Provectories: Embedding-based Analysis of Interaction Provenance Data

Provectories Teaser

Abstract

Understanding user behavior patterns and visual analysis strategies is a long-standing challenge. Existing approaches rely largely on time-consuming manualprocesses such as interviews and the analysis of observational data. While it is technically possible to capture a history of user interactions and application states, it remains difficult to extract and describe analysis strategies based on interaction provenance. In this paper, we propose a novel visual approach to meta-analysis of interaction provenance. We capture single and multiple user sessions as graphs of high-dimensional application states. Our meta-analysis is based on two different types of two-dimensional embeddings of these high-dimensional states: layouts based on (i) topology and (ii) attribute similarity. We applied these visualization approaches to synthetic and real user provenance data. From our visualizations, we were able to extract patterns for data types and analytical reasoning strategies.

Citation

BibTeX

@article{2020_preprint_provectories,
    title = {Provectories: Embedding-based Analysis of Interaction Provenance Data},
    author = {Conny Walchshofer and Andreas Hinterreiter and Kai Xu and Holger Stitz and Marc Streit},
    journal = {Preprint},
    url = {https://osf.io/mtfxn/},
    year = {2020}
}

Acknowledgements

This work was 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. Additional support was granted by the State of Upper Austria and the Austrian Federal Ministry of Education, Science and Research via the LIT – Linz Institute of Technology (LIT-2019-7-SEE-117), and by the State of Upper Austria (Human-Interpretable Machine Learning).