Provectories: Embedding-based Analysis of Interaction Provenance Data

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Conny Walchshofer, Andreas Hinterreiter, Kai Xu, Holger Stitz, Marc Streit

 View presentation:2022-10-20T21:09:00ZGMT-0600Change your timezone on the schedule page
2022-10-20T21:09:00Z

Exemplar figure, described by caption below — Cluster-based analysis using t-SNE on the example of theoutlier cluster dataset, performing a multiple-user investigation. Distinct clusters (Cluster A--H) can be observed for outlier selections and superimposed trajectories, which indicates that data points were selected performed in the same a similar sequence by multiple users. The ground truth is indicated in orange

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Keywords

Visualization techniques, Information visualization, Visual analytics, Interaction Provenance, Sensemaking

Abstract

Understanding user behavior patterns and visual analysis strategies is a long-standing challenge. Existing approaches rely largely on time-consuming manual processes 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 the 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 captured in two user studies. From our visualizations, we were able to extract patterns for data types and analytical reasoning strategies.