Information visualization

Information visualization is a research area that deals with computer-aided methods for the graphic representation of abstract data. Abstract data are understood here as numerical, relational or textual data for which a spatial representation is not immediately given. In contrast to this, scientific visualization deals with the representation of spatial data.

The visual presentation methods are intended to help evaluate the data and gain new knowledge from them. Information visualizations also support the communication and illustration of these findings.

In general, information is conceptually differentiated from knowledge . Thus, also from the information visualization even differentiated knowledge visualization ( English knowledge visualization ) as a process of transmitting knowledge ( knowledge transfer ) , serviced, which is a graphical means of any type, even non-computer-linked. Knowledge visualization is based on the learning psychological advantages of the visual form of communication .

Workspace

Information visualization is a relatively new interdisciplinary field that uses methods and findings from computer science , statistics , data mining and cognitive science , among other things . The aim here is also to improve human-computer interaction .

The task of information visualization is basically the expressive and thereby effective representation of the data pattern and the information contained therein. Expressive means that all data and only the data flow into the visualization. Effectiveness means that the viewer of a visualization should be able to get an overview of the information contained in the data as quickly as possible. Perception effects, such as optical illusions , must be taken into account.

Visualizations can be used to present information or for exploratory data analysis . An exploratory analysis is an open, exploratory process in which a precise analysis goal does not have to be formulated initially.

The most important scientific conference for information visualization is the IEEE Information Visualization (InfoVis) , which takes place annually as part of the IEEE VIS . Other important scientific conferences with a similar focus are the EG / VGTC Conference on Visualization (EuroVis) and the IEEE Pacific Visualization Symposium (PacificVis).

Examples

Citespace analysis

Result of the analysis: publications on the subject of outsourcing

In his article Searching for intellectual turning points , Chaomei Chen presents a method for the analysis of citation spaces. A tool for analyzing the Web of Science is available on his homepage . Not only bibliographic data, but also the citations of authors by other authors can be tracked. The investigation of cozitation is a long known information technology method. What is new at Chen is the application of Progressive Pathfinder Network Scaling to the problem of cocitation. This analysis works with Pathfinder Associative Networks . The algorithm comes from the psychologist Roger W. Schvaneveldt and belongs to the field of artificial intelligence .

The tool is very flexible and offers the possibility of other analyzes such as B. Carry out author- or publication-related cluster analysis .

Visualization of semantic networks

For the ontology - editor Protégé-2000 different visualization exist plug-ins , which is a representation of ontologies and semantic networks in graph format enabling variable layout. Furthermore, there are advanced approaches to the visualization of semantics that go beyond pure formal ontologies and generate semantics with analytical approaches.

Visualization techniques

Different visualization techniques can be used depending on the data type. Certain visual display forms and diagrams have established themselves as standards for frequently occurring data types .

Time series and univariate data

Changes in a numerical variable over time are often shown as a bar , column or line diagram, with one of the two axes of the two-dimensional coordinate system used representing the respective time sequence. If you compare several objects with regard to a numerical variable (univariate data), similar diagrams can be used - the time axis is replaced by a list of the objects to be compared. Pie charts are also suitable for comparing numerical values, especially if the sum of the values results in an interpretable total (e.g. the composition of voting shares).

Multivariate data

Multivariate data describes a set of objects using several numeric variables. Simple scatter diagrams allow two variables to be compared by marking the objects as points in a two-dimensional coordinate system . Based on this, a scatter diagram matrix compares all paired combinations of variables. As a further diagram type, parallel coordinates convert the variables as parallel axes and draw each object as a polyline that connects all axes according to the variable values of the object.

Networks and hierarchies

Networks (also called graph in computer science ) represent relationships between objects. In node-edge diagrams, nodes (mostly circles or rectangles) represent the objects that are connected with straight or curved lines. Techniques for displaying such diagrams, in particular for calculating the node and edge positions, are known as graph drawing . Alternatively, networks can also be visualized as an adjacency matrix .

Hierarchies (also called trees in computer science ) are a special case of networks in which an object is only assigned to one superordinate object. Node-edge diagrams are also used to represent hierarchies. But alternative representations such as tree maps are also common, which recursively subdivide an area into partial areas based on the hierarchy structure.

Further visualization techniques

application areas

Information visualizations can be used in a variety of different applications. Significant areas of application include:

literature

General

Stuart Card, Jock Mackinley, Ben Shneiderman (Eds.): Readings in Information Visualization. Using Vision to Think. Kaufmann, San Francisco 1999.
Chaomei Chen: Information Visualization. Beyond the horizon. 2nd Edition. Springer, London 2004, ISBN 1-85233-789-3 .
Chaomei Chen: Mapping Scientific Frontiers. The Quest for Knowledge Visualization. Springer, London 2002, ISBN 1-85233-494-0 .
Manuel Lima : The Book of Trees: Visualizing Branches of Knowledge . Foreword by Ben Shneiderman . Princeton Architectural Press, New York 2014, ISBN 978-1-61689-218-0 .
Tamara Munzner, Stephen North (Eds.): IEEE Symposium on Information Visualization 2003. Seattle, Washington, October 19-21. Los Alamitos Publishing House, Calif. u. a .: IEEE Computer Soc. 2003, ISBN 0-7803-8154-8 .
Ben Shneiderman: The eyes have it: A task by data-type taxonomy for information visualizations. In: Proceedings of Visual Languages. (IEEE 1996). 1996, pp. 336-343.
Keller Tergan: Knowledge and Information Visualization: Searching for Synergies. Springer, Berlin 2005, ISBN 3-540-26921-5 .
Colin Ware: Information Visualization: Perception for Design. (= Morgan Kaufmann Series in Interactive Technologies ). San Francisco 2000, ISBN 1-55860-819-2 .
Oliver Grau: Imagery in the 21st Century . MIT-Press, Cambridge 2011. (With contributions by James Elkins , Eduardo Kac , Peter Weibel , Lev Manovich , Olaf Breitbach , Martin Kemp , Sean Cubitt , Christa Sommerer , Marie Luise Angerer , Wendy Chun and others)
Andreas Kerren, Achim Ebert, Jörg Meyer (Eds.): Human-Centered Visualization Environments. (= LNCS Tutorial. Volume 4417). Springer, 2007.
Andreas Kerren, John T. Stasko, Jean-Daniel Fekete, Chris North (Eds.): Information Visualization - Human-Centered Issues and Perspectives. (= LNCS State-of-the-Art Survey. Volume 4950). Springer, 2008.

To citespace analysis

Roger W. Schvaneveldt (Ed.): Studies in Knowledge Organization. Pathfinder Associative Networks, Norwood 1989, ISBN 0-89391-624-2 .

To intelligent visualization

Visualizing the Semantic Web . XML-based Internet and Information Visualization. Springer, London 2003, ISBN 1-85233-576-9 .
Robert Spence: Information Visualization: Design for Interaction. 2nd Edition. Prentice Hall, 2007, ISBN 978-0-13-206550-4 .
Alexander Martens: Visualization in Information Retrieval - Theory and practice applied in Wikis as an alternative to the Semantic Web. BoD, Norderstedt 2009, ISBN 978-3-8391-2064-4 .
Kawa Nazemi et al: A Reference Model for Adaptive Visualization Systems. In: Julie A. Jacko (Ed.): Human-Computer Interaction. Part I: Design and Development Approaches. (= Lecture Notes in Computer Science (LNCS). 6761). Springer, Berlin / Heidelberg / New York 2011, pp. 480–489.
Kawa Nazemi: Adaptive Semantics Visualization. Dissertation TU Darmstadt. Eurographics Association, 2014. (diglib.eg.org)

Web links

see conference - annual German conference on information visualization

Individual evidence

^ Stuart Card, Jock Mackinlay, Ben Shneiderman: Readings in Information Visualization: Using Vision to Think . Morgan Kaufmann Publishers, San Francisco, CA, US 1999, ISBN 1-55860-533-9 .
^ Daniel Weiskopf, Kwan-Liu Ma, Jarke J. van Wijk, Robert Kosara, Helwig Hauser: SciVis, InfoVis - bridging the community divide ?! In: Proceedings of the IEEE Visualization Conference . IEEE, 2006.
^ Jean-Daniel Fekete, Jarke J. van Wijk, John T. Stasko, Chris North: The Value of Information Visualization . In: Information Visualization (= Lecture Notes in Computer Science ). Springer, Berlin / Heidelberg 2008, ISBN 978-3-540-70955-8 , pp. 1-18 , doi : 10.1007 / 978-3-540-70956-5_1 ( springer.com ).
^ Martin Eppler, Remo Burkhard: Knowledge Visualization. 2004.
^ Searching for intellectual turning points: Progressive Knowledge Domain Visualization. In. Proceedings of the National Academy of Sciences . (PNAS), 101 (Suppl. 1), 2004, pp. 5303-5310. doi : 10.1073 / pnas.0307513100
↑ cluster.cis.drexel.edu
↑ Topic - Visualization (visualization plugins for Protégé, OntoViz, etc.)
↑ Kawa Nazemi u. a: SemaVis: A New Approach for Visualizing Semantic Information. 2014. doi : 10.1007 / 978-3-319-06755-1_15 .
^ Kawa Nazemi: Adaptive Semantics Visualization. 2014.
↑ Jeffrey Heer, Michael Bostock, Vadim Ogievetsky: A tour through the visualization zoo . In: Communications of the ACM . tape 53 , no. 6 , June 1, 2010, ISSN 0001-0782 , doi : 10.1145 / 1743546.1743567 .

[1] Stuart Card, Jock Mackinlay, Ben Shneiderman: Readings in Information Visualization: Using Vision to Think . Morgan Kaufmann Publishers, San Francisco, CA, US 1999, ISBN 1-55860-533-9 .

[2] Daniel Weiskopf, Kwan-Liu Ma, Jarke J. van Wijk, Robert Kosara, Helwig Hauser: SciVis, InfoVis - bridging the community divide ?! In: Proceedings of the IEEE Visualization Conference . IEEE, 2006.

[3] Jean-Daniel Fekete, Jarke J. van Wijk, John T. Stasko, Chris North: The Value of Information Visualization . In: Information Visualization (= Lecture Notes in Computer Science ). Springer, Berlin / Heidelberg 2008, ISBN 978-3-540-70955-8 , pp. 1-18 , doi : 10.1007 / 978-3-540-70956-5_1 ( springer.com ).

[4] Martin Eppler, Remo Burkhard: Knowledge Visualization. 2004.

[5] Searching for intellectual turning points: Progressive Knowledge Domain Visualization. In. Proceedings of the National Academy of Sciences . (PNAS), 101 (Suppl. 1), 2004, pp. 5303-5310. doi : 10.1073 / pnas.0307513100

[6] uster.cis.drexel.edu

[7] Topic - Visualization (visualization plugins for Protégé, OntoViz, etc.)

[8] Kawa Nazemi u. a: SemaVis: A New Approach for Visualizing Semantic Information. 2014. doi : 10.1007 / 978-3-319-06755-1_15 .

[9] Kawa Nazemi: Adaptive Semantics Visualization. 2014.

[10] Jeffrey Heer, Michael Bostock, Vadim Ogievetsky: A tour through the visualization zoo . In: Communications of the ACM . tape 53 , no. 6 , June 1, 2010, ISSN 0001-0782 , doi : 10.1145 / 1743546.1743567 .