Visually Abstracting Event Sequences as Double Trees Enriched with Category-Based Comparison

Computer Graphics Forum, 2023

Abstract:

Abstract Event 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 double trees 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.

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To cite: Cedric Krause, Shivam Agarwal, Michael Burch, Fabian Beck, "Visually Abstracting Event Sequences as Double Trees Enriched with Category-Based Comparison" Computer Graphics Forum, 2023. 10.1111/cgf.14805

BibTeX:

@article{Krause2023Visually,
author = {Krause, Cedric and Agarwal, Shivam and Burch, Michael and Beck, Fabian},
title = {Visually Abstracting Event Sequences as Double Trees Enriched with Category-Based Comparison},
journal = {Computer Graphics Forum},
volume = {42},
number = {6},
pages = {},
year = {2023},
doi = {10.1111/cgf.14805},
abstract = {Abstract Event 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 double trees 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.}
}