Corpus ID: 220055635

Survey on Visual Analysis of Event Sequence Data

@article{Guo2020SurveyOV,
  title={Survey on Visual Analysis of Event Sequence Data},
  author={Y. Guo and Shunan Guo and Zhuochen Jin and Smiti Kaul and David Gotz and Nan Cao},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.14291}
}

Y. Guo, Shunan Guo, +3 authors Nan Cao

Published 2020

Computer Science

ArXiv

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… CONTINUE READING

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