ModelSpace: Visualizing the Trails of Data Models in Visual Analytics Systems

User interactions with visualization systems have been shown to encode a great deal of information about the the users’ thinking processes, and analyzing their interaction trails can teach us more about the users, their approach, and how they arrived at insights. This deeper understanding is critical to improving their experience and outcomes, and there are tools available to visualize logs of interactions. It can be difficult to determine the structurally interesting parts of interaction data, though, like what set of button clicks constitutes an action that matters. In the case of visual analytics systems that use machine learning models, there is a convenient marker of when the user has significantly altered the state of the system via interaction: when the model is updated based on new information. We present a method for numerical analytic provenance using high-dimensional visualization to show and compare the trails of these sequences of model states of the system. We evaluate this approach with a prototype tool, ModelSpace, applied to two case studies on experimental data from model-steering visual analytics tools. ModelSpace reveals individual user’s progress, the relationships between their paths, and the characteristics of certain regions of the space of possible models.