InfoVis:Wiki - User contributions [en]

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04 - Aufgabe 4

2010-01-06T19:57:58Z

UE-InfoVis0910 0325870:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe4.html Beschreibung der Aufgabe 4]
=== Zu erstellende Visualisierung ===
-------------------------------
* Stammbaum der Nachkommen von Lisa und Bart Simpson*

...Visualisierung der Nachkommen von Lisa Simpson sowie der Nachkommen von Bart Simpson. Dabei sollen zwei Stammbäume entstehen - einer von Bart und einer von Lisa - die dann miteinander verglichen werden können. Zuerst kommen Lisa und Bart, dann deren Kinder, ihre Enkel, etc. (mind 4 Generationen). Da es noch keine Nachkommen gibt, können diese frei erfunden werden.

Die Visualisierung soll folgende Informationen darstellen:

- Verwandtschaftsverhältnisse (zumindest Eltern-Kinder),

- Unterscheidung zwischen Blutsverwandtschaft und angeheirateten Familienmitgliedern,

- Geburts- und Todestag sowie Lebensdauer von allen Familienmitgliedern,

- wichtige Ereignisse im Leben jedes Familienmitglieds (z.B., Anzeigen, Gefängnisaufenthalte, Schulzeit, Studienzeit, Nobelpreise, Arbeitslosigkeit etc.)

- Zufriedenheit jedes Familienmitglieds (Skala: sehr niedrig - niedrig - mittel - hoch - sehr hoch); kann sich im Laufe des Lebens ändern.

Die Visualisierung soll die interaktive Auseinandersetzung mit den Daten ermöglichen.
Verpflichtend:
Möglichkeiten zum besseren Vergleich von einzelnen Abschnitten der Stammbäume bzw. Vergleich von Ausschnitten aus Lisas und Barts Stammbäumen.
+ mind. 2 weitere Interaktionsmöglichkeiten (z.B., Details on Demand, Filteroptionen)

Allgemein:

- Die Daten sollen zur Analyse von Zusammenhängen zwischen Familienverhältnissen, wichtigen Ereignissen und Zufriedenheit visualisiert werden (die Anwendungsgebiets- und Zielgruppenanalyse kann kurz gehalten werden).

- Die bisher erlernten Design-Prinzipien sollen umgesetzt werden z.B.: Optimierung der Data-ink ratio (keine Comics!), visuelle Attribute (Größe, Farbe, Position, etc.) sollen sinnvoll eingesetzt werden (Information darstellen).

- Die Mockups sollten zumindest 1) die beiden Stammbäume im Überblick und 2) eine detaillierte Vergleichsansicht von 2 Teil-Stammbäumen wiedergeben.

- Alle nicht angeführten Daten können frei erfunden werden.

------------------------------

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2009/10|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04|Gruppe 04]]

--------------------------------

== part a ==

=== 1. used application area and datas===

==== application analysis ====
* The used family tree analysis helps to visualise Bart and Lisa Simpsons´ pedigree until the fourth generation including detailed information like the date of birth, relations inbetween each others, etc. according to each certain person. Supportingly color represents the satisfaction of each family member. The final tree should allow to compare single sections with each others.
* In the visualization of the family tree you find the following implementation of the information:
** Gender --> (color?)
** Name -->
** Relations --> (lines?)
** Date of birth/death --> ?
** Satisfaction -->
** Special events -->

==== data analysis ====
* bla
* bla

=== 2. analysis of the target group ===
* In general this type of visualization of the family tree mainly adresses future family members of psychoterapists.
* The information you gain concerns information in general that is very superficial and unpersonal, whereas the color underlined envelopment of satisfaction gives a very personal view of the family member. For this reason it will just be related persons or professions with an interest of the psychical side of a person and their past.
* Different ways of visualization:
** The most current and best known way of the visualization of relations between families is the form of a directed graph (gerichteter Graph), whereby generations are ranked by the age from down to up with the eldest family member on the bottom of the graph.
** The pedigree (Stammtafel) puts a person on the top of it to form the basis for the following members. The information is limited to the surnames and their marriage partners. The pedigree becomes confusing and unclear in the moment the family grows.
** Another display format of a listed pedigree (Stammliste/ Nachfahrenliste) which includes just family members with the same surname.

=== 3. aim of visualization ===
* The aim of visualization is the creation of a clear, fast reproducible graphic to recognize relations between family members easy and clear. The additional information you get out of the visualization depends on the type of graph you chose.
* Unclear relations between family members may be recognized fast. i.e. adpoted children or stepparents.
* Different relationships that are possible between the members of a family should be shown in a graphic.

== Concept ==
Our program has multiple views which can be used to present the data needed for a certain purpose, because not all data available is needed all the time.

Our first view is a classical hierarchical presentation of the family tree.

This view is used to get a brief overview of the family tree, how big it is, who is related by blood and who is not.

On the right side of the screen is an information area which presents all available information for the current selected person. The data displayed can be configured.
The current selected person is colored, e.g. in light blue.

The screen is devided by horizontal lines, each section is presenting a new generation.

Connected horizontal names represent a relationship of the two.
Conntected vertical names represent the parent - child relationship, which also represents a relation by blood.

When moving over a name with the mouse a new, small, window is displayed, which contains some detailed informations of the specific person.
Details like mood, sex, birthdate, deathdate and age are displayed.

Sex is displayed in the top left corner.
The international symbols for man and woman are used.

The mood is displayed in the top right corner.

There are 5 different moods available.
* Very high - displayed with a happy smiley and the color green
* High - displayed with a happy smiley and the color light green
* Medium - displayed with a neutral smiley and the color yellow
* Low - displayed with a sad smiley and the color orange
* Very low - displayed with a sad smiley and the color red

In this view its possible to arrange 2 or more persons with their family trees.
It is, for example, possible to arrange the family trees of Bard and Lisa side by side
to compare the two family trees.

[[Image:Stammbaum01.png]]

For more detailed comparisons its possible to compare different Persons or a whole generation
whith just a single click.

To get to the next screenshot simply double click on an empty space in the first generation of the family tree.

In the detailed comparison of a generation, e.g. the first generation of the family tree, we have a kind of timeline.
From left to right is a timeline, which is used to "sort" the happenings according to their appearance.
From top to bottom are all family members from the first generation.

In the comparison we have a "container" which contains a short information of the happening, e.g. Grammar School.

The start and the end of the timeline depends on the data available. If the data is available its also possible to compare the whole life of the selected generation / persons.
The start of each container represents the date when it happened and the length represents the duration of the happening.
Happenings that have a too short duration to have a container get a dot.

The timeline can be scalled to be more detailed or to just get an overview over a whole life.
According to the granularity of the timeline, the containers are adjusted automatically to fit the actual timeline.

To get detailed information of a specific happening you just have to move the mouse over the container and a new window with detailed informations is displayed.
To get all informations available for a specific happening, you can just double click on a container and a new window is opened with all informations, background informations, links (if possible),...

To represent the mood in the timeline, there is a special colored line which represents the mood at a specific point in time.

[[Image:Stammbaum02.png]]

File:Stammbaum02.png

2010-01-06T19:53:17Z

UE-InfoVis0910 0325870: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

File:Stammbaum01.png

2010-01-06T19:52:59Z

UE-InfoVis0910 0325870: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

Teaching talk:TUW - UE InfoVis WS 2009/10 - Gruppe 04 - Aufgabe 4

2010-01-06T19:52:20Z

UE-InfoVis0910 0325870: New page: Forgot to login "20:50, 6 January 2010 213.47.67.136 " is from User UE-InfoVis0910 0325870

Forgot to login "20:50, 6 January 2010 213.47.67.136 " is from User UE-InfoVis0910 0325870

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04 - Aufgabe 3

2009-12-09T14:12:30Z

UE-InfoVis0910 0325870:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe3.html Beschreibung der Aufgabe 3]
=== Zu verbessernde Grafik ===
------------------------------- 
[[Image:geo1.jpg]]

== Ausarbeitung ==

=== Criticism ===

==== Steven Few, Show me the numbers: ====
* Some of the circles cover the map behind which are supposed to convey important information. Thus decreasing or putting next to the map with a line connected to the country it is concerned to.
* The shape of a circle covers way too much information. The usage of a bar would give the same information without blocking that much.
* The real message/data is not supported in the Graph, which is against the task of visualizing quantitative information.
* It is not clear, which data has priority. TOE/capita? The sources of energy?
* There is no direction for order in which data should be read.
* The information-scale on the left can´t be taken as a referenc, because it is too far away from the circles.To decode the size of the circles it takes several steps: first compare the size with the scale, which is hard to tell, when the circles have no extreme. Secondly you have to look for an explanation for the shortcut "TOE". It is put somewhere under a title insted of underneath or put in parenthesis next to the TOE.
* The many colors are not necessary. It is better to work with forms and sizes.
* We are more sensitive to the vertical and horizontal alignment than we might imagine. Therefor we should use it to give more weight to the real content of the graphic.
* The unstrucstured ordering of the circled makes it difficult to separately scan the major content.

==== Steven Few, Elegance Through Simplicity: ====
* There is too much unnecessary ink used for the graphic, because the important values need less place in contrary to the borders around them. The greater the ratio of the ink that you use to communicate the data to the total amount of ink in the table or graph, the better you´ve highlighted the data.

=== Corrections ===
* The graph is reorganized by dividing the graph in three parts:
** TOE/capita plot in bars
** Types of energy by colors. Besides is the color scale - not round.
** Some explaning lines are added.
* The essential message of the graph and additional information is clarified.
** Main message: Most of the sources of energy is based on Fossil Fuel.
** Additional Message: Does the size of the country has a bigger effect on the used energy or does it more depend on the richness there or the mental relation to energy.
* The TOE/capita scale on the left side of the bars helps to read the scale. It does not go until infinity, but until the highest information we have. There is no need for an y-axis.
** Additionally the bars are ordered by their size. That way it is easier to compare and to read the content.
* Objects positioned on the right are less seen that objects positioned on the top. Therefor we put the titles underneath each other to introduce the graph.

=== New Table ===

==== Energy Production per Capita (2004) ====

[[Image:Tabelle03.png]]

=== References ===
* Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Chapter 7 - General Design for Communication.
* Stephen Few, Elegance Through Simplicity, intelligent enterprise, Oct 16, 2004.

File:Tabelle03.png

2009-12-09T14:09:20Z

UE-InfoVis0910 0325870: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04 - Aufgabe 3

2009-12-07T17:10:28Z

UE-InfoVis0910 0325870:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe3.html Beschreibung der Aufgabe 3]
=== Zu verbessernde Grafik ===
------------------------------- 
[[Image:geo1.jpg]]

== Ausarbeitung ==

=== Criticism ===

==== Steven Few, Show me the numbers: ====
* Some of the circles cover the map behind which are supposed to convey important information. Thus decreasing or putting next to the map with a line connected to the country it is concerned to.
* The shape of a circle covers way too much information. The usage of a bar would give the same information without blocking that much.
* The real message/data is not supported in the Graph, which is against the task of visualizing quantitative information.
* It is not clear, which data has priority. TOE/capita? The sources of energy?
* There is no direction for order in which data should be read.
* The information-scale on the left can´t be taken as a referenc, because it is too far away from the circles.To decode the size of the circles it takes several steps: first compare the size with the scale, which is hard to tell, when the circles have no extreme. Secondly you have to look for an explanation for the shortcut "TOE". It is put somewhere under a title insted of underneath or put in parenthesis next to the TOE.
* The many colors are not necessary. It is better to work with forms and sizes.
* We are more sensitive to the vertical and horizontal alignment than we might imagine. Therefor we should use it to give more weight to the real content of the graphic.
* The unstrucstured ordering of the circled makes it difficult to separately scan the major content.

==== Steven Few, Elegance Through Simplicity: ====
* There is too much unnecessary ink used for the graphic, because the important values need less place in contrary to the borders around them. The greater the ratio of the ink that you use to communicate the data to the total amount of ink in the table or graph, the better you´ve highlighted the data.

=== Corrections ===
* The graph is reorganized by dividing the graph in three parts:
** TOE/capita plot in bars
** Types of energy by colors. Besides is the color scale - not round.
** Some explaning lines are added.
* The essential message of the graph and additional information is clarified.
** Main message: Most of the sources of energy is based on Fossil Fuel.
** Additional Message: Does the size of the country has a bigger effect on the used energy or does it more depend on the richness there or the mental relation to energy.
* The TOE/capita scale on the left side of the bars helps to read the scale. It does not go until infinity, but until the highest information we have. There is no need for an y-axis.
** Additionally the bars are ordered by their size. That way it is easier to compare and to read the content.
* Objects positioned on the right are less seen that objects positioned on the top. Therefor we put the titles underneath each other to introduce the graph.

=== New Table ===

[[Image:Tabelle02_03.png]]

=== References ===
* Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Chapter 7 - General Design for Communication.
* Stephen Few, Elegance Through Simplicity, intelligent enterprise, Oct 16, 2004.

File:Tabelle02 03.png

2009-12-07T17:09:17Z

UE-InfoVis0910 0325870: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04 - Aufgabe 2

2009-11-19T18:50:33Z

UE-InfoVis0910 0325870:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe2.html Beschreibung der Aufgabe 2]
=== Zu beurteilende Tabelle ===
[[Image:imageRRH.JPG]]

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2009/10|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04|Gruppe 04]]

== Ausarbeitung ==

=== Criticism ===
* The column "Rank" is used too often. Multiple "Rank" columns would be necessary if the table spreads over more pages, but this is not the case.
* It is not obvious what "t" and "alpha" means.
**Same with the title "All Ss", as long as there is no column called "S". The reader can just guess what "S" means.
* Rankings should always be sorted, so that the reader needs less time to read the table.
* The allignment of the columns could be better arranged.
* Headers should usualy not be much longer than the longest entry in their column.
** Missing headers are very bad, so hardly noone can imagine what the content means. If the first column doesn´t use any header, the font-style should be at least different to the rest, i.e. just big letters.
** By the way you can argue about the title of the first column, but no title of the second one. You can summarize the two columns, if you chose a title that includes the information that the lines are ordered by a rank.
* Abbreviations should not be used. They cant be understood by someone who is not familiar with the table's content.
* The grid is not necessary. A wisely chosen white-space would be enough.
* Numbers should have the same format, so its easier for the reader.
* Calculations within the table should always be displayed next to the column which is used for calculation.
** The use of a separated space for explanations could also be used for definitions, what HN, t,... means.

=== Improved Table ===

[[Image:Tabelle.jpg]]
[[Image:Tabelle2.jpg]]

=== Explanation of Improvements ===
* Our opinion is, that the table displays the Top 9 of something. So we sorted the table accordingly to their ranking.
* We put the Ranking in the first column, because its better for the reader, so he can quickly see how the ranking is and its not that irritating when reading the table.
* We alligned the columns correctly.
** Numbers should be alligned to the right, because thats easier to read
** Ranking should also be alligned to the right, because the only thing that should be center alligned are values which always have the same size (e.g. "Y", "N").
* All numbers have now the same format. (-xx,yy)
* The usage of alpha is not obvious to an average reader.
**It would be readable for a whider spread if you used % instead --> by the way a nicer format
**Instead of footnotes you could define alpha in the seperated space
* The column "t=" is the only number column which is alligned to the left, because the "*" at the end varies and so its easier for the reader.
* The calculation of the difference between Hard and Soft News is now next to the columns Hard News and Soft News. That way it´s easier to see a connection between them.
** Instead of M1 and M2 we useIt is easier to find a connection if you use HN and SN for Hard and Soft Use instead of new letters (M1, M2). The column "M1-M2" should be better named "difference (HN-SN)". This way you also see how it is calculated.
* We removed the grid, because its not needed. Instead of it we made a light grey line underneath the headers.
* For the readability we sepparated the columns and rows through white-spaces.

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04 - Aufgabe 2

2009-11-18T23:04:54Z

UE-InfoVis0910 0325870:

File:Tabelle.jpg

2009-11-18T23:03:42Z

UE-InfoVis0910 0325870: Erste Version der neuen Tabelle für Abgabe 02

== Summary ==
Erste Version der neuen Tabelle für Abgabe 02
== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04 - Aufgabe 2

2009-11-18T22:47:46Z

UE-InfoVis0910 0325870:

Teaching talk:TUW - UE InfoVis WS 2008/09 - Gruppe 09 - Aufgabe 1 - Types of Data (quantitative vs. qualitative)

2009-11-03T19:13:32Z

UE-InfoVis0910 0325870:

Es wurden noch zwei Unterkategorien zu den quantitativen Daten hinzugefügt.
 
03.11.2009
*Artikel wurde auf Rechtschreibung / Groß- und Kleinschreibung überprüft.
*Überarbeitung der References
**Die References haben nicht ganz den Richtlinien entsprochen.
**Ein Link war sogar komplett unformatiert.
**Neue Reference hinzugefügt.

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 09 - Aufgabe 1 - Types of Data (quantitative vs. qualitative)

2009-11-03T19:12:56Z

UE-InfoVis0910 0325870:

==Definition==
===Qualitative Data===
{{Quotation | The term qualitative is used to describe certain types of information. Qualitative data are described in terms of quality (that is, 'informal' or relative characteristics such as warmth and flavour). This is the converse of quantitative, which more precisely describes data in terms of quantity and often using a numerical figure to represent something in a statement.| Wikipedia, Nov 2008 }}

===Quantitative Data===
{{Quotation | [...] Quantitative data is data measured or identified on a numerical scale. Numerical data can be analysed using statistical methods, and results can be displayed using tables, charts, histograms and graphs. For example, a researcher will ask questions to a participant that include words how often, how many or percentage. The answers of the questions will be numerical. [...] After the data is collected the researcher will make an analysis of the quantitative data and produce statistics.| Wikipedia, Nov 2008 }}
 
Additionally, quantitative data can be divided into ''discrete'' and ''continuous'' data. 
'''Discrete Data''' 

They can take specific numeric values. For Example: shoe size, age,... 

'''Continuous Data''' 
They can take any numerical value. For Example: weigth, width,... 
----

==Qualitative Research==
Qualitative research is used when the goal is a complete and detailed description of the subject. The data is not presented in form of numbers an statistics as in quantitative data, but in the form of words, pictures and objects. Contrary to quantitative methods it is much more subjective, because the observations which are made during a research are strongly dependent on the researcher's interpretation. 
In most cases qualitative methods aim to gather an in-depth understanding of human behavior and the causes that affect human behavior. There are many different ways of doing qualitative research: Qualitative data can e.g. be gained through
*in-depth interviews with persons or groups
*direct observation
*referring to existing written papers.

[[image:689px-Piecharts.svg.png|thumb|[Wikipedia, 2004] This figure shows quantitative data represented in different ways]]
==Quantitative Research==
Quantitative research is done by systematically collecting and analyzing numerical data concerning the subject. The researcher has to know clearly in advance what he/she is looking for, i.e. all aspects of the study have to be carefully designed before the data is collected. Based on the collected quantitative data statistical models can be constructed or hypotheses can be developed. One advantage of quantitative research is, that it tends to stay more objective than qualitative research. But because quantitative methods are only supplied with numerical data, the results often lack details. 
Quantitative research is widely used in both, natural and social sciences.

----

==Example: Qualitative Data vs. Quantitative Data==
In this example the difference between qualitative and quantitative data is shown. Each bullet point describes a certain attribute of a freshman class. On the left side you can see some qualitative data of the class. Obviously this data can only be gained through a close and continuous observation of the class. On the right side quantitative data of the class is shown. It contains purely numerical and statistical data.
<table>
<tr>
<td style="padding-right:15px;">
'''Qualitative data:'''

*friendly demeanors

*civic minded

*environmentalists

*positive school spirit
</td>
<td>
'''Quantitative data:'''

*672 students

*394 girls, 278 boys

*68% on honor roll

*150 students accelerated in mathematics
</td>
</tr>
</table>
 
----

==References==
*[Neill, 2007] James Neill. Qualitative versus Quantitative Research: Key Points in a Classic Debate. Created at: February 28, 2007. Retrieved at: November 24, 2008. http://wilderdom.com/research/QualitativeVersusQuantitativeResearch.html.
*[Roberts, 1999] Donna Roberts. Qualitative vs Quantitative Data. Oswego City School District Regents Exam Prep Center. Created at: 1999. Retrieved at: November 24, 2008. http://www.regentsprep.org/Regents/math/ALGEBRA/AD1/qualquant.htm.
*[Wikipedia, 2006] Crissyd. Numerical data. Created at: February 22, 2006. Retrieved at: November 24, 2008. http://en.wikipedia.org/wiki/Quantitative_data.
*[Wikipedia, 2003] 62.151.154.241. Qualitative data. Created at: August 8, 2003. Retrieved at: November 24, 2008. http://en.wikipedia.org/wiki/Qualitative_data.
*[Wikipedia, 2003] Sheldon Rampton. Quantitative research. Created at: December 05, 2003. Retrieved at: November 24, 2008. http://en.wikipedia.org/wiki/Quantitative_research.
*[Wikipedia, 2003] Trontonian. Qualitative research. Created at: November 18, 2003. Retrieved at: November 24, 2008. http://en.wikipedia.org/wiki/Qualitative_research.
*[Wikipedia, 2004] Chmod007. Pie Chart. Created at: June 15, 2004. Retrieved at: November 26, 2008. http://en.wikipedia.org/wiki/Pie_chart
*[Reynolds, 2001] A. J. Reynolds. Types of Data. Centre for Innovation in Mathematics Teaching. Created at: January, 2001. http://www.cimt.plymouth.ac.uk/projects/mepres/book7/bk7i11/bk7_11i1.htm.
*[Trochim, 2006] William M. K. Trochim. Types of Data. Research Methods Knowledge Base. Created at: October 20, 2006. Visited at: November 03, 2009. http://www.socialresearchmethods.net/kb/datatype.php.

Teaching talk:TUW - UE InfoVis WS 2008/09 - Gruppe 01 - Aufgabe 1 - Visual Encoding

2009-11-03T18:57:38Z

UE-InfoVis0910 0325870: New page: 03.11.2009 *Artikel wurde auf Rechtschreibung / Groß- und Kleinschreibung überprüft *Überarbeitung der References **Die References haben nicht ganz den Richtlinien entsprochen. Eine de...

03.11.2009
*Artikel wurde auf Rechtschreibung / Groß- und Kleinschreibung überprüft
*Überarbeitung der References
**Die References haben nicht ganz den Richtlinien entsprochen. Eine dementsprechende Anpassung wurde vorgenommen
**Ein Link wurde korrigiert, welcher nicht funktioniert hat.

Teaching talk:TUW - UE InfoVis WS 2008/09 - Gruppe 09 - Aufgabe 1 - Types of Data (quantitative vs. qualitative)

2009-11-03T18:56:06Z

UE-InfoVis0910 0325870:

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 09 - Aufgabe 1 - Types of Data (quantitative vs. qualitative)

2009-11-03T18:51:07Z

UE-InfoVis0910 0325870:

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 09 - Aufgabe 1 - Types of Data (quantitative vs. qualitative)

2009-11-03T18:49:28Z

UE-InfoVis0910 0325870:

==Definition==
===Qualitative Data===
{{Quotation | The term qualitative is used to describe certain types of information. Qualitative data are described in terms of quality (that is, 'informal' or relative characteristics such as warmth and flavour). This is the converse of quantitative, which more precisely describes data in terms of quantity and often using a numerical figure to represent something in a statement.| Wikipedia, Nov 2008 }}

===Quantitative Data===
{{Quotation | [...] Quantitative data is data measured or identified on a numerical scale. Numerical data can be analysed using statistical methods, and results can be displayed using tables, charts, histograms and graphs. For example, a researcher will ask questions to a participant that include words how often, how many or percentage. The answers of the questions will be numerical. [...] After the data is collected the researcher will make an analysis of the quantitative data and produce statistics.| Wikipedia, Nov 2008 }}
 
Additionally, quantitative data can be divided into ''discrete'' and ''continuous'' data. 
'''Discrete Data''' 

They can take specific numeric values. For Example: shoe size, age,... 

'''Continuous Data''' 
They can take any numerical value. For Example: weigth, width,... 
----

==Qualitative Research==
Qualitative research is used when the goal is a complete and detailed description of the subject. The data is not presented in form of numbers an statistics as in quantitative data, but in the form of words, pictures and objects. Contrary to quantitative methods it is much more subjective, because the observations which are made during a research are strongly dependent on the researcher's interpretation. 
In most cases qualitative methods aim to gather an in-depth understanding of human behavior and the causes that affect human behavior. There are many different ways of doing qualitative research: Qualitative data can e.g. be gained through
*in-depth interviews with persons or groups
*direct observation
*referring to existing written papers.

[[image:689px-Piecharts.svg.png|thumb|[Wikipedia, 2004] This figure shows quantitative data represented in different ways]]
==Quantitative Research==
Quantitative research is done by systematically collecting and analyzing numerical data concerning the subject. The researcher has to know clearly in advance what he/she is looking for, i.e. all aspects of the study have to be carefully designed before the data is collected. Based on the collected quantitative data statistical models can be constructed or hypotheses can be developed. One advantage of quantitative research is, that it tends to stay more objective than qualitative research. But because quantitative methods are only supplied with numerical data, the results often lack details. 
Quantitative research is widely used in both, natural and social sciences.

----

==Example: Qualitative Data vs. Quantitative Data==
In this example the difference between qualitative and quantitative data is shown. Each bullet point describes a certain attribute of a freshman class. On the left side you can see some qualitative data of the class. Obviously this data can only be gained through a close and continuous observation of the class. On the right side quantitative data of the class is shown. It contains purely numerical and statistical data.
<table>
<tr>
<td style="padding-right:15px;">
'''Qualitative data:'''

*friendly demeanors

*civic minded

*environmentalists

*positive school spirit
</td>
<td>
'''Quantitative data:'''

*672 students

*394 girls, 278 boys

*68% on honor roll

*150 students accelerated in mathematics
</td>
</tr>
</table>
 
----

==References==
[Neill, 2007] James Neill. Qualitative versus Quantitative Research: Key Points in a Classic Debate. Created at: February 28, 2007. Retrieved at: November 24, 2008. http://wilderdom.com/research/QualitativeVersusQuantitativeResearch.html. 
[Roberts, 1999] Donna Roberts. Qualitative vs Quantitative Data. Oswego City School District Regents Exam Prep Center. Created at: 1999. Retrieved at: November 24, 2008. http://www.regentsprep.org/Regents/math/ALGEBRA/AD1/qualquant.htm. 
[Wikipedia, 2006] Crissyd. Numerical data. Created at: February 22, 2006. Retrieved at: November 24, 2008. http://en.wikipedia.org/wiki/Quantitative_data. 
[Wikipedia, 2003] 62.151.154.241. Qualitative data. Created at: August 8, 2003. Retrieved at: November 24, 2008. http://en.wikipedia.org/wiki/Qualitative_data. 
[Wikipedia, 2003] Sheldon Rampton. Quantitative research. Created at: December 05, 2003. Retrieved at: November 24, 2008. http://en.wikipedia.org/wiki/Quantitative_research. 
[Wikipedia, 2003] Trontonian. Qualitative research. Created at: November 18, 2003. Retrieved at: November 24, 2008. http://en.wikipedia.org/wiki/Qualitative_research. 
[Wikipedia, 2004] Chmod007. Pie Chart. Created at: June 15, 2004. Retrieved at: November 26, 2008. http://en.wikipedia.org/wiki/Pie_chart 
[Reynolds, 2001] A. J. Reynolds. Types of Data. Centre for Innovation in Mathematics Teaching. Created at: January, 2001. http://www.cimt.plymouth.ac.uk/projects/mepres/book7/bk7i11/bk7_11i1.htm.

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 01 - Aufgabe 1 - Visual Encoding

2009-11-03T18:24:30Z

UE-InfoVis0910 0325870:

= Visual Encoding =

==Introduction==

Firstly it's a must to mention that visual representations of information has some unsolved problems as well as advantages. 

"The visual display of information allows people to become more easily aware of essential facts, to quickly see regularities and outliers in data, and therefore to develop a deeper understanding of data."[Dourish et al,2003]

Visual representations are widely used not only in business companies, universities, newspapers, etc.. ,but also in daily life these representations are everywhere around us. In this point it comes into question how the visualizations should be built up, to make the visual representations perceptible. Visual encoding and it's techniques help us with this, they are the guide of converting the data into a visual form. 

== Definition ==

Visual encoding aims to represent data in a form that we all can perceive and it consists of some techniques to convert the data into a perceptible form. Thus enabling us to make visualization on any kind of display. For a visualization we need graphical elements to show the syntax of what we mean. 

"Visual encoding is the set of techniques enabling the representation of data on a screen or on paper. It comprises of the use of shapes, colours, areas and other visual variables that make it possible to associate data variables to graphic variables [Dürsteler, 2005]." 

"Visual encodings are mappings of information to display elements [Munzner, 2000]".

== Techniques of Visual Encoding ==

"The fundamental substrate of visualizations is spatial position. Marks such as points, lines, or area-covering elements can be placed on this substrate. These marks can carry additional information independent of their spatial position, such as size, greyscale luminance (brightness) value, surface texture density, color hue, color saturation, curvature, angle, and shape [Munzner, 2000]".

Six kinds of Visual Encoding:

* Space
* Marks
* Connections & Enclosures
* Retinal Properties
* Temporal Encoding
* Viewpoint Transformation

=== Space ===

When you think about an empty paper as the beginning of a visualisation, the space between some possible object is one of the most powerful aspects to give something an important note. It is the space where data is represented in a visualization.

=== Marks ===

Marks are the objects which are needed to represent the data in a visualization, that the data is into a perceptible form. Thus making the perception of the visual representation easier.

"Marks are the visible things that occur in space [Card et al., 1999]." 

"Marks are simply any graphical symbol visible on a display medium [Ignatius and Senay, 1999]." 

There are four types of simple Marks :
* Points
* Lines
* Areas
* Volumes

=== Connections & Enclosures ===

Connections and enclosures build the important dependencies between the objects (marks). Connections show a relationship between objects, while enclosures can also indicate related objects.

=== Retinal Properties ===

Simply retinal properties are all those properties of marks which have something to do with human eye perception. Thus describing how objects (marks) look like.

"A retinal encoding is any variation of the marks that the retina of the eye can perceive besides position [Ignatius and Senay, 1999]." 

"Retinal properties are the informations that are carried by marks, such as crispness, shape, resolution, transperancy, color(value,hue & saturation), grayscale [Munzner, 2000]." 

[[Image:retinal.JPG|center|400px|thumb|[Card et al., 1999] Retinal properties]]

=== Temporal Encoding ===

A temporal encoding shows how the marks' properties vary over time. 

"It's the encoding which depends on the temporal changes of marks (mark position and their retinal properties) [Ignatius and Senay, 1999]".

=== Viewpoint Transformations ===

"Viewpoint transformations modify interactively and augment Visual Structures to turn static presentations into visualizations by establishing graphical parameters to create Views of Visual Structures [Card et al., 1999; Jacko and Sears, 2002]." 

In other words it is the modification of the view position of the viewer (or in general the modification of viewing parameters), thus enhancing the visual perception.

There are three common view transformations :
* Location Probes : Usage of Location in the visual structures to reveal additional info
* Viewpoint control : Moving the viewpoint to create visualisation : Pan,zoom,clip the viewpoint.
* Distortion : Creating a focus area and context area.

[[Image:filmfinder_sm.gif|center|400px|thumb| This is a software which support users to search for films and it includes the techniques above [Waloszek, 2008] FilmFinder]]

==References==

*[Card et al., 1999] Stuart K. Card, Jock D. Mackinlay and Ben Schneiderman. ''Readings in Information Visualization. Using Vision to Think.'' Morgan Kaufman Publ Inc, San Diego, 1999.
* [Dürsteler, 2005] Juan C. Dürsteler. Web Content Mining Visualisation. Created at: November 7, 2005. http://www.infovis.net/printMag.php?num=175&lang=2.
* [Ignatius and Senay, 1999] Ignatius Eve and Senay Hikmet. Data, vocabulary, marks, composition rules and visual perception rules. Created at: February 11, 1999. http://cose.math.bas.bg/Sci_Visualization/visConcepts/concepts/marks.htm.
*[Jacko and Sears, 2002] Julie A. Jacko and Andrew Sears.''The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies.'' Lawrence Erlbaum Assoc in, NY, 2002.
* [Munzner, 2000] Tamara Munzner. Interactive Visualization of Large Graphs and Networks. Dissertation, Stanford University, June 2000. http://graphics.stanford.edu/papers/munzner_thesis/.
* [Waloszek, 2008] Gerd Waloszek. SAP AG, SAP User Experience. Selecting Objects By Attributes – Part II. Created at: October 3, 2008. http://www.sapdesignguild.org/community/design/attributes_2.asp.
* [Dourish et al,2003] Paul Dourish, Alfred Kobsa, Gloria Mark and David Redmiles. Information Visualization Research. Retrieved at: June, 2003. http://www.isr.uci.edu/research-visualization.html.

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 01 - Aufgabe 1 - Visual Encoding

2009-11-03T17:55:12Z

UE-InfoVis0910 0325870:

= Visual Encoding =

==Introduction==

Firstly it's a must to mention that visual representations of information has some unsolved problems as well as advantages. 

"The visual display of information allows people to become more easily aware of essential facts, to quickly see regularities and outliers in data, and therefore to develop a deeper understanding of data."[Dourish et al,2003]

Visual representations are widely used not only in business companies, universities, newspapers, etc.. ,but also in daily life these representations are everywhere around us. In this point it comes into question how the visualizations should be built up, to make the visual representations perceptible. Visual encoding and it's techniques help us with this, they are the guide of converting the data into a visual form. 

== Definition ==

Visual encoding aims to represent data in a form that we all can perceive and it consists of some techniques to convert the data into a perceptible form. Thus enabling us to make visualization on any kind of display. For a visualization we need graphical elements to show the syntax of what we mean. 

"Visual encoding is the set of techniques enabling the representation of data on a screen or on paper. It comprises of the use of shapes, colours, areas and other visual variables that make it possible to associate data variables to graphic variables [Dürsteler, 2005]." 

"Visual encodings are mappings of information to display elements [Munzner, 2000]".

== Techniques of Visual Encoding ==

"The fundamental substrate of visualizations is spatial position. Marks such as points, lines, or area-covering elements can be placed on this substrate. These marks can carry additional information independent of their spatial position, such as size, greyscale luminance (brightness) value, surface texture density, color hue, color saturation, curvature, angle, and shape [Munzner, 2000]".

Six kinds of Visual Encoding:

* Space
* Marks
* Connections & Enclosures
* Retinal Properties
* Temporal Encoding
* Viewpoint Transformation

=== Space ===

When you think about an empty paper as the beginning of a visualisation, the space between some possible object is one of the most powerful aspects to give something an important note. It is the space where data is represented in a visualization.

=== Marks ===

Marks are the objects which are needed to represent the data in a visualization, that the data is into a perceptible form. Thus making the perception of the visual representation easier.

"Marks are the visible things that occur in space [Card et al., 1999]." 

"Marks are simply any graphical symbol visible on a display medium [Ignatius and Senay, 1999]." 

There are four types of simple Marks :
* Points
* Lines
* Areas
* Volumes

=== Connections & Enclosures ===

Connections and enclosures build the important dependencies between the objects (marks). Connections show a relationship between objects, while enclosures can also indicate related objects.

=== Retinal Properties ===

Simply retinal properties are all those properties of marks which have something to do with human eye perception. Thus describing how objects (marks) look like.

"A retinal encoding is any variation of the marks that the retina of the eye can perceive besides position [Ignatius and Senay, 1999]." 

"Retinal properties are the informations that are carried by marks, such as crispness, shape, resolution, transperancy, color(value,hue & saturation), grayscale [Munzner, 2000]." 

[[Image:retinal.JPG|center|400px|thumb|[Card et al., 1999] Retinal properties]]

=== Temporal Encoding ===

A temporal encoding shows how the marks' properties vary over time. 

"It's the encoding which depends on the temporal changes of marks (mark position and their retinal properties) [Ignatius and Senay, 1999]".

=== Viewpoint Transformations ===

"Viewpoint transformations modify interactively and augment Visual Structures to turn static presentations into visualizations by establishing graphical parameters to create Views of Visual Structures [Card et al., 1999; Jacko and Sears, 2002]." 

In other words it is the modification of the view position of the viewer (or in general the modification of viewing parameters), thus enhancing the visual perception.

There are three common view transformations :
* Location Probes : Usage of Location in the visual structures to reveal additional info
* Viewpoint control : Moving the viewpoint to create visualisation : Pan,zoom,clip the viewpoint.
* Distortion : Creating a focus area and context area.

[[Image:filmfinder_sm.gif|center|400px|thumb| This is a software which support users to search for films and it includes the techniques above [Waloszek, 2008] FilmFinder]]

==References==

*[Card et al., 1999] Stuart K. Card, Jock D. Mackinlay, and Ben Schneiderman. ''Readings in Information Visualization. Using Vision to Think.''Morgan Kaufman Publ Inc, San Diego, 1990.
* [Dürsteler, 2005] Juan C. Dürsteler, Web Content Mining Visualisation. Created at: November 7. http://www.infovis.net/printMag.php?num=175&lang=2.
* [Ignatius and Senay, 1999] Ignatius Eve, and Senay Hikmet. Data,vocabulary,marks,composition rules and visual perception rules. Created at: 1999. http://cose.math.bas.bg/Sci_Visualization/visConcepts/concepts/marks.htm.
*[Jacko and Sears, 2002] Julie A. Jacko, and Andrew Sears.''The Human-computer Interaction Handbook: Fundamentals, Evolving Technologies.''Lawrence Erlbaum Assoc in, NY, 2002.
* [Munzner, 2000] Tamara Munzner, Interactive Visualization of Large Graphs and Networks. Ph.D. dissertation, Stanford University. June 2000, http://graphics.stanford.edu/papers/munzner_thesis/.
* [Waloszek, 2008] Gerd Waloszek, SAP AG, SAP User Experience,Selecting Objects By Attributes – Part II. Created at: October 3. http://www.sapdesignguild.org/.../attributes_2.asp.
* [Dourish et al,2003] Paul Dourish, Alfred Kobsa, Gloria Mark, David Redmiles. Information Visualization Research. http://www.isr.uci.edu/research-visualization.html, visited June 2003

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 01 - Aufgabe 1 - Visual Encoding

2009-11-03T17:53:07Z

UE-InfoVis0910 0325870:

= Visual Encoding =

==Introduction==

Firstly it's a must to mention that visual representations of information has some unsolved problems as well as advantages. 

"The visual display of information allows people to become more easily aware of essential facts, to quickly see regularities and outliers in data, and therefore to develop a deeper understanding of data."[Dourish et al,2003]

Visual representations are widely used not only in business companies, universities, newspapers, etc.. ,but also in daily life these representations are everywhere around us. In this point it comes into question how the visualizations should be built up, to make the visual representations perceptible. Visual encoding and it's techniques help us with this, they are the guide of converting the data into a visual form. 

== Definition ==

Visual encoding aims to represent data in a form that we all can perceive and it consists of some techniques to convert the data into a perceptible form. Thus enabling us to make visualization on any kind of display. For a visualization we need graphical elements to show the syntax of what we mean. 

"Visual encoding is the set of techniques enabling the representation of data on a screen or on paper. It comprises of the use of shapes, colours, areas and other visual variables that make it possible to associate data variables to graphic variables [Dürsteler, 2005]." 

"Visual encodings are mappings of information to display elements [Munzner, 2000]".

== Techniques of Visual Encoding ==

"The fundamental substrate of visualizations is spatial position. Marks such as points, lines, or area-covering elements can be placed on this substrate. These marks can carry additional information independent of their spatial position, such as size, greyscale luminance (brightness) value, surface texture density, color hue, color saturation, curvature, angle, and shape [Munzner, 2000]".

Six kinds of Visual Encoding:

* Space
* Marks
* Connections & Enclosures
* Retinal Properties
* Temporal Encoding
* Viewpoint Transformation

=== Space ===

When you think about an empty paper as the beginning of a visualisation, the space between some possible object is one of the most powerful aspects to give something an important note. It is the space where data is represented in a visualization.

=== Marks ===

Marks are the objects which are needed to represent the data in a visualization, that the data is into a perceptible form. Thus making the perception of the visual representation easier.

"Marks are the visible things that occur in space [Card et al., 1999]." 

"Marks are simply any graphical symbol visible on a display medium [Ignatius and Senay, 1999]." 

There are four types of simple Marks :
* Points
* Lines
* Areas
* Volumes

=== Connections & enclosures ===

Connections and enclosures build the important dependencies between the objects (marks). Connections show a relationship between objects, while enclosures can also indicate related objects.

=== Retinal properties ===

Simply retinal properties are all those properties of marks which have something to do with human eye perception. Thus describing how objects (marks) look like.

"A retinal encoding is any variation of the marks that the retina of the eye can perceive besides position [Ignatius and Senay, 1999]." 

"Retinal properties are the informations that are carried by marks, such as crispness, shape, resolution, transperancy, color(value,hue & saturation), grayscale [Munzner, 2000]." 

[[Image:retinal.JPG|center|400px|thumb|[Card et al., 1999] Retinal properties]]

=== Temporal encoding ===

A temporal encoding shows how the marks' properties vary over time. 

"It's the encoding which depends on the temporal changes of marks (mark position and their retinal properties) [Ignatius and Senay, 1999]".

=== Viewpoint transformations ===

"Viewpoint transformations modify interactively and augment Visual Structures to turn static presentations into visualizations by establishing graphical parameters to create Views of Visual Structures [Card et al., 1999; Jacko and Sears, 2002]." 

In other words it is the modification of the view position of the viewer (or in general the modification of viewing parameters), thus enhancing the visual perception.

There are three common view transformations :
* Location Probes : Usage of Location in the visual structures to reveal additional info
* Viewpoint control : Moving the viewpoint to create visualisation : Pan,zoom,clip the viewpoint.
* Distortion : Creating a focus area and context area.

[[Image:filmfinder_sm.gif|center|400px|thumb| This is a software which support users to search for films and it includes the techniques above [Waloszek, 2008] FilmFinder]]

==References==

*[Card et al., 1999] Stuart K. Card, Jock D. Mackinlay, and Ben Schneiderman. ''Readings in Information Visualization. Using Vision to Think.''Morgan Kaufman Publ Inc, San Diego, 1990.
* [Dürsteler, 2005] Juan C. Dürsteler, Web Content Mining Visualisation. Created at: November 7. http://www.infovis.net/printMag.php?num=175&lang=2.
* [Ignatius and Senay, 1999] Ignatius Eve, and Senay Hikmet. Data,vocabulary,marks,composition rules and visual perception rules. Created at: 1999. http://cose.math.bas.bg/Sci_Visualization/visConcepts/concepts/marks.htm.
*[Jacko and Sears, 2002] Julie A. Jacko, and Andrew Sears.''The Human-computer Interaction Handbook: Fundamentals, Evolving Technologies.''Lawrence Erlbaum Assoc in, NY, 2002.
* [Munzner, 2000] Tamara Munzner, Interactive Visualization of Large Graphs and Networks. Ph.D. dissertation, Stanford University. June 2000, http://graphics.stanford.edu/papers/munzner_thesis/.
* [Waloszek, 2008] Gerd Waloszek, SAP AG, SAP User Experience,Selecting Objects By Attributes – Part II. Created at: October 3. http://www.sapdesignguild.org/.../attributes_2.asp.
* [Dourish et al,2003] Paul Dourish, Alfred Kobsa, Gloria Mark, David Redmiles. Information Visualization Research. http://www.isr.uci.edu/research-visualization.html, visited June 2003

Teaching:TUW - UE InfoVis WS 2009/10

2009-10-30T07:32:18Z

UE-InfoVis0910 0325870:

[[Image:Aigner03infovis ue.gif]] <big>WS 2009/10</big>

'''LVA Nr:''' 188.308 ([http://tuwis.tuwien.ac.at/lva/tuwien/188308 TUWIS++ Seite])

'''LVA Homepage:''' http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/index.html

'''Leitung:''' [[Gschwandtner, Theresia|Theresia Gschwandtner]] [gschwandtner (at) ifs.tuwien.ac.at] 
'''Tutorin:''' [[User:Katharina-Anna Wendelin|Katharina-Anna Wendelin]] [e0425160 (at) student.tuwien.ac.at]

== Gruppen ==


[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 01|Gruppe 01 (moved, moved, <frei>)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 02|Gruppe 02 (Feichtinger, Rezaei, Schindelka)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 03|Gruppe 03 (Lang, Hackl, Hasslacher)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04|Gruppe 04 (Kaiser, Ehsani, Sverak)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 05|Gruppe 05 (Paizoni, Wuttej, Hudl)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 06|Gruppe 06 (Fried, Fritz, Hiller)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 07|Gruppe 07 (moved, <???>, <???>)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 08|Gruppe 08 (Bauer, Olea, Schwengerer)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09|Gruppe 09 (Hubmann-Haidvogel, Kloibhofer, Riederer)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 10|Gruppe 10 (Kickinger, Ramsauer, Wittmann)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 11|Gruppe 11 (Gastecker, Hahn, Leeb)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 12|Gruppe 12 (Bauer, Eigner, Sakai)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 13|Gruppe 13 (Sadauskas, Scheikl, Burger)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15|Gruppe 15 (Martin, Stix, Lenzhofer)]]

== News / Bemerkungen ==

Liebe TeilnehmerInnen! 
Die Links zu Aufgabe 1 (Auszubessernde InfoVis Begriffe) sind nun auf eurer Gruppenseite unter "Aufgabe 1" zu finden.
Achtung: die Aufgabenstellung hat sich im Vergleich zum Vorjahr geändert! (siehe http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe1.html) -- [[User:Katharina-Anna Wendelin|Katharina-Anna Wendelin]] 21:18, 29 October 2009 (CEST)

Liebe TeilnehmerInnen, 
fügt bitte Gruppen mit nur einem oder zwei Mitgliedern selbstständig zusammen,
z.B., Martin Schwengerer geht zur Gruppe 08 und Gruppe 12 verteilt sich auf Gruppe 01 und Gruppe 04. 
Wie ihr euch umverteilt überlasse ich euch. Sollte das nicht funktionieren, bitte ich um
Rückmeldung bis morgen, dann muss ich "nachhelfen" ;). 
Vielen Dank! 
-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 07:27, 29 October 2009 (CEST)

Liebe TeilnehmerInnen, 
aus Datenschutz-Gründen bitte ich euch eure Martrikelnummer
nicht auf eurer InfoVis:Wiki Userseite anzugeben. 
Vielen Dank! 
-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 09:40, 27 October 2009 (CEST)

Liebe TeilnehmerInnen! 
Um diese Seite einheitlich zu gestalten (auch bezüglich der Vorjahre), schlage ich vor die Nachnamen
der Gruppenmitglieder in Klammer neben der Gruppe anzugeben, 
z.B.: Gruppe XX (Maier, Müller, Mustermann). 
-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 10:05, 01 October 2009 (CEST)

Teaching:TUW - UE InfoVis WS 2009/10

2009-10-30T07:31:56Z

UE-InfoVis0910 0325870:

[[Image:Aigner03infovis ue.gif]] <big>WS 2009/10</big>

'''LVA Nr:''' 188.308 ([http://tuwis.tuwien.ac.at/lva/tuwien/188308 TUWIS++ Seite])

'''LVA Homepage:''' http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/index.html

'''Leitung:''' [[Gschwandtner, Theresia|Theresia Gschwandtner]] [gschwandtner (at) ifs.tuwien.ac.at] 
'''Tutorin:''' [[User:Katharina-Anna Wendelin|Katharina-Anna Wendelin]] [e0425160 (at) student.tuwien.ac.at]

== Gruppen ==


[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 01|Gruppe 01 (Sverak, moved, <frei>)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 02|Gruppe 02 (Feichtinger, Rezaei, Schindelka)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 03|Gruppe 03 (Lang, Hackl, Hasslacher)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04|Gruppe 04 (Kaiser, Ehsani, Sverak)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 05|Gruppe 05 (Paizoni, Wuttej, Hudl)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 06|Gruppe 06 (Fried, Fritz, Hiller)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 07|Gruppe 07 (moved, <???>, <???>)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 08|Gruppe 08 (Bauer, Olea, Schwengerer)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 09|Gruppe 09 (Hubmann-Haidvogel, Kloibhofer, Riederer)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 10|Gruppe 10 (Kickinger, Ramsauer, Wittmann)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 11|Gruppe 11 (Gastecker, Hahn, Leeb)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 12|Gruppe 12 (Bauer, Eigner, Sakai)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 13|Gruppe 13 (Sadauskas, Scheikl, Burger)]]

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 15|Gruppe 15 (Martin, Stix, Lenzhofer)]]

== News / Bemerkungen ==

Liebe TeilnehmerInnen! 
Die Links zu Aufgabe 1 (Auszubessernde InfoVis Begriffe) sind nun auf eurer Gruppenseite unter "Aufgabe 1" zu finden.
Achtung: die Aufgabenstellung hat sich im Vergleich zum Vorjahr geändert! (siehe http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws09/infovis_ue_aufgabe1.html) -- [[User:Katharina-Anna Wendelin|Katharina-Anna Wendelin]] 21:18, 29 October 2009 (CEST)

Liebe TeilnehmerInnen, 
fügt bitte Gruppen mit nur einem oder zwei Mitgliedern selbstständig zusammen,
z.B., Martin Schwengerer geht zur Gruppe 08 und Gruppe 12 verteilt sich auf Gruppe 01 und Gruppe 04. 
Wie ihr euch umverteilt überlasse ich euch. Sollte das nicht funktionieren, bitte ich um
Rückmeldung bis morgen, dann muss ich "nachhelfen" ;). 
Vielen Dank! 
-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 07:27, 29 October 2009 (CEST)

Liebe TeilnehmerInnen, 
aus Datenschutz-Gründen bitte ich euch eure Martrikelnummer
nicht auf eurer InfoVis:Wiki Userseite anzugeben. 
Vielen Dank! 
-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 09:40, 27 October 2009 (CEST)

Liebe TeilnehmerInnen! 
Um diese Seite einheitlich zu gestalten (auch bezüglich der Vorjahre), schlage ich vor die Nachnamen
der Gruppenmitglieder in Klammer neben der Gruppe anzugeben, 
z.B.: Gruppe XX (Maier, Müller, Mustermann). 
-- [[Gschwandtner, Theresia|Theresia Gschwandtner]] 10:05, 01 October 2009 (CEST)

Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04

2009-10-30T07:31:12Z

UE-InfoVis0910 0325870:

'''Gruppenmitglieder'''

[[User:UE-InfoVis0910_0401099|Kaiser, Stefanie]] 
[[User:UE-InfoVis0910_0405144|Ehsani, Anna Parisa]] 
[[User:UE-InfoVis0910_0325870|Sverak, Sascha]] 

'''Aufgaben'''

[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04 - Aufgabe 0|Aufgabe 0]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04 - Aufgabe 1|Aufgabe 1]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04 - Aufgabe 2|Aufgabe 2]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04 - Aufgabe 3|Aufgabe 3]] 
[[Teaching:TUW - UE InfoVis WS 2009/10 - Gruppe 04 - Aufgabe 4|Aufgabe 4]]

User:UE-InfoVis0910 0325870

2009-10-27T16:54:14Z

UE-InfoVis0910 0325870:

* '''Name:''' Sascha Sverak

[[Image:Wien.JPG]]

User:UE-InfoVis0910 0325870

2009-10-25T13:32:26Z

UE-InfoVis0910 0325870:

* '''Name:''' Sascha Sverak
* '''Matrikelnr.:''' 0325870
* '''Kennzahl:''' 534

[[Image:Wien.JPG]]

User:UE-InfoVis0910 0325870

2009-10-25T13:19:44Z

UE-InfoVis0910 0325870:

* '''Name:''' Sascha Sverak
* '''Matrikelnr.:''' 0325870
* '''Kennzahl:''' 534

[[Image:Wien.jpg]]

File:Wien.JPG

2009-10-25T13:15:41Z

UE-InfoVis0910 0325870: Längenfeldgasse bei Sonnenaufgang.

== Summary ==
Längenfeldgasse bei Sonnenaufgang.
== Copyright status ==

== Source ==

User:UE-InfoVis0910 0325870

2009-10-25T13:02:14Z

UE-InfoVis0910 0325870: New page: * '''Name:''' Sascha Sverak * '''Matrikelnr.:''' 0325870 * '''Kennzahl:''' 534

* '''Name:''' Sascha Sverak
* '''Matrikelnr.:''' 0325870
* '''Kennzahl:''' 534