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[[Image:250px-HaloVisualizationTechnique.png|250px|thumb|A personal digital assistant ([[personal digital assistant|PDA]]) showing a street map enhanced with the halo visualization technique.]]
#REDIRECT [[Visualization (computer graphics)#Information visualization]]

'''Information visualization''' the [[interdisciplinary]] study of the [[visualisation|visual]] [[Representation (arts)|representation]] of large-scale collections of non-numerical information, such as files and lines of code in software systems<ref>S.G. Eick (1994). "Graphically displaying text". In: ''Journal of Computational and Graphical Statistics'', vol 3, pp. 127–142.</ref> library and bibliographic databases, networks of relations on the internet, and so forth.<ref name = "MF08"> [[Michael Friendly]] (2008). [http://www.math.yorku.ca/SCS/Gallery/milestone/milestone.pdf "Milestones in the history of thematic cartography, statistical graphics, and data visualization"].</ref>

== Overview ==
The term ''Information visualization'' could be taken to subsume all developments in [[data visualization]], [[information graphics]], [[knowledge visualization]], [[scientific visualization]] and [[visual design]]. At this level, almost anything, if sufficiently organized, is [[information]] of a sort: Tables, graphs, maps and even text, whether static or dynamic, provide some means to see what lies within, determine the answer to a question, find relations, and perhaps apprehend things which could not be seen so readily in other forms. But today the term information visualization is generally applied to the visual representation of large-scale collections of non-numerical information.<ref name = "MF08"/>

Information visualization focused on the creation of approaches for conveying abstract information in intuitive ways. Visual representations and interaction techniques take advantage of the human eye’s broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once. <ref>James J. Thomas and Kristin A. Cook (Ed.) (2005). [http://nvac.pnl.gov/agenda.stm ''Illuminating the Path: The R&D Agenda for Visual Analytics'']. National Visualization and Analytics Center. p.30</ref>

=== Some examples ===
Visualization of various data structures requires new user interface and visualization techniques, which is now evolving into a separate discipline.<ref name= "IH97"> [http://db.cwi.nl/projecten/project.php4?prjnr=133 CWI Project Information Visualization (IV)]. Coordinator Dr. I. Herman. Startdate: 1997-07-01, Enddate: 2000-12-31. Retrieved 14 July 2008.</ref> This area of information visualization is different from the classical [[scientific visualization]], although the two fields are related. In information visualization the data to be visualized is not the result of some mathematical models or large data set, but abstract data with their own, inherent structure Examples of such data are:<ref name= "IH97"/>
* internal data structures of various programs, like compilers, or trace information for massively parallel programs;
* WWW site contents;
* operating system file spaces;
* data returned from various database query engines, e.g., for digital libraries.
Another characteristics of the field is that the tools to be used are deliberately focused on widely available environments, such as general workstations, WWW, PC-s, etc. These are not tailored at high-end, expensive, and specialized computing equipments.<ref name= "IH97"/>

=== Link with visual analytics ===
Information visualization has some overlapping goals and techniques with [[Visual analytics]]. There is currently no clear consensus on the boundaries between these fields, but broadly speaking the three areas can be distinguished as follows. Scientific visualization deals with data that has a natural geometric structure (e.g., MRI data, wind flows). Information visualization handles abstract data structures such as trees or graphs. Visual analytics is especially concerned with sensemaking and reasoning.<ref name= "JJT05"> James J. Thomas and Kristin A. Cook (Ed.) (2005). [http://nvac.pnl.gov/agenda.stm ''Illuminating the Path: The R&D Agenda for Visual Analytics'']. National Visualization and Analytics Center. p.3-33.</ref>

=== Human cognitive capabilities ===
Visual analytics seeks to marry techniques from information visualization with techniques from computational transformation and analysis of data. Information visualization itself forms part of the direct interface between user and machine. Information visualization amplifies human cognitive capabilities in six basic ways:<ref name= "JJT05"/><ref> Stuart Card, J.D. Mackinlay, and Ben Shneiderman (1999). "Readings in Information Visualization: Using Vision to Think". Morgan Kaufmann Publishers, San Francisco.</ref>
# by increasing cognitive resources, such as by using a visual resource to expand human working memory,
# by reducing search, such as by representing a large amount of data in a small space,
# by enhancing the recognition of patterns, such as when information is organized in space by its time relationships,
# by supporting the easy perceptual inference of relationships that are otherwise more difficult to induce,
# by perceptual monitoring of a large number of potential events, and
# by providing a manipulable medium that, unlike static diagrams, enables the exploration of a space of parameter values.
These capabilities of information visualization, combined with computational data analysis, can be applied to analytic reasoning to support the sense-making process.<ref name= "JJT05"/>

== History ==
Since the introduction of data graphics in the late 1700’s [[Representation (arts)|visual representations]] of abstract [[information]] have been used to demystify data and reveal otherwise hidden patterns. The recent advent of graphical interfaces in the 1990s has enabled direct interaction with visualized information, giving rise to over a decade of information visualization research. Information visualization seeks to augment human cognition
by leveraging human visual capabilities to make sense of abstract information, providing means by which humans with constant perceptual abilities can grapple with increasing hordes of data.<ref>Jeffrey Heer, Stuart K. Card, James Landay (2005). [http://bid.berkeley.edu/files/papers/2005-prefuse-CHI.pdf "Prefuse: a toolkit for interactive information visualization"]. In: ''ACM Human Factors in Computing Systems'' CHI 2005.</ref> The term "information visualization" itselve is cointed by [[Stuart K. Card]], [[Jock D. Mackinlay]] and [[George G. Robertson]] in 1989.<ref>[[Stuart K. Card]], [[Jock D. Mackinlay]], and and [[Ben Shneiderman]] (1999). [http://books.google.nl/books?id=wdh2gqWfQmgC&dq=readings+in+information+visualization+using+vision+to+think&psp=1&source=gbs_summary_s&cad=0 ''Readings in Information Visualization: Using Vision to Think''], Morgan Kaufmann Publishers. p.8.</ref> The field of Information visualization which has emerged since the 1990s derives, according to Stuart K. Card in 1999, from several communities:
* Work in [[information graphics]] dates from about the time of [[William Playfair]] end of the 18th century, who was among the earliest to use abstract visual properties such as line and area to represent data visually.<ref name="ERT83"> [[Edward R. Tufte]] (1983). [http://www.edwardtufte.com/tufte/books_vdqi ''The Visual Display of Quantitative Information'']. Graphics Press.</ref> Ever since classical methods of plotting were developed In 1967 [[Jacques Bertin]] was the first to published a theory of graphics. This theory identified the basic elements of diagrams and describes a framework for their design. [[Edward Tufte]] in 1983<ref name="ERT83"/> published a theory of data graphics that emphasized maximizing the density of useful information. Both Bertin's and Tufte's theories became well known and influential in the various communities that led to the development of information visualization as a discipline.<ref name="SKC99"> [[Stuart K. Card]], Jock D. Mackinlay and Ben Shneiderman (1999). [http://books.google.nl/books?id=wdh2gqWfQmgC&dq=readings+in+information+visualization+using+vision+to+think&psp=1&source=gbs_summary_s&cad=0 ''Readings in Information Visualization: Using Vision to Think''], Morgan Kaufmann Publishers. pp.6-8.</ref>
* Within [[statistics]] in 1977 [[John Tukey]] began a movement with his work on "Exploring Data Analysis", which effected the data graphics community. The emphasis on this work was not on the quality of graphics but on the use of pictures to give rapid statistical insight into data. For example the [[Box and whisker plot]] allowed an analysis to see in an instant the most important four numbers that characterize a distribution. In the 1988 book "Dynamic Graphics for Statistics" William S. Cleveland explicated new visualizations of data in this area. A particular problem here is how to visualize data sets with many variables, see for example Inselberg's [[parallel coordinates]] method from 1990.<ref name="SKC99"/>
* In 1986 the National Science Foundation launched an important new initiative on [[scientific visualization]] with the work of H.B. McCormick. The first IEEE Visualization Conference was held in 1990, which initiated a community from earth resource scientists, physicists, to computer scientists in supercomputing.<ref name="SKC99"/>
* In the [[artificial intelligence]] community there was an interest in automatic design of visual presentation of data. The effort here was catalyzed by Jock D. Mackinlay thesis <ref>Jock D. Mackinlay (1986)[http://www2.parc.com/istl/projects/uir/publications/items/UIR-1986-02-Mackinlay-TOG-Automating.pdf "Automating the Design of Graphical Presentations of Relational Information"]. In: ''ACM Transactions on Graphics'' 5(2, April): 110-141. </ref>, which formalized Bertin's design theory. added psychophysical data and used generated presentation.<ref name="SKC99"/>
* Finally the [[user interface]] community saw advances in graphics hardware opening the possibility of a new generation of user interfaces.<ref name="SKC99"/>

In 2003 [[Ben Shneiderman]] stated that this field has emerging from research in slightly different direction:<ref name = "BBB03"> Benjamin B. Bederson and [[Ben Shneiderman]] (2003). [http://www.cs.umd.edu/hcil/pubs/books/craft.shtml ''The Craft of Information Visualization: Readings and Reflections''], Morgan Kaufmann ISBN 1-55860-915-6.</ref> He also mentions graphics, visual design, computer science and human-computer interaction, and newly [[psychology]] and [[business methods]].

== Information visualization topics ==
Visualization provide deep insight into the structure of data. There are graphical tools such as coplots, multiway dot plots, and the equal count algorithm. There are fitting tools such as loess and bisquare that fit equations, nonparametric curves, and nonparametric surfaces to data.<ref>William S. Cleveland (1993). [http://www.amazon.com/Visualizing-Data-William-S-Cleveland/dp/0963488406 ''Visualizing Data'']. Hobart Press.</ref>

=== Specific methods and techniques ===
* [[Color alphabet]]
* [[Information visualization reference model]]
* [[Graph drawing]]
* [[Halo (visualization technique)]]
* [[HyperbolicTree]]
* [[Multidimensional scaling]]
* [[Problem Solving Environment]]
* [[Treemapping]]

=== Software and toolkits ===
;OpenLink AJAX Toolkit
:[[OpenLink AJAX Toolkit]] is a [[JavaScript library|JavaScript-based toolkit]] for browser-independent [[Rich Internet application|Rich Internet Application]] development. It includes a rich collection of [[GUI widget|UI Widgets/Controls]], [[Event-driven programming|Event Management]] System, and a truly platform independent [[Data access layer|Data Access Layer]] called [[Ajax (programming)|AJAX]] [[Database connection|Database Connectivity]]. OpenLink AJAX Toolkit is fully [[OpenAjax Alliance]] Conformant.

;Prefuse
:[[Prefuse]] is a [[Java (sun)|Java]]-based [[toolkit]] for building interactive information visualization applications. It supports a rich set of features for [[data modeling]], [[visualization]], and interaction. It provides optimized [[data structures]] for [[table (information)|tables]], [[graphs]], and [[trees]], a host of layout and visual encoding techniques, and support for [[animation]], dynamic queries, integrated search, and database connectivity.

;XEE
:[[XEE (Starlight)]] This a visual language for data processing and [[Extract, transform, load|ETL]] tasks. It is designed for the [[Starlight_Information_Visualization_System|Starlight Information Visualization System]] as a method for producing and processing XML data.

See further: [[List of information graphics software]]

== Information visualization applications ==
[[Image:Starlight visualization software .jpg|thumb|240px|[[Starlight Information Visualization System|Starlight visualization software]] solves super-sized data analysis challenges.<ref>[http://www.pnl.gov/news/release.asp?id=142 PNNL recognized for commercializing technology] February 15, 2006.</ref>.]]
[[Image:In-spire overview.jpg|thumb|320px|The IN-SPIRE™ discovery tool integrates information visualization with query and other interactive capabilities.<ref>[http://in-spire.pnl.gov/about.stm The IN-SPIRE™ discovery tool]. Pacific Northwest National Laboratory. Last Update: May 2008.</ref>]]
Information visualization is increasingly applied as a critical component in different directions:<ref name = "BBB03"/>
* scientific research,
* [[digital libraries]],
* [[data mining]],
* financial data analysis, market studies,
* manufacturing production control,
* and [[crime mapping]].

See also:
* [[Command Post of the Future]]
* [[Informedia Digital Library]]
* [[Information graphics]]
* [[Starlight Information Visualization System]]

== Information visualization experts ==
;Related scientists
* [[Stuart K. Card]]
* [[George Furnas]]
* [[James D. Hollan]]
* [[Scott Meyers]]
* [[George G. Robertson]]
* [[Pierre Rosenstiehl]]
* [[Ben Shneiderman]]

== Information visualization organization ==
;Organizations
*[[International Symposium on Graph Drawing]]
*[[Panopticon Software]]
*[[University of Maryland Human-Computer Interaction Lab]]
*[[Vvi]]

==See also==
;Related fields
* [[Computational visualistics]]
* [[Geovisualization]]
* [[Infographics]]
* [[Infonomics]]
* [[Visual analytics]]
* [[Web mapping]]

==References==
{{reflist}}

== Further reading ==
<!--
Publications listed here should only relate specificly to information visualization, and not: Computational visualistics, Data visualization, Information graphics, Knowledge visualization, Information visualization and Visual analytics.

There are some links added here to check the content of every publication. Later on these links should be removed or moved to the talk page.
-->
* Benjamin B. Bederson and Ben Shneiderman (2003). [http://books.google.nl/books?id=TrZZQ5I76BcC&dq=the+craft+of+information+visualization+readings+and+reflections&psp=1&source=gbs_summary_s&cad=0 ''The Craft of Information Visualization: Readings and Reflections'']. Morgan Kaufmann.
* Stuart K. Card, Jock D.Mackinlay and Ben Shneiderman (1999). [http://books.google.nl/books?id=wdh2gqWfQmgC&dq=readings+in+information+visualization+using+vision+to+think&psp=1&source=gbs_summary_s&cad=0 ''Readings in Information Visualization: Using Vision to Think''], Morgan Kaufmann Publishers.
* Jeffrey Heer, Stuart K. Card, James Landay (2005). [http://bid.berkeley.edu/files/papers/2005-prefuse-CHI.pdf "Prefuse: a toolkit for interactive information visualization"]. In: ''ACM Human Factors in Computing Systems'' CHI 2005.
* Colin Ware (2000). [http://www.amazon.com/Foundation-Computational-Visualistics-J%C3%B6rg-Schirra/dp/3835060155 ''Information Visualization: Perception for design'']. San Francisco, CA: Morgan Kaufmann.

==External links==
{{Commonscat|Information visualization}}
<!-- External links listed here should only relate specificly to information visualization, and not: Computational visualistics, Data visualization, Information graphics, Knowledge visualization, Information visualization, Visual analytics, Visualisation techniques and other specific visualization subjects.-->
* [http://www.asis.org/SIG/SIGVIS/index.htm American Society for Information Science and Technology] Special Interest Group in Visualization Information and Sound.
* [http://vis.computer.org/ IEEE Visualization Conference] for visualization advances in science and engineering for academia, government, and industry.
* [http://www.infovis-wiki.net InfoVis-Wiki.net] - Wiki about Information Visualization
* [http://infosthetics.com/ Information Aesthetics: Data visualization & visual communication], a continuously updated collection of infoviz applications and software
* http://vam.anest.ufl.edu - A free transparent reality simulation of an anesthesia machine that uses information visualization, including sound and color
* {{dmoz|Reference/Knowledge_Management/Knowledge_Discovery/Information_Visualization/|Information Visualization}}
* [http://courses.iicm.tugraz.at/ivis/ivis.pdf. Information Visualisation Course Notes]. Dr. Keith Andrews, IICM, Graz University of Technology.


{{Visualization}}

[[Category:Computational science]]
[[Category:Computer graphics]]
[[Category:Infographics]]
[[Category:Visualization (graphic)]]
[[Category:Scientific modeling]]
[[Category:User interface techniques]]

[[de:Informationsvisualisierung]]

Revision as of 08:17, 12 August 2008

File:250px-HaloVisualizationTechnique.png
A personal digital assistant (PDA) showing a street map enhanced with the halo visualization technique.

Information visualization the interdisciplinary study of the visual representation of large-scale collections of non-numerical information, such as files and lines of code in software systems[1] library and bibliographic databases, networks of relations on the internet, and so forth.[2]

Overview

The term Information visualization could be taken to subsume all developments in data visualization, information graphics, knowledge visualization, scientific visualization and visual design. At this level, almost anything, if sufficiently organized, is information of a sort: Tables, graphs, maps and even text, whether static or dynamic, provide some means to see what lies within, determine the answer to a question, find relations, and perhaps apprehend things which could not be seen so readily in other forms. But today the term information visualization is generally applied to the visual representation of large-scale collections of non-numerical information.[2]

Information visualization focused on the creation of approaches for conveying abstract information in intuitive ways. Visual representations and interaction techniques take advantage of the human eye’s broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once. [3]

Some examples

Visualization of various data structures requires new user interface and visualization techniques, which is now evolving into a separate discipline.[4] This area of information visualization is different from the classical scientific visualization, although the two fields are related. In information visualization the data to be visualized is not the result of some mathematical models or large data set, but abstract data with their own, inherent structure Examples of such data are:[4]

  • internal data structures of various programs, like compilers, or trace information for massively parallel programs;
  • WWW site contents;
  • operating system file spaces;
  • data returned from various database query engines, e.g., for digital libraries.

Another characteristics of the field is that the tools to be used are deliberately focused on widely available environments, such as general workstations, WWW, PC-s, etc. These are not tailored at high-end, expensive, and specialized computing equipments.[4]

Information visualization has some overlapping goals and techniques with Visual analytics. There is currently no clear consensus on the boundaries between these fields, but broadly speaking the three areas can be distinguished as follows. Scientific visualization deals with data that has a natural geometric structure (e.g., MRI data, wind flows). Information visualization handles abstract data structures such as trees or graphs. Visual analytics is especially concerned with sensemaking and reasoning.[5]

Human cognitive capabilities

Visual analytics seeks to marry techniques from information visualization with techniques from computational transformation and analysis of data. Information visualization itself forms part of the direct interface between user and machine. Information visualization amplifies human cognitive capabilities in six basic ways:[5][6]

  1. by increasing cognitive resources, such as by using a visual resource to expand human working memory,
  2. by reducing search, such as by representing a large amount of data in a small space,
  3. by enhancing the recognition of patterns, such as when information is organized in space by its time relationships,
  4. by supporting the easy perceptual inference of relationships that are otherwise more difficult to induce,
  5. by perceptual monitoring of a large number of potential events, and
  6. by providing a manipulable medium that, unlike static diagrams, enables the exploration of a space of parameter values.

These capabilities of information visualization, combined with computational data analysis, can be applied to analytic reasoning to support the sense-making process.[5]

History

Since the introduction of data graphics in the late 1700’s visual representations of abstract information have been used to demystify data and reveal otherwise hidden patterns. The recent advent of graphical interfaces in the 1990s has enabled direct interaction with visualized information, giving rise to over a decade of information visualization research. Information visualization seeks to augment human cognition by leveraging human visual capabilities to make sense of abstract information, providing means by which humans with constant perceptual abilities can grapple with increasing hordes of data.[7] The term "information visualization" itselve is cointed by Stuart K. Card, Jock D. Mackinlay and George G. Robertson in 1989.[8] The field of Information visualization which has emerged since the 1990s derives, according to Stuart K. Card in 1999, from several communities:

  • Work in information graphics dates from about the time of William Playfair end of the 18th century, who was among the earliest to use abstract visual properties such as line and area to represent data visually.[9] Ever since classical methods of plotting were developed In 1967 Jacques Bertin was the first to published a theory of graphics. This theory identified the basic elements of diagrams and describes a framework for their design. Edward Tufte in 1983[9] published a theory of data graphics that emphasized maximizing the density of useful information. Both Bertin's and Tufte's theories became well known and influential in the various communities that led to the development of information visualization as a discipline.[10]
  • Within statistics in 1977 John Tukey began a movement with his work on "Exploring Data Analysis", which effected the data graphics community. The emphasis on this work was not on the quality of graphics but on the use of pictures to give rapid statistical insight into data. For example the Box and whisker plot allowed an analysis to see in an instant the most important four numbers that characterize a distribution. In the 1988 book "Dynamic Graphics for Statistics" William S. Cleveland explicated new visualizations of data in this area. A particular problem here is how to visualize data sets with many variables, see for example Inselberg's parallel coordinates method from 1990.[10]
  • In 1986 the National Science Foundation launched an important new initiative on scientific visualization with the work of H.B. McCormick. The first IEEE Visualization Conference was held in 1990, which initiated a community from earth resource scientists, physicists, to computer scientists in supercomputing.[10]
  • In the artificial intelligence community there was an interest in automatic design of visual presentation of data. The effort here was catalyzed by Jock D. Mackinlay thesis [11], which formalized Bertin's design theory. added psychophysical data and used generated presentation.[10]
  • Finally the user interface community saw advances in graphics hardware opening the possibility of a new generation of user interfaces.[10]

In 2003 Ben Shneiderman stated that this field has emerging from research in slightly different direction:[12] He also mentions graphics, visual design, computer science and human-computer interaction, and newly psychology and business methods.

Information visualization topics

Visualization provide deep insight into the structure of data. There are graphical tools such as coplots, multiway dot plots, and the equal count algorithm. There are fitting tools such as loess and bisquare that fit equations, nonparametric curves, and nonparametric surfaces to data.[13]

Specific methods and techniques

Software and toolkits

OpenLink AJAX Toolkit
OpenLink AJAX Toolkit is a JavaScript-based toolkit for browser-independent Rich Internet Application development. It includes a rich collection of UI Widgets/Controls, Event Management System, and a truly platform independent Data Access Layer called AJAX Database Connectivity. OpenLink AJAX Toolkit is fully OpenAjax Alliance Conformant.
Prefuse
Prefuse is a Java-based toolkit for building interactive information visualization applications. It supports a rich set of features for data modeling, visualization, and interaction. It provides optimized data structures for tables, graphs, and trees, a host of layout and visual encoding techniques, and support for animation, dynamic queries, integrated search, and database connectivity.
XEE
XEE (Starlight) This a visual language for data processing and ETL tasks. It is designed for the Starlight Information Visualization System as a method for producing and processing XML data.

See further: List of information graphics software

Information visualization applications

File:Starlight visualization software .jpg
Starlight visualization software solves super-sized data analysis challenges.[14].
File:In-spire overview.jpg
The IN-SPIRE™ discovery tool integrates information visualization with query and other interactive capabilities.[15]

Information visualization is increasingly applied as a critical component in different directions:[12]

See also:

Information visualization experts

Related scientists

Information visualization organization

Organizations

See also

Related fields

References

  1. ^ S.G. Eick (1994). "Graphically displaying text". In: Journal of Computational and Graphical Statistics, vol 3, pp. 127–142.
  2. ^ a b Michael Friendly (2008). "Milestones in the history of thematic cartography, statistical graphics, and data visualization".
  3. ^ James J. Thomas and Kristin A. Cook (Ed.) (2005). Illuminating the Path: The R&D Agenda for Visual Analytics. National Visualization and Analytics Center. p.30
  4. ^ a b c CWI Project Information Visualization (IV). Coordinator Dr. I. Herman. Startdate: 1997-07-01, Enddate: 2000-12-31. Retrieved 14 July 2008.
  5. ^ a b c James J. Thomas and Kristin A. Cook (Ed.) (2005). Illuminating the Path: The R&D Agenda for Visual Analytics. National Visualization and Analytics Center. p.3-33.
  6. ^ Stuart Card, J.D. Mackinlay, and Ben Shneiderman (1999). "Readings in Information Visualization: Using Vision to Think". Morgan Kaufmann Publishers, San Francisco.
  7. ^ Jeffrey Heer, Stuart K. Card, James Landay (2005). "Prefuse: a toolkit for interactive information visualization". In: ACM Human Factors in Computing Systems CHI 2005.
  8. ^ Stuart K. Card, Jock D. Mackinlay, and and Ben Shneiderman (1999). Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann Publishers. p.8.
  9. ^ a b Edward R. Tufte (1983). The Visual Display of Quantitative Information. Graphics Press.
  10. ^ a b c d e Stuart K. Card, Jock D. Mackinlay and Ben Shneiderman (1999). Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann Publishers. pp.6-8.
  11. ^ Jock D. Mackinlay (1986)"Automating the Design of Graphical Presentations of Relational Information". In: ACM Transactions on Graphics 5(2, April): 110-141.
  12. ^ a b Benjamin B. Bederson and Ben Shneiderman (2003). The Craft of Information Visualization: Readings and Reflections, Morgan Kaufmann ISBN 1-55860-915-6.
  13. ^ William S. Cleveland (1993). Visualizing Data. Hobart Press.
  14. ^ PNNL recognized for commercializing technology February 15, 2006.
  15. ^ The IN-SPIRE™ discovery tool. Pacific Northwest National Laboratory. Last Update: May 2008.

Further reading

Wikimedia Commons has media related to Information visualization.


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