Survey on Visual Analysis of Event Sequence Data
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 e...
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| Published in | IEEE transactions on visualization and computer graphics Vol. 28; no. 12; pp. 5091 - 5112 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| 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. From our review of relevant literature, we have also identified several remaining research challenges and future research opportunities. |
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| AbstractList | 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.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. 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. |
| Author | Guo, Shunan Kaul, Smiti Gotz, David Jin, Zhuochen Cao, Nan Guo, Yi |
| Author_xml | – sequence: 1 givenname: Yi surname: Guo fullname: Guo, Yi email: dennis.guo.china@gmail.com organization: Intelligent Big Data Visualization Lab, Tongji University, Shanghai, China – sequence: 2 givenname: Shunan orcidid: 0000-0001-5355-8399 surname: Guo fullname: Guo, Shunan email: g.shunan@gmail.com organization: Intelligent Big Data Visualization Lab, Tongji University, Shanghai, China – sequence: 3 givenname: Zhuochen surname: Jin fullname: Jin, Zhuochen email: chjzcjames@gmail.com organization: Intelligent Big Data Visualization Lab, Tongji University, Shanghai, China – sequence: 4 givenname: Smiti orcidid: 0000-0001-6435-0497 surname: Kaul fullname: Kaul, Smiti email: smiti@unc.edu organization: Visual Analysis and Communication Lab, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA – sequence: 5 givenname: David orcidid: 0000-0002-6424-7374 surname: Gotz fullname: Gotz, David email: gotz@unc.edu organization: Visual Analysis and Communication Lab, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA – sequence: 6 givenname: Nan orcidid: 0000-0003-1316-7515 surname: Cao fullname: Cao, Nan email: nan.cao@gmail.com organization: Intelligent Big Data Visualization Lab, Tongji University, Shanghai, China |
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| Snippet | 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... |
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| SubjectTerms | Data mining Data visualization Electronic health records Event detection event sequences Literature reviews Mathematical analysis Medical diagnostic imaging Sequences State-of-the-art reviews Visual analysis Visual analytics visualization |
| Title | Survey on Visual Analysis of Event Sequence Data |
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