InkSight: Leveraging Sketch Interaction for Documenting Chart Findings in Computational Notebooks.

Computational notebooks have become increasingly popular for exploratory data analysis due to their ability to support data exploration and explanation within a single document. Effective documentation for explaining chart findings during the exploration process is essential as it helps recall and share data analysis. However, documenting chart findings remains a challenge due to its time-consuming and tedious nature. While existing automatic methods alleviate some of the burden on users, they often fail to cater to users' specific interests. In response to these limitations, we present InkSight, a mixed-initiative computational notebook plugin that generates finding documentation based on the user's intent. InkSight allows users to express their intent in specific data subsets through sketching atop visualizations intuitively. To facilitate this, we designed two types of sketches, i.e., open-path and closed-path sketch. Upon receiving a user's sketch, InkSight identifies the sketch type and corresponding selected data items. Subsequently, it filters data fact types based on the sketch and selected data items before employing existing automatic data fact recommendation algorithms to infer data facts. Using large language models (GPT-3.5), InkSight converts data facts into effective natural language documentation. Users can conveniently fine-tune the generated documentation within InkSight. A user study with 12 participants demonstrated the usability and effectiveness of InkSight in expressing user intent and facilitating chart finding documentation.

Why Change My Design: Explaining Poorly Constructed Visualization Designs with Explorable Explanations.

Although visualization tools are widely available and accessible, not everyone knows the best practices and guidelines for creating accurate and honest visual representations of data. Numerous books and articles have been written to expose the misleading potential of poorly constructed charts and teach people how to avoid being deceived by them or making their own mistakes. These readings use various rhetorical devices to explain the concepts to their readers. In our analysis of a collection of books, online materials, and a design workshop, we identified six common explanation methods. To assess the effectiveness of these methods, we conducted two crowdsourced studies (each with N=125) to evaluate their ability to teach and persuade people to make design changes. In addition to these existing methods, we brought in the idea of Explorable Explanations, which allows readers to experiment with different chart settings and observe how the changes are reflected in the visualization. While we did not find significant differences across explanation methods, the results of our experiments indicate that, following the exposure to the explanations, the participants showed improved proficiency in identifying deceptive charts and were more receptive to proposed alterations of the visualization design. We discovered that participants were willing to accept more than 60% of the proposed adjustments in the persuasiveness assessment. Nevertheless, we found no significant differences among different explanation methods in convincing participants to accept the modifications.

Explaining With Examples: Lessons Learned From Crowdsourced Introductory Description of Information Visualizations.

Data visualizations have been increasingly used in oral presentations to communicate data patterns to the general public. Clear verbal introductions of visualizations to explain how to interpret the visually encoded information are essential to convey the takeaways and avoid misunderstandings. We contribute a series of studies to investigate how to effectively introduce visualizations to the audience with varying degrees of visualization literacy. We begin with understanding how people are introducing visualizations. We crowdsource 110 introductions of visualizations and categorize them based on their content and structures. From these crowdsourced introductions, we identify different introduction strategies and generate a set of introductions for evaluation. We conduct experiments to systematically compare the effectiveness of different introduction strategies across four visualizations with 1,080 participants. We find that introductions explaining visual encodings with concrete examples are the most effective. Our study provides both qualitative and quantitative insights into how to construct effective verbal introductions of visualizations in presentations, inspiring further research in data storytelling.

A Design Space for Applying the Freytag's Pyramid Structure to Data Stories.

Data stories integrate compelling visual content to communicate data insights in the form of narratives. The narrative structure of a data story serves as the backbone that determines its expressiveness, and it can largely influence how audiences perceive the insights. Freytag's Pyramid is a classic narrative structure that has been widely used in film and literature. While there are continuous recommendations and discussions about applying Freytag's Pyramid to data stories, little systematic and practical guidance is available on how to use Freytag's Pyramid for creating structured data stories. To bridge this gap, we examined how existing practices apply Freytag's Pyramid by analyzing stories extracted from 103 data videos. Based on our findings, we proposed a design space of narrative patterns, data flows, and visual communications to provide practical guidance on achieving narrative intents, organizing data facts, and selecting visual design techniques through story creation. We evaluated the proposed design space through a workshop with 25 participants. Results show that our design space provides a clear framework for rapid storyboarding of data stories with Freytag's Pyramid.

CloudDet: Interactive Visual Analysis of Anomalous Performances in Cloud Computing Systems.

Detecting and analyzing potential anomalous performances in cloud computing systems is essential for avoiding losses to customers and ensuring the efficient operation of the systems. To this end, a variety of automated techniques have been developed to identify anomalies in cloud computing. These techniques are usually adopted to track the performance metrics of the system (e.g., CPU, memory, and disk I/O), represented by a multivariate time series. However, given the complex characteristics of cloud computing data, the effectiveness of these automated methods is affected. Thus, substantial human judgment on the automated analysis results is required for anomaly interpretation. In this paper, we present a unified visual analytics system named CloudDet to interactively detect, inspect, and diagnose anomalies in cloud computing systems. A novel unsupervised anomaly detection algorithm is developed to identify anomalies based on the specific temporal patterns of the given metrics data (e.g., the periodic pattern). Rich visualization and interaction designs are used to help understand the anomalies in the spatial and temporal context. We demonstrate the effectiveness of CloudDet through a quantitative evaluation, two case studies with real-world data, and interviews with domain experts.