Visual Progression Analysis of Event Sequence Data

Abstract

Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identification and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a significant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help define those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2, which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2: (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design.

A survey of visual analytics techniques for machine learning

“…These threads are represented as segmented linear stripes, and a line map metaphor is used to reveal the changes between different stages. Later, EventThread was extended to overcome the limitation of the fixed length of each stage [201]. The authors proposed an unsupervised stage analysis algorithm to effectively identify the latent stages in event sequences.…”

A visual analytics system for multi-model comparison on clinical data predictions

“…These sets of information help clinicians make their decision with confidence. Guo et al [17] created a scalable interface to aggregate event sequence records of patients based on the RNN model they devised. Wang et al [49] produced a matrix of small multiples to visually reason about feature attributes (i.e., attention values of their RNN model) as well as a time sequence view to make comparisons.…”

Bombalytics: Visualization of Competition and Collaboration Strategies of Players in a Bomb Laying Game

“…Taking an even more general view, Guo et al [GXZ*18] segmented event sequences into groups of fixed‐length time intervals with similar segments being grouped into clusters to help better understand progression patterns within the sequence data. Subsequent work by Guo et al [GJG*19] utilizes this approach within a larger visual analytics system and relaxes the fixed‐width time interval restriction of the original approach. Chen et al [CPYQ18], also dealing with progression analysis, extracted and visualized frequently occurring subsequences.…”