Literature DB >> 34314358

A Survey on Visual Analysis of Event Sequence Data.

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


  1 in total

1.  Mining sequential patterns with flexible constraints from MOOC data.

Authors:  Wei Song; Wei Ye; Philippe Fournier-Viger
Journal:  Appl Intell (Dordr)       Date:  2022-03-23       Impact factor: 5.086

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.