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

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. Create "virtual nodes" that innovatively makes direct "mouse click" on data items possible in parallel coordinates visualization.Refine the classification of visual interactions into a four-layer model.The new approach can handle visualization and interaction with extremely large dataset.

[1]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[2]  Jarke J. van Wijk,et al.  The value of visualization , 2005, VIS 05. IEEE Visualization, 2005..

[3]  Hong Zhou,et al.  Visual Clustering in Parallel Coordinates , 2008, Comput. Graph. Forum.

[4]  Ramana Rao,et al.  The Hyperbolic Browser: A Focus + Context Technique for Visualizing Large Hierarchies , 1996, J. Vis. Lang. Comput..

[5]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[6]  John T. Stasko,et al.  Mental Models, Visual Reasoning and Interaction in Information Visualization: A Top-down Perspective , 2010, IEEE Transactions on Visualization and Computer Graphics.

[7]  Harri Siirtola,et al.  Interacting with parallel coordinates , 2006, Interact. Comput..

[8]  Daniel A. Keim,et al.  Visual Analytics: Definition, Process, and Challenges , 2008, Information Visualization.

[9]  Matthew O. Ward,et al.  XmdvTool: integrating multiple methods for visualizing multivariate data , 1994, Proceedings Visualization '94.

[10]  John T. Stasko,et al.  Toward a Deeper Understanding of the Role of Interaction in Information Visualization , 2007, IEEE Transactions on Visualization and Computer Graphics.

[11]  Manojit Sarkar,et al.  Graphical fisheye views , 1994, CACM.

[12]  Usama M. Fayyad,et al.  Data mining and KDD: Promise and challenges , 1997, Future Gener. Comput. Syst..

[13]  Ben Shneiderman,et al.  The eyes have it: a task by data type taxonomy for information visualizations , 1996, Proceedings 1996 IEEE Symposium on Visual Languages.

[14]  Peter Eades,et al.  Journal of Graph Algorithms and Applications Navigating Clustered Graphs Using Force-directed Methods , 2022 .

[15]  Helwig Hauser,et al.  Angular brushing of extended parallel coordinates , 2002, IEEE Symposium on Information Visualization, 2002. INFOVIS 2002..

[16]  Haim Levkowitz,et al.  Uncovering Clusters in Crowded Parallel Coordinates Visualizations , 2004, IEEE Symposium on Information Visualization.

[17]  Jesse S. Jin,et al.  Parallel Rough Set: Dimensionality Reduction and Feature Discovery of Multi-Dimensional Data in Visualization , 2011, ICONIP.

[18]  Marc Green,et al.  Toward a Perceptual Science of Multidimensional Data Visualization : Bertin and Beyond , 1998 .

[19]  H. Edelsbrunner,et al.  Efficient algorithms for agglomerative hierarchical clustering methods , 1984 .

[20]  Jean-Daniel Fekete,et al.  Excentric Labeling: Dynamic Neighborhood Labeling for Data Visualization , 2003 .

[21]  Rocky Ross,et al.  Mental models , 2004, SIGA.

[22]  Matthew O. Ward,et al.  Visual Hierarchical Dimension Reduction for Exploration of High Dimensional Datasets , 2003, VisSym.

[23]  Fujio Yamaguchi,et al.  Curves and Surfaces in Computer Aided Geometric Design , 1988, Springer Berlin Heidelberg.

[24]  Heidi Lam,et al.  A Framework of Interaction Costs in Information Visualization , 2008, IEEE Transactions on Visualization and Computer Graphics.

[25]  Pierre Michaud,et al.  Clustering techniques , 1997, Future Gener. Comput. Syst..

[26]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

[27]  Alfred Inselberg,et al.  The plane with parallel coordinates , 1985, The Visual Computer.

[28]  K. Thankavel,et al.  CLUSTERING TECHNIQUE , 2011 .

[29]  J. J. van Wijk.  The value of visualization , 2005 .

[30]  Xiaoru Yuan,et al.  Interactive local clustering operations for high dimensional data in parallel coordinates , 2010, 2010 IEEE Pacific Visualization Symposium (PacificVis).

[31]  Hans-Peter Kriegel,et al.  'Circle Segments': A Technique for Visually Exploring Large Multidimensional Data Sets , 1996 .