  Event sequence data record series of discrete events in the time order of
occurrence. They are commonly observed in a variety of applications ranging
from electronic health records to network logs, with the characteristics of
large-scale, high-dimensional, and heterogeneous. This high complexity of event
sequence data makes it difficult for analysts to manually explore and find
patterns, resulting in ever-increasing needs for computational and perceptual
aids from visual analytics techniques to extract and communicate insights from
event sequence datasets. In this paper, we review the state-of-the-art visual
analytics approaches, characterize them with our proposed design space, and
categorize them based on analytical tasks and applications. From our review of
relevant literature, we have also identified several remaining research
challenges and future research opportunities.
