InfoVis:Wiki - User contributions [en]

Category:Events

2012-02-13T07:18:46Z

Thietkeweb:

{{Conferences}}
* [http://www.sensor-nets.org SENSORNETS 2012: 1st International Conference on Sensor Networks ]
[[Category:Top level]]
* [http://egc2012.labri.fr 12th (FR) International Conf on Knowledge discovery and management]
* [http://www.beliv.org/beliv2010 BELIV'10: BEyond time and errors: novel evaLuation methods for Information Visualization. Workshop at CHI2010]
* [http://www.vietnamairlinestickets.com/visas.html vietnam visa], [http://www.greatvietnamtours.com vietnam tours]
* [http://www.visigrapp.org/ VISIGRAPP 2011: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications ]
* [http://www.visapp.visigrapp.org/ VISAPP 2011: International Conference on Computer Vision Theory and Applications ]
* [http://www.ivapp.visigrapp.org/ IVAPP 2011: International Conference on Information Visualization Theory and Applications ]
* [http://www.imagapp.visigrapp.org/ IMAGAPP 2011: International Conference on Imaging Theory and Applications ]
* [http://www.grapp.visigrapp.org/ GRAPP 2011: International Conference on Computer Graphics Theory and Applications ]
Link Exchange
* [http://www.vietnamairlinestickets.com Vietnam Flights]
* [http://www.aboutvietnam.org/vietnamairlines Vietnam Airlines]

Category:Events

2011-11-28T09:44:18Z

Thietkeweb:

{{Conferences}}
* [http://www.sensor-nets.org SENSORNETS 2012: 1st International Conference on Sensor Networks ]
[[Category:Top level]]
* [http://egc2012.labri.fr 12th (FR) International Conf on Knowledge discovery and management]
* [http://www.beliv.org/beliv2010 BELIV'10: BEyond time and errors: novel evaLuation methods for Information Visualization. Workshop at CHI2010]
* [http://www.visigrapp.org/ VISIGRAPP 2011: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications ]
* [http://www.visapp.visigrapp.org/ VISAPP 2011: International Conference on Computer Vision Theory and Applications ]
* [http://www.ivapp.visigrapp.org/ IVAPP 2011: International Conference on Information Visualization Theory and Applications ]
* [http://www.imagapp.visigrapp.org/ IMAGAPP 2011: International Conference on Imaging Theory and Applications ]
* [http://www.grapp.visigrapp.org/ GRAPP 2011: International Conference on Computer Graphics Theory and Applications ]
Link Exchange

[http://www.vietnamairlinestickets.com/1.html Vietnam Flights]
[http://www.vietnamairlinestickets.com/visas.html Vietnam Visa]
[http://www.aboutvietnam.org/vietnamairlines Vietnam Airlines]

Category:Events

2011-10-01T04:13:10Z

Thietkeweb:

{{Conferences}}
* [http://www.sensor-nets.org SENSORNETS 2012: 1st International Conference on Sensor Networks ]
[[Category:Top level]]
* [http://egc2012.labri.fr 12th (FR) International Conf on Knowledge discovery and management]
* [http://www.beliv.org/beliv2010 BELIV'10: BEyond time and errors: novel evaLuation methods for Information Visualization. Workshop at CHI2010]
* [http://www.visigrapp.org/ VISIGRAPP 2011: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications ]
* [http://www.visapp.visigrapp.org/ VISAPP 2011: International Conference on Computer Vision Theory and Applications ]
* [http://www.ivapp.visigrapp.org/ IVAPP 2011: International Conference on Information Visualization Theory and Applications ]
* [http://www.imagapp.visigrapp.org/ IMAGAPP 2011: International Conference on Imaging Theory and Applications ]
* [http://www.grapp.visigrapp.org/ GRAPP 2011: International Conference on Computer Graphics Theory and Applications ]
Link Exchange

[http://www.vietnamairlinestickets.com/1.html Vietnam Flights]
[http://www.vietnamairlinestickets.com/visas.html Vietnam Visa]

Empirical Evaluation of Interactive Visualizations for Preferential Choice

2011-09-15T02:08:09Z

Thietkeweb:

'''An Empirical Evaluation of Interactive Visualizations for Preferential Choice''' by [http://infovis-wiki.net/index.php?title=Bautista%2C_Jeanette Jeanette Bautista] and [http://infovis-wiki.net/index.php?title=Carenini%2C_Giuseppe Giuseppe Carenini]

== Short Description ==
The authors of this paper tried to not only show the usefulness of Value Charts to support preferential choice, which is finding the best option out of a set of alternatives. Furthermore, they compared two types of Value Charts: a horizontal version against a vertical version. The outcome of this extensive user study was that Value Charts in general and in particular Vertical Value Charts (abbreviated VC+V) seemed to be very effective in supporting decision making.

=== Process of decision making ===
The process of effective preferential choice can be divided into 3 steps according to prescriptive decision theory.

'''Phase 1 / Model construction phase''': the decision maker (abbr. DM) finds objectives, which are important to him/her. The degree of importance is also chosen.

'''Phase 2 / Inspection phase''': DM analyzes his/her preference model as applied to a set of alternatives.

'''Phase 3 / Sensitivity analysis''': DM is able to answer "what if" questions - such as "if we make a slight change in one or more aspects of the model, does it effect the optimal decision?"

{{Quotation|In the development of interactive tools for preferential choice, we argue that full support for - and ﬂuid interaction between - all three phases are essential in making good decisions.| [Bautista and Carenini, 2008]}}

== ValueCharts+ ==
ValueChart+is a set of interactive visualization techniques for preferential choice and is an improvement of ValueCharts. It supports the DM in the 3 phases described above. In an Additive Multiattribute Value Function the DM’s objectives are hierarchically organized. In VC+ this hierarchy is displayed as an exploded stacked-bar. The ValueChart+ follows the information-seeking mantra by Ben Shneiderman: '''overview ﬁrst, zoom and ﬁlter, then details on demand.'''

== Figures ==
[[image:VCh.jpg]]

Shown above is the horizontal version of a ValueChart+ presented by Bautista et Carenini

[[image:VCv.jpg]]

Shown above is the vertical version of a ValueChart+ presented by Bautista et Carenini.

The vertical height of each row indicates the relative weight assigned to each objective (e.g., size is much less important
than internet-access). Each column represents an alternative, thus each cell portrays an objective corresponding to an
alternative (bottom-right quadrant). The amount of ﬁlled color relative to cell size depicts the alternative’s preference.

== Important Citation(s) ==
{{Quotation|An eﬀective visualization will aid the Decision Maker to see things that would otherwise go unnoticed, as well as enable her to view information about her preferences in a new light.| [Bautista and Carenini, 2008]}}

== Suitable for which data types ==
Suitable for different domains, where preferential choice objective values are possible to be quantized.

== Evaluation Part A: Controlled Study ==
In Part A, the authors took a quantitative approach by performing a '''controlled usability study''' to see how users performed the primitive tasks of the PVIT (Preferential Choice Visualization Integrated Task Model).
Part A starts in the sensitivity analysis phase. The task is the following: '''5 questions''' (based on the PVIT model) should be answered:

What are the top 3 alternatives according to total value?

For a speciﬁed alternative, which ob jective contributes to its total value the most?

What is the domain value of objective x for alternative y?

What is the best alternative when considering only objective x?

What is the best outcome for a objective x?

Mapped to the '''house domain''', for example, we get the following tasks:

List the 3 highest valued houses.

For HouseX, which is its strongest factor according to your preferences?

How many bathrooms are there in House1?

Which is the least expensive house?

What is the best bus-distance?

The subjects had to test the ValueCharts+ in the sensitivity analysis and inspection phase — no questions directed at the experimenter were allowed. Test domain was a set of hotels in the Vancouver area. 20 subjects were tested, 10 tested the VC+H and the remaining 10 the VC+V. Each subject performed each task, writing down the answer to applicable tasks that asked a question about the data. This procedure was re-iterated 5 times. The authors looked closely at their results to ﬁnd an indication of whether one version of VC+ was a better fit than the other during the decision making process.

Finally, in the second step of the analysis the authors determined, for each task, what interface the subjects performed better. VC+V performed better on all ﬁve inspection tasks and also performed better on three out of the four sensitivity analysis tasks.

== Evaluation Part B: User Study ==
In Part B, the authors followed a more qualitative approach by '''observing subjects using the tool in a real decision-making context'''. In this second part of the study, they attempted to measure the users’ insight in the decision problem.
Once the subjects had completed Part B, they ﬁlled out a questionnaire regarding their experience with VC+ in the decision-making process. In our exploratory study we measure the amount of insight each sub ject gains from using VC+ for a particular decision-making scenario.
In the exploratory study the amount of insight each sub ject gains from using VC+ for a particular decision-making scenario is measured.
'''Insights''' are characterized by the following:

'''Fact''': The actual ﬁnding about the data (e.g. “Samsung [cell phones] are the smallest”)

'''Value''': How to measure each insight? The authors determined and coded the value of each insight from 1 - 3, whereas simple observations of domain value and top ranking (e.g. “cheapest place is in East Van”) are fairly trivial, and more global observations regarding relationships and comparison (e.g. “more expensive phones have all the features”) are more valuable.

'''Category: Insights were grouped into several categories:

– Simple fact: an alternative rank or identiﬁcation of domain value e.g. “This phone is fairly light”, “This phone is only [ranked] fourth for battery”

– Sensitivity: how a change aﬀects the results e.g. “This house again!”, “Now this phone is third”

– Realization of personal preferences: users often stated that they made a realization about their preferences e.g. “it makes sense, because I really like hiking and nature”, “brand should be more important [to me]”

The '''Datasets''' the subjects were allowed to choose were: '''house rental''', '''cell phone''' and '''tourism'''.

There were more insights counted for the vertical interface, which also fared better when value factor was considered. There were more sensitivity analysis related value-changes in the vertical version, because the interface was more inviting to do so.

In a '''post-study questionnaire''' it turned out, that all of the subjects were generally satisfied with their decision. Subjects felt that VC+ was a good tool for learning about their preferences in the selected domain. This was tied closely to insights as well, as we found a signiﬁcant positive correlation between the rating of this question and insight. Persons, that didn't have much exposure to decision analysis reported that they learned how to analize a decision model. All subjects thought that VC+ is useful, intuitive, easy to use and quick to learn. However, the lack of statistical signiﬁcance for the diﬀerence in insights (count and value) indicates the need for a larger experiment.

=References=
[http://portal.acm.org/citation.cfm?doid=1385569.1385603 An empirical evaluation of interactive visualizations for preferential choice]

[[category: techniques]]

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Information Visualization

2011-09-08T03:24:18Z

Thietkeweb:

{{Definition|'''Information visualization ''(InfoVis)''''' produces (interactive) visual representations of [[abstract data]] to reinforce human cognition; thus enabling the viewer to gain knowledge about the internal structure of the data and causal relationships in it.}}

{{Definition|'''Information visualization ''(InfoVis)''''' is the communication of [[abstract data]] through the use of interactive visual interfaces. [Keim et al., 2006]}}

== Definitions ==

{{Quotation|Compact graphical presentation and user interface for
*manipulating large numbers of items
*possibly extracted from far larger datasets
Enables users to make
*discoveries,
*decisions, or
*explanations
*[http://www.gather.com/viewArticle.action?articleId=281474979624859 boils]
about
*patterns (trend, cluster, gap, outlier...),
*groups of items, or
*individual items.|[Plaisant, 2001]}}

{{Quotation|The use of computer-supported, interactive, visual representations of [[abstract data]] to amplify [[cognition]].|[Card et al., 1999]}}

{{Quotation|Information visualization utilizes computer graphics and [[interaction]] to assist humans in solving problems.|[Purchase et al., 2008, p. 58]}}

{{Quotation|Information visualization is a set of technologies that use visual computing to amplify human [[cognition]] with abstract information.|[Card, 2008, p. 542]}}

{{Quotation|Information visualization promises to help us speed our understanding and action in a world of increasing information volumes.|[Card, 2008, p. 542]}}

{{Quotation|The purpose of information visualization is to amplify cognitive performance, not just to create interesting pictures. Information visualizations should do for the mind what automobiles do for the feet.|[Card, 2008, p. 539]}}

{{Quotation|Information visualizations attempt to efficiently map data variables onto visual dimensions in order to create graphic representations.|[Gee et al., 2005]}}

{{Quotation|Information visualization, an increasingly important subdiscipline within [[HCI (Human-Computer Interaction)|HCI]], focuses on graphical mechanisms designed to show the structure of information and improve the cost of access to large data repositories. In printed form, information visualization has included the display of numerical data (e.g., bar charts, plot charts, pie charts), combinatorial relations (e.g., drawings of graphs), and geographic data (e.g., encoded maps). Computer-based systems, such as the information visualizer and [[Dynamic query|dynamic queries]] have added interactivity and new visualization techniques (e.g., 3D, animation).|[Averbuch, 2004]}}

{{Quotation|Visual representations of the semantics, or meaning, of information. In contrast to [[Scientific Visualization|scientific visualization]], information visualization typically deals with nonnumeric, nonspatial, and high-dimensional data.|[Chen, 2005]}}

{{Quotation|A method of presenting data or information in non-traditional, interactive graphical forms. By using 2-D or 3-D color graphics and animation, these visualizations can show the structure of information, allow one to navigate through it, and modify it with graphical interactions.|[UIUC DLI, 1998]}}

{{Quotation|As a subject in computer science, information visualization is the use of interactive, sensory representations, typically visual, of abstract data to reinforce cognition. Information visualization is a complex research area. It builds on theory in [[information design]], computer graphics, human-computer interaction and cognitive science. Practical application of information visualization in computer programs involves selecting, transforming and representing abstract data in a form that facilitates human interaction for exploration and understanding. Important aspects of information visualization are the interactivity and dynamics of the visual representation. Strong techniques enable the user to modify the visualization in real-time, thus affording unparalleled perception of patterns and structural relations in the abstract data in question. 
Although much work in information visualization regards to visual forms, auditory and other sensory representations are also of concern.|[Wikipedia, 2005]}}
<center>''Read full article on [http://www.diamondlinks.net link building service] [http://seoph2.cafe24.com/wordpress seo blog] [[wikipedia:Information visualization|Wikipedia]]''</center>

{{Quotation|The study of how to effectively present information visually. Much of the work in this field focuses on creating innovative graphical displays for complicated datasets, such as census results, scientific data, and databases. An example problem would be deciding how to display the pages on a website or the files on a hard disk. Visualization techniques include selective hiding of data, layering data, taking advantage of 3-dimensional space, using scaling techniques to provide more space for more important information (e.g. Fisheye views), and taking advantage of psychological principles of layout, such as proximity, alignment, and shared visual properties (e.g. color).|[Usability First, 2003]}}

{{Quotation|'''Information visualization''', sometimes called InfoVis, is a special kind of visualization. Visualization is a part of computer graphics, which is in turn a subset of computer science. Visualization is defined as follows [Card et al., 1998]: Visualization is the use of interactive visual representations of data to amplify cognition. This means that the data is transformed into an image, it is mapped to screen space. The image can be changed by users as they proceed working with it. This interaction is important as it allows for constant redefinition of goals when new insight into the data has been gained. Visualization makes use of what is called external [[cognition]] [Card et al., 1998]. External resources are used for thinking. People are relieved from having to imagine everything. Instead they can just look at an image. This is only possible because human vision has a very large bandwidth, the largest of all senses [Card et al., 1998]. Information visualization is visualization of [[abstract data]]. This is data that has no inherent mapping to space. Examples for abstract data are the results of a survey or a database of the staff of a company containing names, addresses, salary and other attributes. Information visualization should be seen in contrast to [[Scientific Visualization|scientific visualization]], which deals with physically-based data. This kind of data is defined in reference to space coordinates, which makes it relatively easy to visualize in an intuitive way. The space coordinates in the dataset are mapped to screen coordinates. Examples are geographic data and computer tomography data of a body. Visualization of abstract data is not straightforward. One has to find a good way to map data values to screen space. It makes a difference whether the data is structured or unstructured. Examples for structured data are networks, software, and algorithms. This kind of data does not play a role in this thesis, only unstructured data is used here. Unstructured data is a collection of records with a number of different criteria in each record. The records can be, for instance, the individual fish in a fish-catch. Of each fish the following criteria can be recorded: species, weight, sex, and different measurements of length [...]. The records are arranged in rows, the criteria make up the columns of a table. The records are also called observations. The criteria are sometimes called variables, and sometimes dimensions. [...]|[Voigt, 2002]}}

{{Quotation|involves abstract, nonspatial data|[Tory and Möller, 2004]}}

{{Quotation|In [[Information Visualization|information visualization]], the graphical models may represent [[Abstract data|abstract]] concepts and relationships that do not necessarily have a counterpart in the physical world, e.g., information describing user accesses to pages of an Internet portal or records describing selected properties of different car brands and models. Typically, each data unity describes multiple related attributes (usually more than four) that are not of a spatial or temporal nature. Although spatial and temporal attributes may occur, the data exists in an abstract (conceptual) data space.|[Ferreira and Levkowitz, 2003]}}

== Overview ==

Application of information visualization on the computer involves providing [http://www.url.vn/2.html/ thiet ke web] means to transform and represent data in a form that allows and encourages human interaction. Data can therefore be analyzed by [[exploratory data analysis|''exploration'']] rather than pure reasoning; users [http://www.seo.url.vn/2.html/ dich vu seo] can develop understanding for structures and connections in the data by observing the immediate effects their interaction has upon the visualization.

[[Image:zook_large.gif|right|thumb|250px|Information Visualization Example]][[Image:boom.gif|right|thumb|250px|Visualization of a directory structure using a botanical model]]

== Examples ==

Information visualization is applied in countless areas covering every industry [http://www.vietnamairlinestickets.com/2.html/ Vietnam Airlines] and all tasks where understanding of the intrinsic structure in data is crucial.

Some prominent examples are:
*Economical/financial analysis
*Representation of large hierarchies
*Medical training/assistance
*Engineering/Physics

== See also ==
*[[Visualization]]
*[[Scientific Visualization]]

== References ==

*[Averbuch, 2004] Michael Averbuch, ''As you Like It: Tailorable Information Visualization'', Database Visualization Research Group, Tufts University, 2004.
*[Card, 2008] Stuart Card, Information visualization, in A. Sears and J.A. Jacko (eds.), The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, Lawrence Erlbaum Assoc Inc, 2007.
*[Card et al., 1999] Card, S. and Mackinlay, J. and Shneiderman, B., Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann Publishers, 1999.
*[Chen, 2005] Chen, C. [http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=31454&arnumber=1463074&count=14&index=3 Top 10 Unsolved Information Visualization Problems], IEEE Computer Graphics and Applications, 25(4):12-16, July-Aug. 2005.
* [Ferreira and Levkowitz, 2003] Maria Cristina Ferreira de Oliveira, Haim Levkowitz, [http://doi.ieeecomputersociety.org/10.1109/TVCG.2003.1207445 From Visual Data Exploration to Visual Data Mining: A Survey], IEEE Transactions on Visualization and Computer Graphics, vol. 9, no. 3, pp. 378-394, July-September, 2003.
*[Gee et al., 2005] Gee, A.G., Yu, M., and Grinstein, G.G., Dynamic and Interactive Dimensional Anchors for Spring-Based Visualizations. Technical Report, Computer Science, University of Massachussetts Lowell.
*[Keim et al., 2006] Keim, D.A.; Mansmann, F. and Schneidewind, J. and Ziegler, H., Challenges in Visual Data Analysis, Proceedings of Information Visualization (IV 2006), IEEE, p. 9-16, 2006.
*[Plaisant, 2001] Plaisant, C., Information Visualization - Lecture Notes, Created at: November 2001.
*[Purchase et al., 2008] Purchase, H. C., Andrienko, N., Jankun-Kelly, T. J., and Ward, M. 2008. Theoretical Foundations of Information Visualization. In information Visualization: Human-Centered Issues and Perspectives, A. Kerren, J. T. Stasko, J. Fekete, and C. North, Eds. Lecture Notes In Computer Science, vol. 4950. Springer-Verlag, Berlin, Heidelberg, 46-64. DOI= http://dx.doi.org/10.1007/978-3-540-70956-5_3
*[Tory and Möller, 2004] Melanie Tory and Torsten Möller, Human Factors in Visualization Research, ''IEEEE Transactions on Visualization and Computer Graphics'', 10(1):72-84, January/February 2004.
*[UIUC DLI, 1998] University of Illinois at Urbana-Champaign Digital Libraries Initiative, UIUC DLI Glossary. Created: November 23, 1998. http://dli.grainger.uiuc.edu/glossary.htm
*[Usability First, 2003] Usability First, Usability Glossary. Retrieved at: 2003. http://www.usabilityfirst.com/glossary/main.cgi?function=display_term&term_id=5
*[Voigt, 2002]: Robert Voigt, [http://www.vrvis.at/via/resources/DA-RVoigt/masterthesis.html An Extended Scatterplot Matrix and Case Studies in Information Visualization], Master's thesis, Hochschule Magdeburg-Stendal, 2002, [http://www.vrvis.at/vis/resources/DA-RVoigt/node4.html ''Classification and Definition of Terms'']
*[Wikipedia, 2005] Wikipedia, Information visualization. Retrieved at: July 19, 2005. http://en.wikipedia.org/wiki/Information_visualization

== External links ==

*http://www.math.yorku.ca/SCS/Gallery/ has a lot of (positive and negative) examples including historical milestones.

[[Category:Glossary]]

Information Visualization

2011-09-08T03:21:49Z

Thietkeweb:

{{Definition|'''Information visualization ''(InfoVis)''''' produces (interactive) visual representations of [[abstract data]] to reinforce human cognition; thus enabling the viewer to gain knowledge about the internal structure of the data and causal relationships in it.}}

{{Definition|'''Information visualization ''(InfoVis)''''' is the communication of [[abstract data]] through the use of interactive visual interfaces. [Keim et al., 2006]}}

== Definitions ==

{{Quotation|Compact graphical presentation and user interface for
*manipulating large numbers of items
*possibly extracted from far larger datasets
Enables users to make
*discoveries,
*decisions, or
*explanations
*[http://www.gather.com/viewArticle.action?articleId=281474979624859 boils]
about
*patterns (trend, cluster, gap, outlier...),
*groups of items, or
*individual items.|[Plaisant, 2001]}}

{{Quotation|The use of computer-supported, interactive, visual representations of [[abstract data]] to amplify [[cognition]].|[Card et al., 1999]}}

{{Quotation|Information visualization utilizes computer graphics and [[interaction]] to assist humans in solving problems.|[Purchase et al., 2008, p. 58]}}

{{Quotation|Information visualization is a set of technologies that use visual computing to amplify human [[cognition]] with abstract information.|[Card, 2008, p. 542]}}

{{Quotation|Information visualization promises to help us speed our understanding and action in a world of increasing information volumes.|[Card, 2008, p. 542]}}

{{Quotation|The purpose of information visualization is to amplify cognitive performance, not just to create interesting pictures. Information visualizations should do for the mind what automobiles do for the feet.|[Card, 2008, p. 539]}}

{{Quotation|Information visualizations attempt to efficiently map data variables onto visual dimensions in order to create graphic representations.|[Gee et al., 2005]}}

{{Quotation|Information visualization, an increasingly important subdiscipline within [[HCI (Human-Computer Interaction)|HCI]], focuses on graphical mechanisms designed to show the structure of information and improve the cost of access to large data repositories. In printed form, information visualization has included the display of numerical data (e.g., bar charts, plot charts, pie charts), combinatorial relations (e.g., drawings of graphs), and geographic data (e.g., encoded maps). Computer-based systems, such as the information visualizer and [[Dynamic query|dynamic queries]] have added interactivity and new visualization techniques (e.g., 3D, animation).|[Averbuch, 2004]}}

{{Quotation|Visual representations of the semantics, or meaning, of information. In contrast to [[Scientific Visualization|scientific visualization]], information visualization typically deals with nonnumeric, nonspatial, and high-dimensional data.|[Chen, 2005]}}

{{Quotation|A method of presenting data or information in non-traditional, interactive graphical forms. By using 2-D or 3-D color graphics and animation, these visualizations can show the structure of information, allow one to navigate through it, and modify it with graphical interactions.|[UIUC DLI, 1998]}}

{{Quotation|As a subject in computer science, information visualization is the use of interactive, sensory representations, typically visual, of abstract data to reinforce cognition. Information visualization is a complex research area. It builds on theory in [[information design]], computer graphics, human-computer interaction and cognitive science. Practical application of information visualization in computer programs involves selecting, transforming and representing abstract data in a form that facilitates human interaction for exploration and understanding. Important aspects of information visualization are the interactivity and dynamics of the visual representation. Strong techniques enable the user to modify the visualization in real-time, thus affording unparalleled perception of patterns and structural relations in the abstract data in question. 
Although much work in information visualization regards to visual forms, auditory and other sensory representations are also of concern.|[Wikipedia, 2005]}}
<center>''Read full article on [http://www.diamondlinks.net link building service] [http://seoph2.cafe24.com/wordpress seo blog] [[wikipedia:Information visualization|Wikipedia]]''</center>

{{Quotation|The study of how to effectively present information visually. Much of the work in this field focuses on creating innovative graphical displays for complicated datasets, such as census results, scientific data, and databases. An example problem would be deciding how to display the pages on a website or the files on a hard disk. Visualization techniques include selective hiding of data, layering data, taking advantage of 3-dimensional space, using scaling techniques to provide more space for more important information (e.g. Fisheye views), and taking advantage of psychological principles of layout, such as proximity, alignment, and shared visual properties (e.g. color).|[Usability First, 2003]}}

{{Quotation|'''Information visualization''', sometimes called InfoVis, is a special kind of visualization. Visualization is a part of computer graphics, which is in turn a subset of computer science. Visualization is defined as follows [Card et al., 1998]: Visualization is the use of interactive visual representations of data to amplify cognition. This means that the data is transformed into an image, it is mapped to screen space. The image can be changed by users as they proceed working with it. This interaction is important as it allows for constant redefinition of goals when new insight into the data has been gained. Visualization makes use of what is called external [[cognition]] [Card et al., 1998]. External resources are used for thinking. People are relieved from having to imagine everything. Instead they can just look at an image. This is only possible because human vision has a very large bandwidth, the largest of all senses [Card et al., 1998]. Information visualization is visualization of [[abstract data]]. This is data that has no inherent mapping to space. Examples for abstract data are the results of a survey or a database of the staff of a company containing names, addresses, salary and other attributes. Information visualization should be seen in contrast to [[Scientific Visualization|scientific visualization]], which deals with physically-based data. This kind of data is defined in reference to space coordinates, which makes it relatively easy to visualize in an intuitive way. The space coordinates in the dataset are mapped to screen coordinates. Examples are geographic data and computer tomography data of a body. Visualization of abstract data is not straightforward. One has to find a good way to map data values to screen space. It makes a difference whether the data is structured or unstructured. Examples for structured data are networks, software, and algorithms. This kind of data does not play a role in this thesis, only unstructured data is used here. Unstructured data is a collection of records with a number of different criteria in each record. The records can be, for instance, the individual fish in a fish-catch. Of each fish the following criteria can be recorded: species, weight, sex, and different measurements of length [...]. The records are arranged in rows, the criteria make up the columns of a table. The records are also called observations. The criteria are sometimes called variables, and sometimes dimensions. [...]|[Voigt, 2002]}}

{{Quotation|involves abstract, nonspatial data|[Tory and Möller, 2004]}}

{{Quotation|In [[Information Visualization|information visualization]], the graphical models may represent [[Abstract data|abstract]] concepts and relationships that do not necessarily have a counterpart in the physical world, e.g., information describing user accesses to pages of an Internet portal or records describing selected properties of different car brands and models. Typically, each data unity describes multiple related attributes (usually more than four) that are not of a spatial or temporal nature. Although spatial and temporal attributes may occur, the data exists in an abstract (conceptual) data space.|[Ferreira and Levkowitz, 2003]}}

== Overview ==

Application of information visualization on the computer involves providing [http://www.url.vn/2.html/ thiet ke web] means to transform and represent data in a form that allows and encourages human interaction. Data can therefore be analyzed by [[exploratory data analysis|''exploration'']] rather than pure reasoning; users [http://www.seo.url.vn/2.html/ dich vu seo] can develop understanding for structures and connections in the data by observing the immediate effects their interaction has upon the visualization.

[[Image:zook_large.gif|right|thumb|250px|Information Visualization Example]][[Image:boom.gif|right|thumb|250px|Visualization of a directory structure using a botanical model]]

== Examples ==

Information visualization is applied in countless areas covering every industry and all tasks where understanding of the intrinsic structure in data is crucial.

Some prominent examples are:
*Economical/financial analysis
*Representation of large hierarchies
*Medical training/assistance
*Engineering/Physics

== See also ==
*[[Visualization]]
*[[Scientific Visualization]]

== References ==

*[Averbuch, 2004] Michael Averbuch, ''As you Like It: Tailorable Information Visualization'', Database Visualization Research Group, Tufts University, 2004.
*[Card, 2008] Stuart Card, Information visualization, in A. Sears and J.A. Jacko (eds.), The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications, Lawrence Erlbaum Assoc Inc, 2007.
*[Card et al., 1999] Card, S. and Mackinlay, J. and Shneiderman, B., Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann Publishers, 1999.
*[Chen, 2005] Chen, C. [http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=31454&arnumber=1463074&count=14&index=3 Top 10 Unsolved Information Visualization Problems], IEEE Computer Graphics and Applications, 25(4):12-16, July-Aug. 2005.
* [Ferreira and Levkowitz, 2003] Maria Cristina Ferreira de Oliveira, Haim Levkowitz, [http://doi.ieeecomputersociety.org/10.1109/TVCG.2003.1207445 From Visual Data Exploration to Visual Data Mining: A Survey], IEEE Transactions on Visualization and Computer Graphics, vol. 9, no. 3, pp. 378-394, July-September, 2003.
*[Gee et al., 2005] Gee, A.G., Yu, M., and Grinstein, G.G., Dynamic and Interactive Dimensional Anchors for Spring-Based Visualizations. Technical Report, Computer Science, University of Massachussetts Lowell.
*[Keim et al., 2006] Keim, D.A.; Mansmann, F. and Schneidewind, J. and Ziegler, H., Challenges in Visual Data Analysis, Proceedings of Information Visualization (IV 2006), IEEE, p. 9-16, 2006.
*[Plaisant, 2001] Plaisant, C., Information Visualization - Lecture Notes, Created at: November 2001.
*[Purchase et al., 2008] Purchase, H. C., Andrienko, N., Jankun-Kelly, T. J., and Ward, M. 2008. Theoretical Foundations of Information Visualization. In information Visualization: Human-Centered Issues and Perspectives, A. Kerren, J. T. Stasko, J. Fekete, and C. North, Eds. Lecture Notes In Computer Science, vol. 4950. Springer-Verlag, Berlin, Heidelberg, 46-64. DOI= http://dx.doi.org/10.1007/978-3-540-70956-5_3
*[Tory and Möller, 2004] Melanie Tory and Torsten Möller, Human Factors in Visualization Research, ''IEEEE Transactions on Visualization and Computer Graphics'', 10(1):72-84, January/February 2004.
*[UIUC DLI, 1998] University of Illinois at Urbana-Champaign Digital Libraries Initiative, UIUC DLI Glossary. Created: November 23, 1998. http://dli.grainger.uiuc.edu/glossary.htm
*[Usability First, 2003] Usability First, Usability Glossary. Retrieved at: 2003. http://www.usabilityfirst.com/glossary/main.cgi?function=display_term&term_id=5
*[Voigt, 2002]: Robert Voigt, [http://www.vrvis.at/via/resources/DA-RVoigt/masterthesis.html An Extended Scatterplot Matrix and Case Studies in Information Visualization], Master's thesis, Hochschule Magdeburg-Stendal, 2002, [http://www.vrvis.at/vis/resources/DA-RVoigt/node4.html ''Classification and Definition of Terms'']
*[Wikipedia, 2005] Wikipedia, Information visualization. Retrieved at: July 19, 2005. http://en.wikipedia.org/wiki/Information_visualization

== External links ==

*http://www.math.yorku.ca/SCS/Gallery/ has a lot of (positive and negative) examples including historical milestones.

[[Category:Glossary]]