Andrea Burattin, Ph.D.

Assistant Professor

Designing Visual Decision Making Support with the Help of Eye Tracking

Abstract

Data visualizations are helpful tools to cognitively access large amounts of data and make complex relationships in data understandable. This paper shows how results from neuro-physiological measurements, more specifically eye-tracking, can support justified design decisions about improving existing data visualizations for exploring process execution data. This is achieved by gaining insight into how visualizations are used for decision-making. The presented examination is embedded in the domain of process modeling behavior analysis, and the analyses are performed on the background of representative analytical questions from the domain of process model behavior analysis. We present initial findings on one out of three visualization types we have examined, which is the Rhythm-Eye visualization.

Paper Information and Files

Published in Proceedings of 18th BPMDS working conference; Essen, Germany; June 12-13 2017.