title: DecisionFlow: Visual Analytics for High-Dimensional Temporal Event Sequence Data.
abstract: Temporal event sequence data is increasingly commonplace, with applications ranging from electronic medical records to financial transactions to social media activity. Previously developed techniques have focused on low-dimensional datasets (e.g., with less than 20 distinct event types). Real-world datasets are often far more complex. This paper describes DecisionFlow, a visual analysis technique designed to support the analysis of high-dimensional temporal event sequence data (e.g., thousands of event types). DecisionFlow combines a scalable and dynamic temporal event data structure with interactive multi-view visualizations and ad hoc statistical analytics. We provide a detailed review of our methods, and present the results from a 12-person user study. The study results demonstrate that DecisionFlow enables the quick and accurate completion of a range of sequence analysis tasks for datasets containing thousands of event types and millions of individual events.
authors.forename: David
authors.initials: D
authors.lastname: Gotz
authors.forename: Harry
authors.initials: H
authors.lastname: Stavropoulos
mesh_terms: D000328:Adult; D016208:Databases, Factual; D003658:Decision Making, Computer-Assisted; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D008297:Male; D008490:Medical Informatics / Q000379:methods*; D008962:Models, Theoretical; D013647:Task Performance and Analysis; D014584:User-Computer Interface