You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems

Visual query systems (VQSs) empower users to interactively search for line charts with desired visual patterns, typically specified using intuitive sketch-based interfaces. Despite decades of past work on VQSs, these efforts have not translated to adoption in practice, possibly because VQSs are largely evaluated in unrealistic lab-based settings. To remedy this gap in adoption, we collaborated with experts from three diverse domains---astronomy, genetics, and material science---via a year-long user-centered design process to develop a VQS that supports their workflow and analytical needs, and evaluate how VQSs can be used in practice. Our study results reveal that ad-hoc sketch-only querying is not as commonly used as prior work suggests, since analysts are often unable to precisely express their patterns of interest. In addition, we characterize three essential sensemaking processes supported by our enhanced VQS. We discover that participants employ all three processes, but in different proportions, depending on the analytical needs in each domain. Our findings suggest that all three sensemaking processes must be integrated in order to make future VQSs useful for a wide range of analytical inquiries.Comment: Accepted for presentation at IEEE VAST 2019, to be held October 20-25 in Vancouver, Canada. Paper will also be published in a special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG) IEEE VIS (InfoVis/VAST/SciVis) 2019 ACM 2012 CCS - Human-centered computing, Visualization, Visualization design and evaluation method

Assisting data exploration via in-situ adaptive visualizations

Visual analytics has been widely used by data scientists to shed light on complex problems. Despite the prevalence of many visual analytics tools that empower human decision making with data-driven insights, challenges still exist that hinder users from genuinely capitalizing on insights from visualizations. The two biggest challenges we identify are the lack of task support and disconnected workflow. Visual analytics tools lack task support because they do not actively suggest insights to the users, requiring users to pick each individual step during exploration manually. These tools also suffer from disconnected workflows by keeping interactive exploration via dashboards separate from data preparation and cleaning tools like computational notebooks. To address these challenges, we introduce Lux, a visualization recommendation library that automatically generates useful insights for data exploration, and seamlessly integrates into a user’s data exploration workflow by augmenting the Pandas library. In this thesis, we document the design decisions made and the implementation details of Lux as well as how users can easily unlock intelligent analytical capabilities by adding our library to their code. Furthermore, we share how predecessor visual analytics tools such as Zenvisage that we contributed to guided the development of Lux

Enabling effective visual data exploration for solvent discovery in material science

Data visualization has become increasingly important in almost all scientific fields. However, current visual analytics tools usually require redundant manual processing, resulting in the visualization process remaining overwhelming and error-prone. Zenvisage automates the process of querying for desired visual patterns, thereby speeding up visual exploration. In this work, we collaborate with material scientists, whose goal is to identify battery solvents with favorable properties while considering economical, physical and chemical tradeoffs in their manufacture. We extend Zenvisage to allow material scientists to compare among subsets of data dynamically and employ non-line chart visualizations to explore their data. In this thesis, we introduce the notion of dynamic class creation, which targets the seamless creation of subsets of data and comparison of properties among them. We address the non-time-series data issue by conducting visual property search queries directly on scatter plots. We implemented polygon-bound queries and drag-and-drop queries for scatter plots, along with two similarity metrics. We also introduce a new approach for material scientists to upload their datasets using scripts. Our work would enable material scientists to get insights more quickly on increasingly large datasets

Towards a scripting language for visual data exploration

Visualization of data is becoming increasingly important for data analysis; numerous insights can be derived from visualizations at a glance. While the information provided through visualization is powerful, the visual analysis process remains largely manual, consuming valuable man-hours. Zenvisage automates the search for desired visual patterns, speeding up visual exploration. It does so through a query language, ZQL, that encapsulates the key data analysis operations such as comparison, sorting, filtering, and composition. However, Zenvisage is somewhat lacking in support for expert data analysts, who are comfortable with programming in existing data analytics tools and may not want to switch to a new system. In order to bring our tool to such users, we propose the Zenvisage Scripting Language, which the power of provides the same expressive power as ZQL but also integrates directly into an expert user’s programming language (in our case, we use ggplot and R as a case study). This would allow users to easily adapt their workflows to support visual exploration-based insigh

A Multi-Faceted Approach for Evaluating Visualization Recommendation Algorithms

Data visualizations allow analysts to quickly understand data trends, outliers, and patterns. However, designing the "best" visualizations for a given dataset is complicated. Multiple factors need to be considered, such as the data size, data types, target analysis tasks being supported, and even how the visualization needs to be personalized to the audience. In response, many visualization recommendation algorithms are being proposed to reduce user effort by automatically making some or all of these design decisions for analysts. However, existing visualization recommendation algorithms are evaluated in isolation, or the comparisons do not measure user performance. In other words, existing algorithms are not tested in a way that aligns with how they are used in practice. The lack of evaluation approaches makes it impossible to know how functional an algorithm is compared to another across various analysis tasks, hindering our ability to design new algorithms that provide significantly more benefits than the existing ones.This dissertation contributes a multi-faceted approach for evaluating visualization recommendation algorithms to investigate factors affecting an algorithm's performance and ways to improve it. It first proposes an evaluation-focused framework and then demonstrates how the framework can evaluate strategic behaviors and user performance among a broad range of existing algorithms. The case study results show that newly proposed algorithms might not significantly improve user performance. One way to improve the algorithm performance is by integrating more established theoretical rules or empirical results on how people perceive different visualization designs, i.e., graphical perception guidelines, to guide the recommendation ranking process. Thus, this dissertation next presents a thorough literature review of existing graphical perception literature that can inform visualization recommendation algorithms. It contributes a systematic dataset that collates existing theoretical and experimental visualization comparison results and summarizes key study outcomes. Further, this dissertation conducts an exploratory analysis to investigate the influence of each piece of graphical perception study in changing a visualization recommendation algorithm's behavior and outputs. The analysis results show that some graphical perception studies can alter the behavior of visualization recommendation algorithms dominantly, while others have little influence. Based on the analysis findings, this dissertation opens avenues at the intersection of graphical perception and visualization research, like how to evaluate the effectiveness of new graphical perception work in guiding visualization recommendations