Survey on the Analysis of User Interactions and Visualization Provenance

EuroVis STAR Teaser

Abstract

There is fast-growing literature on provenance-related research, covering aspects such as its theoretical framework, use cases, and techniques for capturing, visualizing, and analyzing provenance data. As a result, there is an increasing need to identify and taxonomize the existing scholarship. Such an organization of the research landscape will provide a complete picture of the current state of inquiry and identify knowledge gaps or possible avenues for further investigation. In this STAR, we aim to produce a comprehensive survey of work in the data visualization and visual analytics field that focus on the analysis of user interaction and provenance data. We structure our survey around three primary questions: (1) WHY analyze provenance data, (2) WHAT provenance data to encode and how to encode it, and (3) HOW to analyze provenance data. A concluding discussion provides evidence-based guidelines and highlights concrete opportunities for future development in this emerging area. The survey and papers discussed can be explored online interactively at https://provenance-survey.caleydo.org.

Citation

Kai Xu, Alvitta Ottley, Conny Walchshofer, Marc Streit, Remco Chang, John Wenskovitch
Survey on the Analysis of User Interactions and Visualization Provenance
Computer Graphics Forum, EuroVis STAR 2020 (Early Access), 2020.

BibTeX

@article{2020_eurovis_anaprov,
    title = {Survey on the Analysis of User Interactions and Visualization Provenance},
    author = {Kai Xu and Alvitta Ottley and Conny Walchshofer and Marc Streit and Remco Chang and John Wenskovitch},
    journal = {Computer Graphics Forum, EuroVis STAR 2020 (Early Access)},
    publisher = {https://diglib.eg.org/handle/10.1111/cgf14035},
    year = {2020}
}

Acknowledgements

We want to thank Christina Humer for contributing to the creation of the companion website. This project was supported in part by The Boeing Company under award 2018-BRT-PA-332 and the National Science Foundation under Grant No. 1755734. This work was also supported in part by the FFG, Contract No. 854184: “Pro2Future” is funded within the Austrian COMET Program Competence Centers for Excellent Technologies under the auspices of the Austrian Federal Ministry of Transport, Innovation and Technology, the Austrian Federal Ministry for Digital and Economic Affairs and of the Provinces of Upper Austria and Styria. COMET is managed by the Austrian Research Promotion Agency FFG.