VIVIR — VIsualizing VIew Relations — VIsual representations of VIew Relations to support effective data analysis on large and high-resolution displays.

VIVIR was a Postdoctoral Fellowship project supported by the Marie Skłodowska-Curie Actions (MSCA) from October 1, 2017 to December 31, 2021. Specifically, I was supported by the Individual Fellowship — Global Fellowships programme. The fellowship was active for more than four years due to a number of factors, such as paternity leave, COVID-19, and the timing of an international relocation of my family of five.

In VIVIR, I conducted state-of-the-art research on supporting collaborative face-to-face data analysis, motivated by the increasing need for interdisciplinary teams to collaborate on understanding and analyzing data. Additionally, as the scale and complexity of data increase, so does the demand for data-based insights and decision-making.

My approach is to empower people who are working with large and complex data, by letting them lay out as many visualization views (in the following, denoted views) as necessary on large displays, and creating specialized meta-visualizations to show relations between these views. These meta-visualizations will allow team workers to be aware of each other’s work and the changing view- and data-relationships as they work. While the potential of view meta-visualizations has been acknowledged, there are currently only a few frequently used and considered essential examples of such meta-visualizations. These might show that data in two views are compared in a third view, or that a view shows a subset of the data shown in another view. Most importantly, there has been no thorough exploration into the power and potential of meta-visualization support for data-driven decision-making. To understand the potential impact of meta-visualizations on data analysis, we need to take a structured approach, to formalize these possibilities, which will improve our abilities to support knowledge worker teams as they face the challenges of analyzing increasingly complex data.

Briefly, data can be difficult to understand. Creating visualizations of data lets people see their data more clearly. As data size and complexity increases, more views are needed to reveal the information hidden in data. It might seem to be a good solution to simply use large and/or multiple displays, since knowledge workers then have multiple views and ‘space to think’. However, a new problem is emerging – how to be aware of the data relationships, and keep an overview of analysis provenance, findings, and decisions between these multiple views and in team work – how to be aware of team members’ activities.

To tackle these problems, I examined the use of meta-visualizations in VIVIR.