How "Applied" is Fifteen Years of VAST conference?

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Lei Shi, Lei Xia, Zipeng Liu, Ye Sun, Huijie Guo, Klaus Mueller

 Room: 104

2023-10-25T05:21:00ZGMT-0600Change your timezone on the schedule page
2023-10-25T05:21:00Z

Exemplar figure, described by caption below — The dynamics of the number of VAST (non-)application papers and their penetration rates.

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Keywords

visual analytics, VAST, application

Abstract

Visual analytics (VA) science and technology emerge as a promising methodology in visualization and data science in the new century. Application-driven research continues to contribute significantly to the development of VA, as well as in a broader scope of VIS. However, existing studies on the trend and impact of VA/VIS application research stay at a commentary and subjective level, using methods such as panel discussions and expert interviews. On the contrary, this work presents a first study on VA application research using data-driven methodology with cutting-edge machine learning algorithms, achieving both objective and scalable goals. Experiment results demonstrate the validity of our method with high F1 scores up to 0.89 for the inference of VA application papers on both the expert-labeled benchmark dataset and two external validation data sources. Inference results on 15 years of VAST conference papers also narrate interesting patterns in VA application research's origin, trend, and constitution.