Sequen-C: A Multilevel Overview of Temporal Event Sequences

Jessica Magallanes, Tony Stone, Paul Morris, Suzanne Mason, Steven Wood, Maria-Cruz Villa-Uriol

View presentation:2021-10-28T17:30:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
Screenshots of Sequen-C for a dataset with 258 event sequences illustrating the methodology. The hierarchical aggregation tree (top left) allows changing the number of clusters shown. The vertical level-of-detail of the multilevel overview can be transformed from coarse (bottom left) to fine (middle and bottom right). Sequence clusters are represented using an Align-Score-Simplify strategy (top right), which allows controlling the horizontal level-of-detail according to an information score.
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Building a visual overview of temporal event sequences with an optimal level-of-detail (i.e. simplified but informative) is an ongoing challenge - expecting the user to zoom into every important aspect of the overview can lead to missing insights. We propose a technique to build and explore a multilevel overview of event sequences, from coarse to fine vertical or horizontal level-of-detail, using hierarchical aggregation and a novel cluster data representation Align-Score-Simplify. By default, the overview shows an optimal number of sequence clusters obtained through the average silhouette width metric – then users are able to explore alternative optimal sequence clusterings. The vertical level-of-detail of the overview changes along with the number of clusters, whilst the horizontal level-of-detail refers to the level of summarization applied to each cluster representation. The proposed technique has been implemented into a visualization system called Sequence Cluster Explorer (Sequen-C) that allows multilevel and detail-on-demand exploration through three coordinated views, and the inspection of data attributes at cluster, unique sequence, and individual sequence level. We present two case studies using real-world datasets in the healthcare domain: CUREd and MIMIC-III, which demonstrate how the technique can aid users in exploring and defining a set of distinct pathways that best summarize the dataset, while also being able of identifying deviating pathways and exploring data attributes for selected patterns.