Provectories: Embedding-Based Analysis of Interaction Provenance Data

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 article, 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.

Automated summary

This article proposes a novel visual approach to the meta-analysis of interaction provenance, which helps to understand user behavior patterns and visual analysis strategies. By capturing user sessions as graph representations and using different types of two-dimensional embeddings, patterns for data types and analytical reasoning strategies can be extracted.