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HCIL-2016-04

Mauriello, M., Shneiderman, B., Du, F., Malik, S., Plaisant, C.
Simplifying Overviews of Temporal Event Sequences
Mauriello, M. L., Shneiderman, B., Du, F., Malik, S., Plaisant, C., Simplifying Overviews of Temporal Event Sequences, Extended Abstracts on Human Factors in Computing Systems, CHI '16 (2016) to appear
HCIL-2016-04

Beginning the analysis of new data is often difficult as modern datasets can be overwhelmingly large. With visual analytics in particular, displays of large datasets quickly become crowded and unclear. Through observing the practices of analysts working with the event sequence visualization tool EventFlow, we identified three techniques to reduce initial visual complexity by reducing the number of event categories resulting in a simplified overview. For novice users, we suggest an initial pair of event categories to display. For advanced users, we provide six ranking metrics and display all pairs in a ranked list. Finally, we present the Event Category Matrix (ECM), which simultaneously displays overviews of every event category pair. In this work, we report on the development of these techniques through two formative usability studies and the improvements made as a result. The goal of our work is to investigate strategies that help users overcome the challenges associated with initial visual complexity and to motivate the use of simplified overviews in temporal event sequence analysis.



User Interface and Visualization for Electronic Health Records: SharpC at Maryland Screenshot

User Interface and Visualization for Electronic Health Records: SharpC at Maryland
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