A novel virtual node approach for interactive visual analytics of big datasets in parallel coordinates

Mao Lin Huang*, Tze-Haw Huang, Xuyun Zhang

*Corresponding author for this work

Research output: Contribution to journalArticle

9 Citations (Scopus)


Big data is a collection of large and complex datasets that commonly appear in multidimensional and multivariate data formats. It has been recognized as a big challenge in modern computing/information sciences to gain (or find out) due to its massive volume and complexity (e.g. its multivariate format). Accordingly, there is an urgent need to find new and effective techniques to deal with such huge datasets. Parallel coordinates is a well-established geometrical system for visualizing multidimensional data that has been extensively studied for decades. There is also a variety of associated interaction techniques currently used with this geometrical system. However, none of these existing techniques can achieve the functions that are covered by the Select layer of Yi's Seven-Layer Interaction Model. This is because it is theoretically impossible to find a select of data items via a mouse-click (or mouse-rollover) operation over a particular visual poly-line (a visual object) with no geometric region. In this paper, we present a novel technique that uses a set of virtual nodes to practically achieve the Select interaction which has hitherto proven to be such a challenging sphere in parallel coordinates visualization.

Original languageEnglish
Pages (from-to)510-523
Number of pages14
JournalFuture Generation Computer Systems
Publication statusPublished - Feb 2016
Externally publishedYes


  • Big data
  • Visual analytics
  • Parallel coordinates
  • Hierarchical clustering
  • Multidimensional data visualization
  • Data retrieval

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