AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization



Aoyu Wu, Yun Wang, Xinhuan Shu, Dominik Moritz, Weiwei Cui, Haidong Zhang, Dongmei Zhang, Huamin Qu

 View presentation:2022-10-20T14:00:00ZGMT-0600Change your timezone on the schedule page
2022-10-20T14:00:00Z

Exemplar figure, described by caption below — We present a survey on AI for visualizations from a corpus of 98 papers spanning 10 different fields in computer science. We argue that visualizations are becoming a new data format (visualization data) and categorize the surveyed papers from three aspects: (A) What is the visualization data and its representation; (B) Why apply AI to visualization data; and (C) How to apply AI to visualization data, where we outline seven tasks and separately discuss corresponding AI approaches.

Prerecorded Talk

The live footage of the talk, including the Q&A, can be viewed on the session page, ML for VIS.

Fast forward

Keywords

Survey; Data Visualization; Artificial Intelligence; Data Format; Machine Learning

Abstract

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 artificial intelligence (AI) techniques. In this survey, we probe the underlying vision of formalizing visualizations as an emerging data format and review the recent advance in applying AI techniques to visualization data (AI4VIS). We define visualization data as the digital representations of visualizations in computers and focus on data visualization (e.g., charts and infographics). We build our survey upon a corpus spanning ten different fields in computer science with an eye toward identifying important common interests. Our resulting taxonomy is organized around WHAT is visualization data and its representation, WHY and HOW to apply AI to visualization data. We highlight a set of common tasks that researchers apply to the visualization data and present a detailed discussion of AI approaches developed to accomplish those tasks. Drawing upon our literature review, we discuss several important research questions surrounding the management and exploitation of visualization data, as well as the role of AI in support of those processes.