| Literature DB >> 34314358 |
Yi Guo, Shunan Guo, Zhuochen Jin, Smiti Kaul, David Gotz, Nan Cao.
Abstract
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.Year: 2021 PMID: 34314358 DOI: 10.1109/TVCG.2021.3100413
Source DB: PubMed Journal: IEEE Trans Vis Comput Graph ISSN: 1077-2626 Impact factor: 4.579