ICE: Identify and Compare Event Sequence Sets through Multi-Scale Matrix and Unit Visualizations
This work addresses the problem of comparative analysis of event sequence data for analysts in domains like website design and medical care, presenting an incremental improvement in visualization techniques.
The paper tackles the challenge of identifying and comparing event sequence sets in complex data by introducing ICE, an interactive visualization tool that enables analysts to explore datasets and compare sequences at pattern and sequence levels, demonstrated with three real-world datasets.
Comparative analysis of event sequence data is essential in many application domains, such as website design and medical care. However, analysts often face two challenges: they may not always know which sets of event sequences in the data are useful to compare, and the comparison needs to be achieved at different granularity, due to the volume and complexity of the data. This paper presents, ICE, an interactive visualization that allows analysts to explore an event sequence dataset, and identify promising sets of event sequences to compare at both the pattern and sequence levels. More specifically, ICE incorporates a multi-level matrix-based visualization for browsing the entire dataset based on the prefixes and suffixes of sequences. To support comparison at multiple levels, ICE employs the unit visualization technique, and we further explore the design space of unit visualizations for event sequence comparison tasks. Finally, we demonstrate the effectiveness of ICE with three real-world datasets from different domains.