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

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.

Network-based visualisation of frequent sequences

“…The use of network theory in the case of frequent sequence pattern post-processing supports the identification of relevant event types, such as unifying and polarising events [17]. Existing visualisation techniques focus on the interpretation of well-defined processes, for example, in [18] and in [19]. The main difference between our approach and existing ones is that the proposed method does not aim to visualise one process model but rather to identify the different and common elements of parallel process models.…”