NetScatter: Visual analytics of multivariate time series with a hybrid of dynamic and static variable relationships

Bao D.Q. Nguyen, Rattikorn Hewett, Tommy Dang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The ability to capture common characteristics among complex multi-variate time series variables can profoundly impact big data analytics in uncovering valuable insights into the relationships among them and enabling a dimensionality reduction technique. Two widely used data displays include time series and scatter plots. While the former focuses on the dynamics over time, the latter deals with static relationships among variables. Motivated by these distinctive perspectives, our research aims to maximally utilize the information captured by both at the same time. This paper presents NetScatter, a visual analytic approach to characterizing changes of pairwise relationships in a high-dimensional time series. Unlike most traditional techniques that employ a single perspective of the visual display, our approach combines static perspectives of two variables in multi-variate time series into a single representation by comparing all data instances over two different time steps. The paper also introduces a list of visual features of the representation to capture how overall data evolve. We have implemented a web-based prototype that supports a full range of operations, such as ranking, filtering, and details on demand. The paper illustrates the proposed approach on data of various sizes in different domains to demonstrate its benefits.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 14th Pacific Visualization Symposium, PacificVis 2021
PublisherIEEE Computer Society
Pages51-60
Number of pages10
ISBN (Electronic)9781665439312
DOIs
StatePublished - Apr 2021
Event14th IEEE Pacific Visualization Symposium, PacificVis 2021 - Virtual, Tianjin, China
Duration: Apr 19 2021Apr 22 2021

Publication series

NameIEEE Pacific Visualization Symposium
Volume2021-April
ISSN (Print)2165-8765
ISSN (Electronic)2165-8773

Conference

Conference14th IEEE Pacific Visualization Symposium, PacificVis 2021
Country/TerritoryChina
CityVirtual, Tianjin
Period04/19/2104/22/21

Keywords

  • High-dimensional data
  • Human-centered computing
  • Time series analysis
  • Visual analytics

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