowing users to steer the recommendations by selecting data fields and transformations. Our user study, which compares Voyager with a traditional chart authoring tool, indicates the complementary benefits of manual authoring and recommendation browsing. Inspired by the study result, Voyager~2 blends manual and automated chart authoring in a single tool to facilitate rapid and systematic data exploration while preserving users' flexibility to directly author a broad range of charts. All of these systems have been released as open-source projects and adopted by both research and professional data science communities