EventBox: A Novel Visual Encoding for Interactive Analysis of Temporal and Multivariate Attributes in Event Sequences

Abstract

The rapid growth and availability of event sequence data across domains requires effective analysis and exploration methods to facilitate decision-making. Visual analytics combines computational techniques with interactive visualizations, enabling the identification of patterns, anomalies, and attribute interactions. However, existing approaches frequently overlook the interplay between temporal and multivariate attributes. We introduce EventBox, a novel data representation and visual encoding approach for analyzing groups of events and their multivariate attributes. We have integrated EventBox into Sequen-C, a visual analytics system for the analysis of event sequences. To enable the agile creation of EventBoxes in Sequen-C, we have added user-driven transformations, including alignment, sorting, substitution and aggregation. To enhance analytical depth, we incorporate automatically generated statistical analyses, providing additional insight into the significance of attribute interactions. We evaluated our approach involving 21 participants (3 domain experts, 18 novice data analysts). We used the ICE-T framework to assess visualization value, user performance metrics completing a series of tasks, and interactive sessions with domain experts. We also present three case studies with real-world healthcare data demonstrating how EventBox and its integration into Sequen-C reveal meaningful patterns, anomalies, and insights. These results demonstrate that our work advances visual analytics by providing a flexible solution for exploring temporal and multivariate attributes in event sequences.