IEEE VIS 2024 Content: Visual Analysis of Time-Stamped Event Sequences

Visual Analysis of Time-Stamped Event Sequences

Jürgen Bernard -

Clara-Maria Barth -

Eduard Cuba -

Andrea Meier -

Yasara Peiris -

Ben Shneiderman -

DOI: 10.1109/TVCG.2024.3382760

Room: Bayshore VI

2024-10-16T14:51:00ZGMT-0600Change your timezone on the schedule page
2024-10-16T14:51:00Z
Exemplar figure, described by caption below
Overview of IVESA. On the left, the Sequence Overview and Details View primarily enable the analysis of the TSEQs content, i.e., events, event sequences, groups of event sequences, motifs, and features. On the right, the Metadata View supports the analysis of metadata attributes and the TSEQs contextualization, whereas the Summary View includes the entry point to auxiliary views for filtering, motif configuration, feature analysis, and clustering.
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Keywords

Time-Stamped Event Sequences, Time-Oriented Data, Visual Analytics, Data-First Design Study, Iterative Design, Visual Interfaces, User Evaluation

Abstract

Time-stamped event sequences (TSEQs) are time-oriented data without value information, shifting the focus of users to the exploration of temporal event occurrences. TSEQs exist in application domains, such as sleeping behavior, earthquake aftershocks, and stock market crashes. Domain experts face four challenges, for which they could use interactive and visual data analysis methods. First, TSEQs can be large with respect to both the number of sequences and events, often leading to millions of events. Second, domain experts need validated metrics and features to identify interesting patterns. Third, after identifying interesting patterns, domain experts contextualize the patterns to foster sensemaking. Finally, domain experts seek to reduce data complexity by data simplification and machine learning support. We present IVESA, a visual analytics approach for TSEQs. It supports the analysis of TSEQs at the granularities of sequences and events, supported with metrics and feature analysis tools. IVESA has multiple linked views that support overview, sort+filter, comparison, details-on-demand, and metadata relation-seeking tasks, as well as data simplification through feature analysis, interactive clustering, filtering, and motif detection and simplification. We evaluated IVESA with three case studies and a user study with six domain experts working with six different datasets and applications. Results demonstrate the usability and generalizability of IVESA across applications and cases that had up to 1,000,000 events.