Visualization recommendation systems make understanding data more accessible to users of all skill levels by automatically generating visualizations for users to explore. However, most existing visualization recommendation systems focus on ranking all possible visualizations based on the attributes or encodings, which makes it difficult to find the most interesting or relevant insights. We therefore introduce a novel class of visualization recommendation systems that automatically rank and recommend both groups of related insights and the most important insights within each group. Our approach combines results across different learning-based methods to discover insights automatically and generalizes to a variety of attribute types (e.g., categorical, numerical, and temporal), including non-trivial combinations of these attribute types. We then implemented a new insight-centric visualization recommendation system, SpotLight, which ranks annotated visualizations in visual insight groups. Finally, we conducted a user study which showed that users are able to quickly understand and find relevant insights in unfamiliar data.

Modern organizations amass their datasets into centralized repositories called data lakes, affording analytics as needed. The resultant scale and complexity of these data lakes, however, can make data navigation and monitoring challenging for users. We present DataCockpit, a Python toolkit that leverages datasets, usage logs, and associated meta-data to provision data usage and quality characteristics. DataCockpit computes these characteristics for each attribute (e.g., number of times it was queried for subsequent use in downstream applications) and record (e.g., number of non-missing, valid values) and aggregates them at the level of datasets. We develop a visual monitoring tool, powered by DataCockpit, and demonstrate how it can assist data / system administrators as well as end-users to effectively navigate and monitor a data lake. DataCockpit and the monitoring tool are available as open source software for developers to build custom monitoring applications on top of data lakes.

Analyzing data and creating effective visualizations often requires extensive domain expertise. For users with less experience, it can be difficult to know how to get started with exploratory data analysis (EDA) and how to approach the code. Chatbots can reduce the gap between analysis outcomes and user expectations by leveraging multi-turn conversations to provide a more natural interface between the user and computer-agent. To inform the design of future visual EDA chatbots, we conduct a survey and interview study with ten potential users. Our results suggest that users want a visual EDA chatbot that can make exploratory data analysis easier, while also augmenting their knowledge of visualization and analysis techniques. Between the initial survey and post-interview questionnaire, we saw increased optimism overall for the usefulness and anticipated analytic ease of visual EDA chatbots. Based on these results, we identify four key design guidelines: future visual EDA chatbots should (1) understand the user’s data and intent, (2) respond with useful visualizations, (3) leverage the history of the visualizations and data, and (4) produce verifiable and shareable analysis processes.

In this work, we develop a Graph Neural Network (GNN) framework for the problem of personalized visualization recommendation. The GNN-based framework first represents the large corpus of datasets and visualizations from users as a large heterogeneous graph. Then, it decomposes a visualization into its data and visual components, and then jointly models each of them as a large graph to obtain embeddings of the users, attributes (across all datasets in the corpus), and visual-configurations. From these user-specific embeddings of the attributes and visual-configurations, we can predict the probability of any visualization arising from a specific user. Finally, the experiments demonstrated the effectiveness of using graph neural networks for automatic and personalized recommendation of visualizations to specific users based on their data and visual (design choice) preferences. To the best of our knowledge, this is the first such work to develop and leverage GNNs for this problem.