Volume 42 (2023)
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Browsing Volume 42 (2023) by Author "Agarwal, Shivam"
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Item VisCoMET: Visually Analyzing Team Collaboration in Medical Emergency Trainings(The Eurographics Association and John Wiley & Sons Ltd., 2023) Liebers, Carina; Agarwal, Shivam; Krug, Maximilian; Pitsch, Karola; Beck, Fabian; Bujack, Roxana; Archambault, Daniel; Schreck, TobiasHandling emergencies requires efficient and effective collaboration of medical professionals. To analyze their performance, in an application study, we have developed VisCoMET, a visual analytics approach displaying interactions of healthcare personnel in a triage training of a mass casualty incident. The application scenario stems from social interaction research, where the collaboration of teams is studied from different perspectives. We integrate recorded annotations from multiple sources, such as recorded videos of the sessions, transcribed communication, and eye-tracking information. For each session, an informationrich timeline visualizes events across these different channels, specifically highlighting interactions between the team members. We provide algorithmic support to identify frequent event patterns and to search for user-defined event sequences. Comparing different teams, an overview visualization aggregates each training session in a visual glyph as a node, connected to similar sessions through edges. An application example shows the usage of the approach in the comparative analysis of triage training sessions, where multiple teams encountered the same scene, and highlights discovered insights. The approach was evaluated through feedback from visualization and social interaction experts. The results show that the approach supports reflecting on teams' performance by exploratory analysis of collaboration behavior while particularly enabling the comparison of triage training sessions.Item Visually Abstracting Event Sequences as Double Trees Enriched with Category‐Based Comparison(© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Krause, Cedric; Agarwal, Shivam; Burch, Michael; Beck, Fabian; Hauser, Helwig and Alliez, PierreEvent sequence visualization aids analysts in many domains to better understand and infer new insights from event data. Analysing behaviour before or after a certain event of interest is a common task in many scenarios. In this paper, we introduce, formally define, and position as a domain‐agnostic tree visualization approach for this task. The visualization shows the sequences that led to the event of interest as a tree on the left, and those that followed on the right. Moreover, our approach enables users to create selections based on event attributes to interactively compare the events and sequences along colour‐coded categories. We integrate the double tree and category‐based comparison into a user interface for event sequence analysis. In three application examples, we show a diverse set of scenarios, covering short and long time spans, non‐spatial and spatial events, human and artificial actors, to demonstrate the general applicability of the approach.