Malik, S., Du, F., Plaisant, C., Bjarnadottir, M., Shneiderman, B.
April 2015
To appear in ACM Transactions on Interactive Intelligent Systems (2015)
Cohort comparison studies have been traditionally hypothesis-driven and conducted with carefully controlled environment (such as clinical trials). Given two groups of event sequence data, researchers test a single hypothesis (e.g., does the group taking Medication A exhibit more deaths and earlier deaths than the group taking Medication B?). However, researchers are now moving towards more exploratory methods and retrospective analysis of existing data. High-Volume Hypothesis Testing (HVHT) becomes useful to compare datasets. Focusing on event sequences we propose new thechniques that provide context, effect, and flexibility during HVHT, and aid researchers in understanding HVHT results (how significant they are, why they are meaningful, and whether the entire dataset has been exhaustively explored). Using interviews and case studies with domain experts, we iteratively designed and implemented techniques dealing with prevalence, time, and frequency in a visual analytics tool, CoCo. These interaction techniques allow users to systematically and flexibly parse large result sets through filtering, searching, and journaling. We illustrate the utility of the method with a case study in the medical domain.
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