Data visualization is crucial in today's data-driven business world, which has been widely
used for helping decision making that is closely related to major revenues of many industrial …

Visualizations themselves have become a data format. Akin to other data formats such as
text and images, visualizations are increasingly created, stored, shared, and (re-) used with …

With the continuous and vast increase in the amount of data in our digital world, it has been
acknowledged that the number of knowledgeable data scientists can not scale to address …

Visual data analysis involves both open-ended and focused exploration. Manual chart
specification tools support question answering, but are often tedious for early-stage …

Machine learning has become an essential tool for gleaning knowledge from data and
tackling a diverse set of computationally hard tasks. However, the accuracy of a machine …

Data visualization is invaluable for explaining the significance of data to people who are
visually oriented. The central task of automatic data visualization is, given a dataset, to …

Rapidly creating effective visualizations using expressive grammars is challenging for users
who have limited time and limited skills in statistics and data visualization. Even high-level …

Supporting exploratory visual analysis (EVA) is a central goal of visualization research, and
yet our understanding of the process is arguably vague and piecemeal. We contribute a …

There is fast‐growing literature on provenance‐related research, covering aspects such as
its theoretical framework, use cases, and techniques for capturing, visualizing, and …

Statistical knowledge and domain expertise are key to extract actionable insights out of data,
yet such skills rarely coexist together. In Machine Learning, high-quality results are only …