Shaping Unlimited Patterns: A Vision for State-of-the-Art Visual Scalability

Scaling data visualizations to represent large data sets remains one of the top challenges from the last decade. The fast growing amount of data is facing limitations of human perception and of technology as bottlenecks to exploring hidden patterns. This literature review summarizes the boundaries of visual scalability and their influencing factors, addressing state-of-the-art challenges. In a semantic differential approach, contrary characteristics were studied to support the classification of low and highly scalable data visualizations. As a result, the literature supports a vision of a shift toward adapting the taxonomy of visual scalability facing emergent technologies.

[1]  Daniel A. Keim,et al.  Scalable Pixel Based Visual Data Exploration , 2006, VIEW.

[2]  J. Jacko,et al.  The human-computer interaction handbook: fundamentals, evolving technologies and emerging applications , 2002 .

[3]  Ben Shneiderman,et al.  Extreme visualization: squeezing a billion records into a million pixels , 2008, SIGMOD Conference.

[4]  Robert B. Ross,et al.  The Top 10 Challenges in Extreme-Scale Visual Analytics , 2012, IEEE Computer Graphics and Applications.

[5]  Patrick Marais,et al.  Panopticon: a scalable monitoring system , 2010, SAICSIT '10.

[6]  Theresa-Marie Rhyne,et al.  Panel 1: Can We Determine the Top Unresolved Problems of Visualization? , 2004, IEEE Visualization.

[7]  Robert Spence,et al.  Information Visualization: Design for Interaction (2nd Edition) , 2006 .

[8]  Jean-Daniel Fekete,et al.  Interactive information visualization of a million items , 2002, IEEE Symposium on Information Visualization, 2002. INFOVIS 2002..

[9]  Chaomei Chen,et al.  Top Ten Problems in Visual Interfaces to Digital Libraries , 2002, Visual Interfaces to Digital Libraries.

[10]  Chaomei Chen,et al.  Top 10 Unsolved Information Visualization Problems , 2005, IEEE Computer Graphics and Applications.

[11]  Chris North,et al.  Realizing embodied interaction for visual analytics through large displays , 2007, Comput. Graph..

[12]  Chris North,et al.  Information Visualization , 2008, Lecture Notes in Computer Science.

[13]  K. Skala,et al.  3D visual analytics for quality control in engineering , 2013, 2013 36th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[14]  Peter J. Huber,et al.  Huge Data Sets , 1994 .

[15]  Christopher Andrews,et al.  Information visualization on large, high-resolution displays: Issues, challenges, and opportunities , 2011, Inf. Vis..

[16]  Daniel A. Keim,et al.  Visual analytics for the big data era — A comparative review of state-of-the-art commercial systems , 2012, 2012 IEEE Conference on Visual Analytics Science and Technology (VAST).

[17]  Christian Sandor,et al.  Visual analytics in Augmented Reality , 2013, ISMAR.

[18]  John T. Stasko,et al.  The Science of Interaction , 2009, Inf. Vis..

[19]  Oliver Bieh-Zimmert,et al.  Representing Multidimensional Cancer Registry Data , 2013, i-Know '13.

[20]  David S. Ebert,et al.  Scale and Complexity in Visual Analytics , 2009, Inf. Vis..

[21]  M. Sheelagh T. Carpendale,et al.  Evaluating Information Visualizations , 2008, Information Visualization.

[22]  E. Wes Bethel,et al.  Sort-first, distributed memory parallel visualization and rendering , 2003, IEEE Symposium on Parallel and Large-Data Visualization and Graphics, 2003. PVG 2003..

[23]  Daniel A. Keim,et al.  Visual Analytics: Combining Automated Discovery with Interactive Visualizations , 2008, ALT.

[24]  James J. Thomas,et al.  Defining Insight for Visual Analytics , 2009, IEEE Computer Graphics and Applications.

[25]  Jussi Myllymaki,et al.  Visual exploration of large data sets , 1996, Electronic Imaging.

[26]  Chris North,et al.  A comparison of two display models for collaborative sensemaking , 2013, PerDis '13.

[27]  Markus Geimer,et al.  Performance measurement and analysis of large-scale parallel applications on leadership computing systems , 2008, Sci. Program..

[28]  Pat Hanrahan,et al.  Multiscale visualization using data cubes , 2002, IEEE Symposium on Information Visualization, 2002. INFOVIS 2002..

[29]  Alfred Inselberg,et al.  Parallel Coordinates: Visual Multidimensional Geometry and Its Applications , 2003, KDIR.

[30]  Daniel A. Keim,et al.  Challenges in Visual Data Analysis , 2006, Tenth International Conference on Information Visualisation (IV'06).

[31]  Christopher Andrews,et al.  Visual encodings that support physical navigation on large displays , 2011, Graphics Interface.

[32]  Christian Chabot.  Demystifying Visual Analytics , 2009, IEEE Computer Graphics and Applications.

[33]  Lucas Mello Schnorr,et al.  A hierarchical aggregation model to achieve visualization scalability in the analysis of parallel applications , 2012, Parallel Comput..

[34]  Chris North,et al.  The Perceptual Scalability of Visualization , 2006, IEEE Transactions on Visualization and Computer Graphics.

[35]  A. Karr,et al.  Visual Scalability , 2002 .

[36]  Daniel A. Keim,et al.  Visual Analytics: Scope and Challenges , 2008, Visual Data Mining.

[37]  Robert Kincaid,et al.  Line graph explorer: scalable display of line graphs using Focus+Context , 2006, AVI '06.

[38]  Mikkel Rønne Jakobsen,et al.  An exploratory study of how abundant display space may support data analysis , 2012, NordiCHI.

[39]  Colin Ware,et al.  Information Visualization: Perception for Design , 2000 .

[40]  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.

[41]  Tamara Munzner,et al.  Process and Pitfalls in Writing Information Visualization Research Papers , 2008, Information Visualization.

[42]  Morten Hertzum,et al.  The notion of overview in information visualization , 2011, Int. J. Hum. Comput. Stud..

[43]  Lucian Voinea,et al.  The Solid* toolset for software visual analytics of program structure and metrics comprehension: From research prototype to product , 2014, Sci. Comput. Program..

[44]  Chris North,et al.  The visual scalability of integrated and multiple view visualizations for large, high resolution displays , 2007 .

[45]  Daniel A. Keim,et al.  Multi-Resolution Techniques for Visual Exploration of Large Time-Series Data , 2007, EuroVis.

[46]  Stuart K. Card.  Information visualization and information foraging , 1996, AVI '96.