ICG JKU Linz Lab Talk: Alexander Lex, University of Utah

Interactive visualization is an important part of the data science process. It enables analysts to directly interact with the data, exploring it with minimal effort. Unlike code, however, an interactive visualization session is ephemeral and can't be easily shared, revisited, or reused. Computational notebooks, such as Jupyter Notebooks, R Markdown, or Observable are a perfect match for many data science applications. They are also the most popular embodiment of Knuth's "Literate Programming", where the logic of a program is explained in natural language, figures, and equations.
In this talk, I will sketch approaches to "Literate Visualization". I will show how we can leverage provenance data of an analysis session to create well-documented and annotated visualization stories that enable reproducibility and sharing. I will also introduce work on inferring analysis goals, which allows us to understand the analysis process at a higher level. Understanding analysis goals enables us to enhance interaction capabilities and even re-used visual analysis processes. I will conclude by demonstrating how this provenance data can be leveraged to bridge between computational and interactive environments.