Despite the recent popularity of visual analytics focusing on big data, little is known about how to support users that use visualization techniques to explore multi-dimensional datasets and accomplish specific tasks. Our lack of models that can assist end-users during the data exploration process has made it challenging to learn from the user's interactive and analytical process. The ability to model how a user interacts with a specific visualization technique and what difficulties they face are paramount in supporting individuals with discovering new patterns within their complex datasets. This paper introduces the notion of visualization systems understanding and modeling user interactions with the intent of guiding a user through a task thereby enhancing visual data exploration. The challenges faced and the necessary future steps to take are discussed; and to provide a working example, a grammar-based model is presented that can learn from user interactions, determine the common patterns among a number of subjects using a K-Reversible algorithm, build a set of rules, and apply those rules in the form of suggestions to new users with the goal of guiding them along their visual analytic process. A formal evaluation study with 300 subjects was performed showing that our grammar-based model is effective at capturing the interactive process followed by users and that further research in this area has the potential to positively impact how users interact with a visualization system.

Despite the significant amount of longitudinal information that Electronic Health Records (EHRs) include, modeling the progression of a particular disease is still a challenging task. Generally, when the trajectory of a cohort of patients is analyzed some patients take similar paths from/to a certain clinical disease while others take vastly different paths. Most existing approaches to analyze trajectories of a patient have focused on a specific disease with the intention of being able to predict whether a new patient will develop a similar diagnosis or not. This paper presents a novel approach to identifying the trajectory of patients by modeling the clinical path as an automata and using grammar induction to reduce the graph. The resulting reduced model represents the common trajectory for the patients without incorporating events that are specific to a particular patient. The proposed model has been tested with 5,000 patients that have sustained a mild traumatic brain injury (mTBI) and have developed post-traumatic stress disorder (PTSD). The model has produced a trajectory indicating the common clinical path that those patients take from their first mTBI to their first diagnosis of PTSD.