Varying Annotations in the Steps of the Visual Analysis

TL;DR

This paper investigates how step-specific annotations can enhance visual analytics by tailoring information to different analysis stages, demonstrated through a clinical ophthalmic data tool.

Contribution

It introduces a framework for designing and integrating annotations tailored to each step of visual analysis, validated with clinical data and expert feedback.

Findings

Annotations improve analysis relevance and clarity

Step-specific annotations facilitate clinical data interpretation

Expert feedback confirms annotation usefulness

Abstract

Annotations in Visual Analytics (VA) have become a common means to support the analysis by integrating additional information into the VA system. That additional information often depends on the current process step in the visual analysis. For example, the data preprocessing step has data structuring operations while the data exploration step focuses on user interaction and input. Describing suitable annotations to meet the goals of the different steps is challenging. To tackle this issue, we identify individual annotations for each step and outline their gathering and design properties for the visual analysis of heterogeneous clinical data. We integrate our annotation design into a visual analysis tool to show its applicability to data from the ophthalmic domain. In interviews and application sessions with experts we asses its usefulness for the analysis of patients with different medications.