InfoVis:Wiki - User contributions [en]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 4

2009-01-07T19:25:46Z

UE-InfoVis0809 0126665:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe4.html Beschreibung der Aufgabe 4]

=== Gegebene Daten ===

'''Homer Simpson's Trinkverhalten in Abhängigkeit von seinen Lebensumständen''' 

...Visualisierung von Homer's Lebensabschnitten bzw. Ereignissen mit Einfluss auf sein
Trinkverhalten (zB.: Kindheit, Pubertät, Arbeitslosigkeit, Beziehungen, Hochzeit, Geburt
der Kinder, Liebeskummer, Alltag, etc.) von seiner Geburt bis Jetzt + mögliche
Zukunftsszenarien (mind. 3).
 
*Die Menge folgender Getränke soll für die jeweiligen Lebensumstände ablesbar sein
(ml oder Liter - je nachdem - pro Tag, Monat, Jahr (z.B.: Fokus+Kontext Methoden):
a) Wasser 
b) Milch 
c) Fruchtsaft 
d) Cola 
e) Kaffee (Würfelzucker?) 
f) Bier
(vereinfacht angenommen, Homer trinkt ausschließlich diese Getränke)
 
*Die folgenden Werte sollen abhängig von den konsumierten Getränken ablesbar sein:
1) g oder kg konsumierter Zucker (aus Getränken) + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 50g; enthaltener Zucker: 10g/100 ml Cola; 10g/100 ml Fruchtsaft; 3g/Würfelzucker). 
2) mg konsumiertes Coffein + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 600mg; enthaltenes Coffein: 10 mg/100 ml Cola; 80 mg/100 ml Kaffee). 
3) g konsumierter Alkohol + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 20g; enthaltener Alkohol: 3,6 g/100ml Bier)
 
*Die Daten sollen zur medizinischen/psychologischen Analyse visualisiert werden. 
*Die bisher erlernten Design-Prinzipien sollen umgesetzt werden (z.B.: Optimierung der Data-ink ratio). 
*Die Mockups sollten zumindest 1) Homer's Leben im Überblick 2) und eine Detailansicht wiedergeben. 
*Alle nicht angeführten Daten können frei erfunden werden.

===Description===
*Application area analysis & dataset analysis
The predefined dataset describes Homer Simpson's lifetime beverage consumption. 
We distinguish between beverages (water, milk, juice, cola, café, and beer), contained substances (sugar, alcohol, caffeine), and their according recommended daily allowance (RDA). As actual data, we store ingested liters of fluids and the time of consumption (=life-period like child ship, puberty, unemployment, marriage, birth of children, retirement, divorce, illness,..). The exact amounts of substances contained in drinks are stored separately and are not subject of primary interest. A special issue is the unknown number of sugar cubes per café. 
For measurement of consumed drinks, we use the ordinal data type Liter and for substances kilogram or gram, the life period is considered as nominal type and cannot directly be linked to a specific age or time span. This results in a dataset structure of two dimensions.
 
[[Image:Homer.gif]]

*Target group analysis
The visualization is tended for medical and psychological stakeholders. The medical view expresses the consumption of substances like sugar, alcohol and caffeine whereas the psychological view details the intake of fluids. With Homer Simpson’s dataset it is also possible to demonstrate the overall intake of amounts of sugar and similar harmful substances to children. Nevertheless the interface for the visualization should be easily comprehended and user friendly to be able to be operated by different target groups.

*Goal
The goal of the visualization is to get a quick and detailed overview on Homer Simpson’s drinking behavior depending his current life period and circumstances. It should be possible to identify the accumulated quantities of substances in relation to the recommended maximum dose. The interactive visualization should also offer an option to select considered fluids.
===Concept ===

*Visualization
We should use graphs, when the message is contained in the shape of the values and the document you produce will be used to reveal relationship among multiple values [Few, 2004]. In our task about Homer Simpson's drink consumption are too much different values that have to be shown, that a table all alone won't show the information clearly enough.

* Visual Mapping
We separate the two different views for medical and psychologic interests: 
On the psychologic or "drink"-view of our data, the drinks to be shown are selected:
The different life periods are mapped onto the x-axis and the consumed amount (in liters) onto the y-axis. 
On the medical or "substance"-view, the sugar-, coffein- and alcohol-rates can be examined:
Again, the various life periods are shown on the x-axis. For each period, the amounts of consumed substances sugar, caffeine and alcohol are mapped onto the y-axis in multiples of the according RDA. We put a horizontal line where y equals one RDA. This That way, it is easy recognizable which substance consumption exceeds one RDA.

*Description of the technologies
We use a bar diagram to show our nominal dataset, because a simple comparison of the categorical subdivision of one or more measures in no particular order can be shown very easy by this visualization[Few, 2004]. 
On the "substance"-view, we decided to show (for the medicine view) the differences to the maximal dosis by mapping the substances on an RDA scale, where the maximum dose is clearly shown by the horizontal line [Few, 2004]. The bars then show whether Homer Simpson's drinking behavior was adequate for each period, and if not, by how much one RDA was exceeded.

*Interaction
The user can use between the two views by clicking on the arrows between the two selection windows, where the different drinks and the different substances can be selected. He can select one or more drinks/substances, which will be shown on the diagram. 
In the "drinks"-view by selecting a bar he can see a label, where the content of substances will be shown and their recommended maximum dose. 
So in the substances-view the liters of the drink and the g, mg and ml are popped-up in a label, when the bar is selected. 
The selection of the different views is made for the different viewers and interests.

*MockUp

Figure1=drinks-view with selected bar
[[Image:Aufgabe4_G.jpg|400px|Redesigned diargram]]

Figure 1 shows the different drinks in the different life periodes, so it is made for the physiological interest on the relation between the drinking habits and the time of his life.

Figure2=substance-view with selected bar

[[Image:Aufgabe4_S.jpg|400px|Redesigned diargram]]
Figure 2 is made for the medicinical interest on the deviation to the recommended maximum dose.

Additionally we think about an drag-and-drop- and copy-option for the different-life-periodes, because maybe Homer has more than one time lovesickness.
Also more than the given drinks could be added.

===References===
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Appendix A - Table and Graph Design at a Glance.

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2008/09|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 4

2009-01-05T16:32:12Z

UE-InfoVis0809 0126665:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe4.html Beschreibung der Aufgabe 4]

=== Gegebene Daten ===

'''Homer Simpson's Trinkverhalten in Abhängigkeit von seinen Lebensumständen''' 

...Visualisierung von Homer's Lebensabschnitten bzw. Ereignissen mit Einfluss auf sein
Trinkverhalten (zB.: Kindheit, Pubertät, Arbeitslosigkeit, Beziehungen, Hochzeit, Geburt
der Kinder, Liebeskummer, Alltag, etc.) von seiner Geburt bis Jetzt + mögliche
Zukunftsszenarien (mind. 3).
 
*Die Menge folgender Getränke soll für die jeweiligen Lebensumstände ablesbar sein
(ml oder Liter - je nachdem - pro Tag, Monat, Jahr (z.B.: Fokus+Kontext Methoden):
a) Wasser 
b) Milch 
c) Fruchtsaft 
d) Cola 
e) Kaffee (Würfelzucker?) 
f) Bier
(vereinfacht angenommen, Homer trinkt ausschließlich diese Getränke)
 
*Die folgenden Werte sollen abhängig von den konsumierten Getränken ablesbar sein:
1) g oder kg konsumierter Zucker (aus Getränken) + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 50g; enthaltener Zucker: 10g/100 ml Cola; 10g/100 ml Fruchtsaft; 3g/Würfelzucker). 
2) mg konsumiertes Coffein + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 600mg; enthaltenes Coffein: 10 mg/100 ml Cola; 80 mg/100 ml Kaffee). 
3) g konsumierter Alkohol + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 20g; enthaltener Alkohol: 3,6 g/100ml Bier)
 
*Die Daten sollen zur medizinischen/psychologischen Analyse visualisiert werden. 
*Die bisher erlernten Design-Prinzipien sollen umgesetzt werden (z.B.: Optimierung der Data-ink ratio). 
*Die Mockups sollten zumindest 1) Homer's Leben im Überblick 2) und eine Detailansicht wiedergeben. 
*Alle nicht angeführten Daten können frei erfunden werden.

===Description===
*Application area analysis & dataset analysis
The predefined dataset describes the fluids consume of Homer Simpson in his life. 
We distinguish between fluids (water, milk, juice, cola, café, and beer), contained substances (sugar, alcohol, caffeine), and their recommended maximum dose. As actual data we store ingested liters of fluids and the time of consumption (=life-period like child ship, puberty, unemployment, marriage, birth of children, retirement, divorce, illness,..). The exact amounts of substances in liquids are stored separately and are not subject of primary interest. A special issue is the unknown number of sugar cubes per café. 
For measurement of fluids we use the ordinal data type Liter and for substances Kilogram or Gram, the life period is considered as nominal type and cannot directly linked to a specific age. This results in a dataset structure of 2 dimensions.
 
[[Image:Homer.gif]]

*Target group analysis

*Goal

===Concept ===

*Visualization
We should use graphs, when the message is contained in the shape of the values and the document you produce will be used to reveal relationship among multiple values[Few, 2004]. In our task about Homer Simpson's Drinking are too much different values that have to be shown, that a table all alone wouldn't show the information clearly enough.

* Visual Mapping
We separate the two different views for medical and psychologic interests: 
On the psychologic or "drink"-view of our data, the drinks to be shown are selected:
The different life-periods are mapped onto the x-axis and the liters onto the y-axis. 
On the medical or "substance"-view, the sugar-, coffein- and alcohol-rates can be seen:
The different life-periodes are shown on the x-axis.
The percent, in relation to the recommended maximum dose (=100%) of different substances in the drinks per day, is mapped on the y-axis.

*Description of the technologies
We use a bar diagram to show our nominal dataset, because a simple comparison of the categorical subdivision of one or more measures in no particular order can be shown very easy by this visualization[Few, 2004]. 
On the "substance"-view, we decided to show (for the medicine view) the differences to the maximal dosis by mapping the substances on a percent-scale, where the maximal dosis is shown by a line[Few, 2004]. The bars show the plus or minus of the substances in Homer Simpson's drinking to these lines.

*Interaction
The user can use between the two views by clicking on the arrows between the two selection windows, where the different drinks and the different substances can be selected. He can select one or more drinks/substances, which will be shown on the diagram. 
In the "drinks"-view by selecting a bar he can see a label, where the content of substances will be shown and their recommended maximum dose. 
So in the substances-view the liters of the drink and the g, mg and ml are popped-up in a label, when the bar is selected. 
The selection of the different views is made for the different viewers and interests.

*MockUp

Figure1=drinks-view with selected bar

Figure 1 shows the different drinks in the different life periodes, so it is made for the physiological interest on the relation between the drinking habits and the time of his life.

Figure2=substance-view with selected bar

Figure 2 is made for the medicinical interest on the deviation to the recommended maximum dose.

Additionally we think about an drag-and-drop- and copy-option for the different-life-periodes, because maybe Homer has more than one time lovesickness.
Also more than the given drinks could be added.

===References===
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Appendix A - Table and Graph Design at a Glance.

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2008/09|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 4

2009-01-05T16:31:28Z

UE-InfoVis0809 0126665:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe4.html Beschreibung der Aufgabe 4]

=== Gegebene Daten ===

'''Homer Simpson's Trinkverhalten in Abhängigkeit von seinen Lebensumständen''' 

...Visualisierung von Homer's Lebensabschnitten bzw. Ereignissen mit Einfluss auf sein
Trinkverhalten (zB.: Kindheit, Pubertät, Arbeitslosigkeit, Beziehungen, Hochzeit, Geburt
der Kinder, Liebeskummer, Alltag, etc.) von seiner Geburt bis Jetzt + mögliche
Zukunftsszenarien (mind. 3).
 
*Die Menge folgender Getränke soll für die jeweiligen Lebensumstände ablesbar sein
(ml oder Liter - je nachdem - pro Tag, Monat, Jahr (z.B.: Fokus+Kontext Methoden):
a) Wasser 
b) Milch 
c) Fruchtsaft 
d) Cola 
e) Kaffee (Würfelzucker?) 
f) Bier
(vereinfacht angenommen, Homer trinkt ausschließlich diese Getränke)
 
*Die folgenden Werte sollen abhängig von den konsumierten Getränken ablesbar sein:
1) g oder kg konsumierter Zucker (aus Getränken) + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 50g; enthaltener Zucker: 10g/100 ml Cola; 10g/100 ml Fruchtsaft; 3g/Würfelzucker). 
2) mg konsumiertes Coffein + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 600mg; enthaltenes Coffein: 10 mg/100 ml Cola; 80 mg/100 ml Kaffee). 
3) g konsumierter Alkohol + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 20g; enthaltener Alkohol: 3,6 g/100ml Bier)
 
*Die Daten sollen zur medizinischen/psychologischen Analyse visualisiert werden. 
*Die bisher erlernten Design-Prinzipien sollen umgesetzt werden (z.B.: Optimierung der Data-ink ratio). 
*Die Mockups sollten zumindest 1) Homer's Leben im Überblick 2) und eine Detailansicht wiedergeben. 
*Alle nicht angeführten Daten können frei erfunden werden.

===Description===
*Application area analysis & dataset analysis
The predefined dataset describes the fluids consume of Homer Simpson in his life. 
We distinguish between fluids (water, milk, juice, cola, café, and beer), contained substances (sugar, alcohol, caffeine), and their recommended maximum dose. As actual data we store ingested liters of fluids and the time of consumption (=life-period like child ship, puberty, unemployment, marriage, birth of children, retirement, divorce, illness,..). The exact amounts of substances in liquids are stored separately and are not subject of primary interest. A special issue is the unknown number of sugar cubes per café. 
For measurement of fluids we use the ordinal data type Liter and for substances Kilogram or Gram, the life period is considered as nominal type and cannot directly linked to a specific age. This results in a dataset structure of 2 dimensions.
[[Image:Homer.gif]]

*Target group analysis

*Goal

===Concept ===

*Visualization
We should use graphs, when the message is contained in the shape of the values and the document you produce will be used to reveal relationship among multiple values[Few, 2004]. In our task about Homer Simpson's Drinking are too much different values that have to be shown, that a table all alone wouldn't show the information clearly enough.

* Visual Mapping
We separate the two different views for medical and psychologic interests: 
On the psychologic or "drink"-view of our data, the drinks to be shown are selected:
The different life-periods are mapped onto the x-axis and the liters onto the y-axis. 
On the medical or "substance"-view, the sugar-, coffein- and alcohol-rates can be seen:
The different life-periodes are shown on the x-axis.
The percent, in relation to the recommended maximum dose (=100%) of different substances in the drinks per day, is mapped on the y-axis.

*Description of the technologies
We use a bar diagram to show our nominal dataset, because a simple comparison of the categorical subdivision of one or more measures in no particular order can be shown very easy by this visualization[Few, 2004]. 
On the "substance"-view, we decided to show (for the medicine view) the differences to the maximal dosis by mapping the substances on a percent-scale, where the maximal dosis is shown by a line[Few, 2004]. The bars show the plus or minus of the substances in Homer Simpson's drinking to these lines.

*Interaction
The user can use between the two views by clicking on the arrows between the two selection windows, where the different drinks and the different substances can be selected. He can select one or more drinks/substances, which will be shown on the diagram. 
In the "drinks"-view by selecting a bar he can see a label, where the content of substances will be shown and their recommended maximum dose. 
So in the substances-view the liters of the drink and the g, mg and ml are popped-up in a label, when the bar is selected. 
The selection of the different views is made for the different viewers and interests.

*MockUp

Figure1=drinks-view with selected bar

Figure 1 shows the different drinks in the different life periodes, so it is made for the physiological interest on the relation between the drinking habits and the time of his life.

Figure2=substance-view with selected bar

Figure 2 is made for the medicinical interest on the deviation to the recommended maximum dose.

Additionally we think about an drag-and-drop- and copy-option for the different-life-periodes, because maybe Homer has more than one time lovesickness.
Also more than the given drinks could be added.

===References===
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Appendix A - Table and Graph Design at a Glance.

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2008/09|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

File:Homer.gif

2009-01-05T16:30:30Z

UE-InfoVis0809 0126665:

== Summary ==

== Copyright status ==

== Source ==
[Beranek, 2008]

File:Homer.gif

2009-01-05T16:29:32Z

UE-InfoVis0809 0126665: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 4

2009-01-05T16:08:19Z

UE-InfoVis0809 0126665:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe4.html Beschreibung der Aufgabe 4]

=== Gegebene Daten ===

'''Homer Simpson's Trinkverhalten in Abhängigkeit von seinen Lebensumständen''' 

...Visualisierung von Homer's Lebensabschnitten bzw. Ereignissen mit Einfluss auf sein
Trinkverhalten (zB.: Kindheit, Pubertät, Arbeitslosigkeit, Beziehungen, Hochzeit, Geburt
der Kinder, Liebeskummer, Alltag, etc.) von seiner Geburt bis Jetzt + mögliche
Zukunftsszenarien (mind. 3).
 
*Die Menge folgender Getränke soll für die jeweiligen Lebensumstände ablesbar sein
(ml oder Liter - je nachdem - pro Tag, Monat, Jahr (z.B.: Fokus+Kontext Methoden):
a) Wasser 
b) Milch 
c) Fruchtsaft 
d) Cola 
e) Kaffee (Würfelzucker?) 
f) Bier
(vereinfacht angenommen, Homer trinkt ausschließlich diese Getränke)
 
*Die folgenden Werte sollen abhängig von den konsumierten Getränken ablesbar sein:
1) g oder kg konsumierter Zucker (aus Getränken) + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 50g; enthaltener Zucker: 10g/100 ml Cola; 10g/100 ml Fruchtsaft; 3g/Würfelzucker). 
2) mg konsumiertes Coffein + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 600mg; enthaltenes Coffein: 10 mg/100 ml Cola; 80 mg/100 ml Kaffee). 
3) g konsumierter Alkohol + empfohlene Maximaldosis pro Tag, Monat, Jahr
(empfohlene Maximaldosis/Tag: 20g; enthaltener Alkohol: 3,6 g/100ml Bier)
 
*Die Daten sollen zur medizinischen/psychologischen Analyse visualisiert werden. 
*Die bisher erlernten Design-Prinzipien sollen umgesetzt werden (z.B.: Optimierung der Data-ink ratio). 
*Die Mockups sollten zumindest 1) Homer's Leben im Überblick 2) und eine Detailansicht wiedergeben. 
*Alle nicht angeführten Daten können frei erfunden werden.

===Description===
*Application area analysis & dataset analysis
The predefined dataset describes the fluids consume of Homer Simpson in his life. 
We distinguish between fluids (water, milk, juice, cola, café, and beer), contained substances (sugar, alcohol, caffeine), and their recommended maximum dose. As actual data we store ingested liters of fluids and the time of consumption (=life-period like child ship, puberty, unemployment, marriage, birth of children, retirement, divorce, illness,..). The exact amounts of substances in liquids are stored separately and are not subject of primary interest. A special issue is the unknown number of sugar cubes per café. 
For measurement of fluids we use the ordinal data type Liter and for substances Kilogram or Gram, the life period is considered as nominal type and cannot directly linked to a specific age. This results in a dataset structure of 2 dimensions.

*Target group analysis

*Goal

===Concept ===

*Visualization
We should use graphs, when the message is contained in the shape of the values and the document you produce will be used to reveal relationship among multiple values[Few, 2004]. In our task about Homer Simpson's Drinking are too much different values that have to be shown, that a table all alone wouldn't show the information clearly enough.

* Visual Mapping
We separate the two different views for medical and psychologic interests: 
On the psychologic or "drink"-view of our data, the drinks to be shown are selected:
The different life-periods are mapped onto the x-axis and the liters onto the y-axis. 
On the medical or "substance"-view, the sugar-, coffein- and alcohol-rates can be seen:
The different life-periodes are shown on the x-axis.
The percent, in relation to the recommended maximum dose (=100%) of different substances in the drinks per day, is mapped on the y-axis.

*Description of the technologies
We use a bar diagram to show our nominal dataset, because a simple comparison of the categorical subdivision of one or more measures in no particular order can be shown very easy by this visualization[Few, 2004]. 
On the "substance"-view, we decided to show (for the medicine view) the differences to the maximal dosis by mapping the substances on a percent-scale, where the maximal dosis is shown by a line[Few, 2004]. The bars show the plus or minus of the substances in Homer Simpson's drinking to these lines.

*Interaction
The user can use between the two views by clicking on the arrows between the two selection windows, where the different drinks and the different substances can be selected. He can select one or more drinks/substances, which will be shown on the diagram. 
In the "drinks"-view by selecting a bar he can see a label, where the content of substances will be shown and their recommended maximum dose. 
So in the substances-view the liters of the drink and the g, mg and ml are popped-up in a label, when the bar is selected. 
The selection of the different views is made for the different viewers and interests.

*MockUp

Figure1=drinks-view with selected bar

Figure 1 shows the different drinks in the different life periodes, so it is made for the physiological interest on the relation between the drinking habits and the time of his life.

Figure2=substance-view with selected bar

Figure 2 is made for the medicinical interest on the deviation to the recommended maximum dose.

Additionally we think about an drag-and-drop- and copy-option for the different-life-periodes, because maybe Homer has more than one time lovesickness.
Also more than the given drinks could be added.

===References===
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Appendix A - Table and Graph Design at a Glance.

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2008/09|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

File:Teaching DiagramVariante2d.jpg

2008-12-09T16:12:58Z

UE-InfoVis0809 0126665:

== Summary ==

== Copyright status ==

== Source ==
[Goldmann, 2008]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 3

2008-12-09T16:01:02Z

UE-InfoVis0809 0126665:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe3.html Beschreibung der Aufgabe 3]

=== Zu beurteilende Grafik ===
[[Image:GeldfuerdenStaat.gif]] 
Beitrag der Steuerpflichtigen zum Einkommensteueraufkommen

===Critics on the table===
*little data-ink ratio
There is too much unnecessary ink used for the graphic, because the important text and values need less place against the borders around them, but not enough to stand out clearly.
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 (i.e., the closer its value is to one), the better
you’ve highlighted the data [Few 2004b].

*visual clutter
**The misalignment of the spending graphic makes the scanning down and across difficult, because we are more sensitve to the vertical and horizontal alignment than we might imagine [Few, 2004a]. If we want to stand out some data, it would be helpful, but in this scenario it isn't.
**The objects positioned on the right are also less seen than the objects positioned on the top, left or in the center and this graphic spent to the right. So the region which could include the easy seen data is unnecessarily white[Few 2004a].
**The unstructured ordering of the pie charts makes it difficult to separatly scan the major content. Firstly there are too much little pie charts which stand nearly in front of each other and enclosure data "too much".
**Secondly the alignmet of the pie charts is different from line to line. But when using indentation, it should be far enough to make the intention clear[Few, 2004a].
**Darker and brighter color makes the contrasting information stand out from the norm[Few, 2004a]. So the red data and pie charts stand out in the graphic, but they aren't as important as they look. The last "100%- and lowest-income-row" doesn't underline the title of the graphic "The richest pay the most". This information would show the first row, but this one isn't red-coloured.
So the most important data isn't standing out in this graphic.

*Organisation of the data

The way the diagram presents the data doesn't fit the message in the header at all. It's not crucial to know that 50% of taxpayers whose gross income is more than 26600€ are paying 93.7% of the collective income tax. To deliver the message correctly it's better to segment the data into meaningful subsets [Few 2004b]. Thus you group the gross income data into groups with upper and lower limits and recalculate the collective tax proportion for these boundaries. Accordingly you have separated the different income groups and can point out the specific tax proportion. Now it's clearly visible that the biggest fraction of taxpayers whose income is between 8,100€ and 29,450€ (55%) are in fact paying only 9 percentage and the smallest and richest fraction are nearly paying half of the overall income tax (45.7%).

===Correction of the table ===

[[Image:Teaching_DiagramVariante2d.jpg|400px|Redesigned diargram]]

===Which changes have been made and why is the changed one better than the original table?===

*Data Type Analysis
There are three dimensions: income, percentage in population and percentage in tax revenue of which the latter two share the same unit. The dimension of income class is discrete with a range of six possible values, but with a rather continuous underlying domain (Euro). Further, the highest income class is special in that it lacks an upper bound.

*Diagram redesign

First, we thought of the simplest variant, a plot of income classes on the x-axis against percentage on the y-axis with linearly interconnected data points. In order to keep income classes to scale and thus minimizing the lie factor, the x-axis’s unit was set to Euro so classes were represented by the mean of their bounds. The result was two intersecting curves, each made up of six points with non-equidistant x-components due to non-uniform class width. The center of the upwardly boundless income class was chosen arbitrarily (about the mean class width). The disadvantage of this solution is having two curves in the same display range.

The second approach was a custom diagram, quite similar to the well known population pyramid: one axis (or column) of classes with laterally protruding percentage indicators. As in the first approach, the class width (height in this case) was chosen to scale of according Euro bounds, avoiding lie effects. The problem with adjacent bars as indicators is that without filling, their contours have a distracting effect. Also, the observer then might misinterpret the bars’ surface area as a representation of data (fine idea, but impossible with the constraint of having both axis to scale). Therefore, we chose pins, again with origins centered on class means. The advantage of this diagram is having separated two variables with the same unit plotted against the dependent variable (class). The effect, especially with a dataset like this, is like the one of an unbalanced balance and immediately reveals the situation.

First, income classes were only indicated on a vertical line with horizontal dashes. Although this amplified the described balance effect, there was no space for class labels. One would have had to add a scale and labels at the left and right border of the diagram, unnecessarily doubling ink. So we decided to adhere to the population pyramid and place the class labeling in the center.

===References===
*[Few, 2004a]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Chapter 7 - General Design for Communication.

*[Few, 2004b]: Stephen Few, Elegance Through Simplicity, intelligent enterprise, Oct 16, 2004.
[http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920 http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=N2ATDQWY5VYKSQSNDBGCKHSCJUMEKJVN?articleID=49400920]

*

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2008/09|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/ UE InfoVis]

* [[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 3

2008-12-05T15:56:46Z

UE-InfoVis0809 0126665:

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 2

2008-11-17T13:07:18Z

UE-InfoVis0809 0126665:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe2.html Beschreibung der Aufgabe 2]

===Table to evaluate===

[[Image:Trace_stats04.gif]]

===Critics on the table===
* distracting grid
:The grid makes the table difficult to read, because it distracts the eyes. Less rulers and grid would increase the table's effectiveness [Few, 2004]. More white space around the data and the table would be clearer.
[[Image:PartsOfTable.jpg|right|200px|thumb|Figure1: parts of a table [Few, 2004] ]]
* missing labels = missing information
:The table's information isn't clearly defined, because of the missing Title/Header.
:In addition there should be a title AND subheader because of the multiple variables. Accordingly the column should be titled with the name followed by the units of measurement [Wallace, 2004].
:Figure1 shows the parts of a table and their naming.

* bad ordering of the data
**column Sequence
*:The categorical subdivision should be arranged down the rows of a single column to the left of the quantitative values associated with them [Few, 2004]. Therefore the information of the table would be shown better by placing the data of the columns in the rows and the data of the rows in the columns.
**data sequence
*:The data in the first row isn't ordered in a logical way, so I would prefer to place the products in an ascending or descending order. It isn't advisable to use a conventional order based on alphabetical sequence, which is useful for look-up purposes but isn't meaningful otherwise [Few, 2004].

* centered alignment of the values and the text
:-make it difficult to read the table.
:Text and numbers that represent quantitative values should always be aligned to the right [Few, 2004]. All numbers in the columns should line up with each other and their headings [Wallace, 2004], each using same number of decimal digits.

* numbers precision
:Some numbers in the table should and could be reduced without the loss of meaningful information to line up with each other in the column.
:To improve readability of the table, high numbers can be truncated to sets of three digits and be declared in the header [Few, 2004] to avoid confusion and misinterpretation as seen in the 'total bytes transferred'-row.

===Correction of the table ===

[[Image:Corrected_table.jpg]]

===Which changes have been made and why is the changed one better than the original table?===
*The table gets a title, a subtitle, a column-title for the first column also and the header-names have been slightly changed, because of the multiple variables, which improves the understanding of the data in the table.
*Only one rule is used to separate the headers of the columns and rows and push the data in the columns together, but there's enough white space left between the data in the rows and the line-headers to see a clearly separation.
*The quantitative values of the categorical subdivision: "WWW", "Gator", "Cydoor", "SaveNow" and "eZula" have been sequenced from left to right, so the data of the rows have been moved to the columns and vice versa, because the comparison of numbers is easier when they are stacked.
*The place of the "SaveNow"-data has been changed with the place of the "eZula"-data to get an hierachical order in the first column of the quantitative values, which looks more logical.
*The numbers and their column-headers have been aligned to the left and the text of the first column to the right to show the parts of the table clearly; like in Figure1.
*The "number of clients"- and "contacted servers"-values has been reduced to an integer. The "transferred bytes" has been truncated to GB to line up with each other in the column and to make the comparison of the columns easier than the centered, different numbers and units of measurement in the table before. So I declare this units in the column-header.

===References===
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Chapter 8 - Table Design.

*[Wallace, 2004]: Rosa Wallace, Designing Tables, NC State University LabWrite Resources, 2004.
[http://www.ncsu.edu/labwrite/res/gh/gh-tables.html http://www.ncsu.edu/labwrite/res/gh/gh-tables.html]

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2008/09|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 2

2008-11-17T13:06:34Z

UE-InfoVis0809 0126665:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe2.html Beschreibung der Aufgabe 2]

===Table to evaluate===

[[Image:Trace_stats04.gif]]

===Critics on the table===
* distracting grid
:The grid makes the table difficult to read, because it distracts the eyes. Less rulers and grid would increase the table's effectiveness [Few, 2004]. More white space around the data and the table would be clearer.
[[Image:PartsOfTable.jpg|right|200px|thumb|Figure1: parts of a table [Few, 2004] ]]
* missing labels = missing information
:The table's information isn't clearly defined, because of the missing Title/Header.
:In addition there should be a title AND subheader because of the multiple variables. Accordingly the column should be titled with the name followed by the units of measurement [Wallace, 2004].
:Figure1 shows the parts of a table and their naming.

* bad ordering of the data
**column Sequence
*:The categorical subdivision should be arranged down the rows of a single column to the left of the quantitative values associated with them [Few, 2004]. Therefore the information of the table would be shown better by placing the data of the columns in the rows and the data of the rows in the columns.
**data sequence
*:The data in the first row isn't ordered in a logical way, so I would prefer to place the products in an ascending or descending order. It isn't advisable to use a conventional order based on alphabetical sequence, which is useful for look-up purposes but isn't meaningful otherwise [Few, 2004].

* centered alignment of the values and the text
:-make it difficult to read the table.
:Text and numbers that represent quantitative values should always be aligned to the right [Few, 2004]. All numbers in the columns should line up with each other and their headings [Wallace, 2004], each using same number of decimal digits.

* numbers precision
:Some numbers in the table should and could be reduced without the loss of meaningful information to line up with each other in the column.
:To improve readability of the table, high numbers can be truncated to sets of three digits and be declared in the header [Few, 2004] to avoid confusion and misinterpretation as seen in the 'total bytes transferred'-row.

===Correction on the table ===

[[Image:Corrected_table.jpg]]

===Which changes have been made and why is the changed one better than the original table?===
*The table gets a title, a subtitle, a column-title for the first column also and the header-names have been slightly changed, because of the multiple variables, which improves the understanding of the data in the table.
*Only one rule is used to separate the headers of the columns and rows and push the data in the columns together, but there's enough white space left between the data in the rows and the line-headers to see a clearly separation.
*The quantitative values of the categorical subdivision: "WWW", "Gator", "Cydoor", "SaveNow" and "eZula" have been sequenced from left to right, so the data of the rows have been moved to the columns and vice versa, because the comparison of numbers is easier when they are stacked.
*The place of the "SaveNow"-data has been changed with the place of the "eZula"-data to get an hierachical order in the first column of the quantitative values, which looks more logical.
*The numbers and their column-headers have been aligned to the left and the text of the first column to the right to show the parts of the table clearly; like in Figure1.
*The "number of clients"- and "contacted servers"-values has been reduced to an integer. The "transferred bytes" has been truncated to GB to line up with each other in the column and to make the comparison of the columns easier than the centered, different numbers and units of measurement in the table before. So I declare this units in the column-header.

===References===
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Chapter 8 - Table Design.

*[Wallace, 2004]: Rosa Wallace, Designing Tables, NC State University LabWrite Resources, 2004.
[http://www.ncsu.edu/labwrite/res/gh/gh-tables.html http://www.ncsu.edu/labwrite/res/gh/gh-tables.html]

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2008/09|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 2

2008-11-17T12:55:08Z

UE-InfoVis0809 0126665:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe2.html Beschreibung der Aufgabe 2]

===Table to evaluate===

[[Image:Trace_stats04.gif]]

===Critics on the table===
* distracting grid
:The grid makes the table difficult to read, because it distracts the eyes. Less rulers and grid would increase the table's effectiveness [Few, 2004]. More white space around the data and the table would be clearer.
[[Image:PartsOfTable.jpg|right|200px|thumb|Figure1: parts of a table [Few, 2004] ]]
* missing labels = missing information
:The table's information isn't clearly defined, because of the missing Title/Header.
:In addition there should be a title AND subheader because of the multiple variables. Accordingly the column should be titled with the name followed by the units of measurement [Wallace, 2004].
:Figure1 shows the parts of a table and their naming.

* bad ordering of the data
**column Sequence
*:The categorical subdivision should be arranged down the rows of a single column to the left of the quantitative values associated with them [Few, 2004]. Therefore the information of the table would be shown better by placing the data of the columns in the rows and the data of the rows in the columns.
**data sequence
*:The data in the first row isn't ordered in a logical way, so I would prefer to place the products in an ascending or descending order. It isn't advisable to use a conventional order based on alphabetical sequence, which is useful for look-up purposes but isn't meaningful otherwise [Few, 2004].

* centered alignment of the values and the text
:-make it difficult to read the table.
:Text and numbers that represent quantitative values should always be aligned to the right [Few, 2004]. All numbers in the columns should line up with each other and their headings [Wallace, 2004], each using same number of decimal digits.

* numbers precision
:Some numbers in the table should and could be reduced without the loss of meaningful information to line up with each other in the column.
:To improve readability of the table, high numbers can be truncated to sets of three digits and be declared in the header [Few, 2004] to avoid confusion and misinterpretation as seen in the 'total bytes transferred'-row.

===Correction on the table ===

[[Image:Corrected_table.jpg]]

===Which changes have been made and why is the changed one better than the original table?===
*The table gets a title, subtitel, a coulmn-titel also for the first column and the header-names has been easily changed, because of the multiple variables, which made the understanding of the data in the table easier.
*Only one rule is used to separate the headers of the columns and rows and push the data in the columns together, but it's enough white space left between the data in the rows and the line-headers to see a clearly separation.
*The quantitative values of the cateogirical subdivision: "WWW", "Gator", "Cydoor", "SaveNow" and "eZula" have been sequenced from left to right, so the data of the rows have been moved to the columns and vice versa, because the comparison of numbers is easier when they are stacked.
*The place of the "SaveNow"-data has been changed with the place of the "eZula"-data to get an hierachical order in the first column of the quantitative values, which looks more logical.
*The numbers and their column-headers have been aligned to the left and the text of the first column to the right to show the parts of the table clearly; like in Figure1.
*The "number of clients"- and "contacted servers"-values has been reduced to an integer. The "transferred bytes" has been truncated to MB to line up with each other in the column to make the comparison down the columns easier than the centered, different numbers and units of measurement in the table before. So I declare this units in the column-header.

===References===
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Chapter 8 - Table Design.

*[Wallace, 2004]: Rosa Wallace, Designing Tables, NC State University LabWrite Resources, 2004.
[http://www.ncsu.edu/labwrite/res/gh/gh-tables.html http://www.ncsu.edu/labwrite/res/gh/gh-tables.html]

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2008/09|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 2

2008-11-17T12:52:58Z

UE-InfoVis0809 0126665:

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe2.html Beschreibung der Aufgabe 2]

=== Zu beurteilende Tabelle ===

[[Image:Trace_stats04.gif]]

===Critics on the table===
* distracting grid
:The grid makes the table difficult to read, because it distracts the eyes. Less rulers and grid would increase the table's effectiveness [Few, 2004]. More white space around the data and the table would be clearer.
[[Image:PartsOfTable.jpg|right|200px|thumb|Figure1: parts of a table [Few, 2004] ]]
* missing labels = missing information
:The table's information isn't clearly defined, because of the missing Title/Header.
:In addition there should be a title AND subheader because of the multiple variables. Accordingly the column should be titled with the name followed by the units of measurement [Wallace, 2004].
:Figure1 shows the parts of a table and their naming.

* bad ordering of the data
**column Sequence
*:The categorical subdivision should be arranged down the rows of a single column to the left of the quantitative values associated with them [Few, 2004]. Therefore the information of the table would be shown better by placing the data of the columns in the rows and the data of the rows in the columns.
**data sequence
*:The data in the first row isn't ordered in a logical way, so I would prefer to place the products in an ascending or descending order. It isn't advisable to use a conventional order based on alphabetical sequence, which is useful for look-up purposes but isn't meaningful otherwise [Few, 2004].

* centered alignment of the values and the text
:-make it difficult to read the table.
:Text and numbers that represent quantitative values should always be aligned to the right [Few, 2004]. All numbers in the columns should line up with each other and their headings [Wallace, 2004], each using same number of decimal digits.

* numbers precision
:Some numbers in the table should and could be reduced without the loss of meaningful information to line up with each other in the column.
:To improve readability of the table, high numbers can be truncated to sets of three digits and be declared in the header [Few, 2004] to avoid confusion and misinterpretation as seen in the 'total bytes transferred'-row.

===Correction on the table ===

[[Image:Corrected_table.jpg]]

===Which changes have been made and why is the changed one better than the original table?===
*The table gets a title, subtitel, a coulmn-titel also for the first column and the header-names has been easily changed, because of the multiple variables, which made the understanding of the data in the table easier.
*Only one rule is used to separate the headers of the columns and rows and push the data in the columns together, but it's enough white space left between the data in the rows and the line-headers to see a clearly separation.
*The quantitative values of the cateogirical subdivision: "WWW", "Gator", "Cydoor", "SaveNow" and "eZula" have been sequenced from left to right, so the data of the rows have been moved to the columns and vice versa, because the comparison of numbers is easier when they are stacked.
*The place of the "SaveNow"-data has been changed with the place of the "eZula"-data to get an hierachical order in the first column of the quantitative values, which looks more logical.
*The numbers and their column-headers have been aligned to the left and the text of the first column to the right to show the parts of the table clearly; like in Figure1.
*The "number of clients"- and "contacted servers"-values has been reduced to an integer. The "transferred bytes" has been truncated to MB to line up with each other in the column to make the comparison down the columns easier than the centered, different numbers and units of measurement in the table before. So I declare this units in the column-header.

===References===
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Chapter 8 - Table Design.

*[Wallace, 2004]: Rosa Wallace, Designing Tables, NC State University LabWrite Resources, 2004.
[http://www.ncsu.edu/labwrite/res/gh/gh-tables.html http://www.ncsu.edu/labwrite/res/gh/gh-tables.html]

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2008/09|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 2

2008-11-17T12:51:49Z

UE-InfoVis0809 0126665: some corr.

== Aufgabenstellung ==
[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe2.html Beschreibung der Aufgabe 2]

=== Zu beurteilende Tabelle ===

[[Image:Trace_stats04.gif]]

===Critics on the table===
* distracting grid
:The grid makes the table difficult to read, because it distracts the eyes. Less rulers and grid would increase the table's effectiveness[Few, 2004]. More white space around the data and the table would be clearer.
[[Image:PartsOfTable.jpg|right|200px|thumb|Figure1: parts of a table [Few, 2004] ]]
* missing labels = missing information
:The table's information isn't clearly defined, because of the missing Title/Header.
:In addition there should be a title AND subheader because of the multiple variables. Accordingly the column should be titled with the name followed by the units of measurement [Wallace, 2004].
:Figure1 shows the parts of a table and their naming.

* bad ordering of the data
**column Sequence
*:The categorical subdivision should be arranged down the rows of a single column to the left of the quantitative values associated with them [Few, 2004]. Therefore the information of the table would be shown better by placing the data of the columns in the rows and the data of the rows in the columns.
**data sequence
*:The data in the first row isn't ordered in a logical way, so I would prefer to place the products in an ascending or descending order. It isn't advisable to use a conventional order based on alphabetical sequence, which is useful for look-up purposes but isn't meaningful otherwise [Few, 2004].

* centered alignment of the values and the text
:-make it difficult to read the table.
:Text and numbers that represent quantitative values should always be aligned to the right [Few, 2004]. All numbers in the columns should line up with each other and their headings [Wallace, 2004], each using same number of decimal digits.

* number precision
:Some numbers in the table should and could be reduced without the loss of meaningful information to line up with each other in the column.
:To improve readability of the table, high numbers can be truncated to sets of three digits and be declared in the header [Few, 2004] to avoid confusion and misinterpretation as seen in the 'total bytes transferred'-row.

===Correction on the table ===

[[Image:Corrected_table.jpg]]

===Which changes have been made and why is the changed one better than the original table?===
*The table gets a title, subtitel, a coulmn-titel also for the first column and the header-names has been easily changed, because of the multiple variables, which made the understanding of the data in the table easier.
*Only one rule is used to separate the headers of the columns and rows and push the data in the columns together, but it's enough white space left between the data in the rows and the line-headers to see a clearly separation.
*The quantitative values of the cateogirical subdivision: "WWW", "Gator", "Cydoor", "SaveNow" and "eZula" have been sequenced from left to right, so the data of the rows have been moved to the columns and vice versa, because the comparison of numbers is easier when they are stacked.
*The place of the "SaveNow"-data has been changed with the place of the "eZula"-data to get an hierachical order in the first column of the quantitative values, which looks more logical.
*The numbers and their column-headers have been aligned to the left and the text of the first column to the right to show the parts of the table clearly; like in Figure1.
*The "number of clients"- and "contacted servers"-values has been reduced to an integer. The "transferred bytes" has been truncated to MB to line up with each other in the column to make the comparison down the columns easier than the centered, different numbers and units of measurement in the table before. So I declare this units in the column-header.

===References===
*[Few, 2004]:Stephen Few, Show Me the Numbers: Designing Tables and Graphs to Enlighten, Analytics Press, 2004, Chapter 8 - Table Design.

*[Wallace, 2004]: Rosa Wallace, Designing Tables, NC State University LabWrite Resources, 2004.
[http://www.ncsu.edu/labwrite/res/gh/gh-tables.html http://www.ncsu.edu/labwrite/res/gh/gh-tables.html]

== Links ==

* [[Teaching:TUW_-_UE_InfoVis_WS_2008/09|InfoVis:Wiki UE Homepage]]

* [http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/ UE InfoVis]

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-16T12:17:59Z

UE-InfoVis0809 0126665:

Parallel Coordinates is a common way of visualizing ''high-dimensional geometry'' and analyzing ''multivariate data''. [Wikipedia]

===Revealed Information===
When analyzing data, it is almost always the first step to get an overview and understand the data, its properties, distribution and pitfalls. In order to achieve this, usually the data is visualized. By visualizing multidimensional datasets with common coordinate systems human cognition is limited to few dimensions [Inselberg, 1990]. The fourth dimension in 3d-diagramms can be visualized by using different colors, point-sizes or shapes. But this technique does not always point out the features you are interested in. 
Parallel coordinates enable displaying multi dimensional datasets in coordinate systems by placing each dimension on its own axis and arranging these axes parallel to each other. Each point is put on the accordant axis; lines are drawn to connect related data points [Scienceblogs, 2008]. Adding more dimensions in the Parallel Coordinates involves adding more axes. 

Figure1 shows the following three points with four dimensions in a parallel coordinate system: 
*P1(20,28,80,20),P2(30,39,35,31),P3(47,48,46,44) 

[[Image:Pc_figure1.gif|right|200px|thumb|Figure1: Parallel Coordinates ]]

As this figure demonstrates distinctive features can be easily found using this technique.
In this way, distinctive features and the distribution of the data can be explored. One can see immediately, that the values of P1 are somehow diverse. With the help of hiliting or linking and brushing also the data can be explored in more detail.

===Real world example===
An example of Parallel Coordinates can be found when evaluating automobiles: the different axis are the horsepower, acceleration, the weight, cylinders etc. The lines crossing the axes represent the values corresponding to a particular car model. In figure 2 cars with six cylinders were emphasized through brushing, histograms are laid over axes, and one data-point is shown with all details. [Dürsteler, 2008], [vrvis.at].

[[Image:Pc_example_car.gif|right|200px|thumb|Figure2: Car Model ]]

===Extruded Parallel Coordinates===
An extension of Parallel Coordinates are Extruded Parallel Coordinates. For each dataset a new set of axis is created and shifted along the z-axis as seen in figure 3. In interactive environments a clear overview can be given by drawing planes between polylines of two different datasets and tilting or rotating the model/ moving the point of view. [CG2, 1997].

[[Image:Pc_extrude.gif|right|200px|thumb|Figure3: Extruded Parallel Coordinates ]]

== References ==
*[Inselberg, 1990] A. Inselberg, B. Dimsdale, Parallel Coordinates: a tool for visualizing multi-dimensionalgeometry in Proceedings of the First IEEE Conference on Visualization, 1990. Published at Oct 23-26, 1990. Retrieved at Nov 2, 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=146402

*[CG1, 1997] Günter Rubik, Parallele Koordinaten. Created at June 27, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/paracord.htm

*[CG2, 1997] Günter Rubik, Extruded Parallel Coordinates. Created at July 19, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/extruded.htm

*[Scienceblogs, 2008] Jörg Blumtritt, Parallelkoordinaten. Created at Feb 28, 2008. Retrieved at Nov 2, 2008. http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

*[Wikipedia] Retrieved at Nov 2, 2008. http://en.wikipedia.org/wiki/Parallel_coordinates

*[Dürsteler, 2008] Juan C. Dürsteler, Visualisation in the 20th century. Retrieved at Nov 2, 2008. http://www.infovis.net/printMag.php?lang=2&num=112

*[vrvis.at] Helwig Hauser, Angular Brushing for Extended Parallel Coordinates. Retrieved at Nov 2, 2008. http://www.vrvis.at/via/research/ang-brush/index.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-16T12:09:44Z

UE-InfoVis0809 0126665: wasnt logged in for last three changes

Parallel Coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data. [Wikipedia]

===Revealed Information===
When analyzing data, it is almost always the first step to get an overview and understand the data, its properties, distribution and pitfalls. In order to achieve this, usually the data is visualized. By visualizing multidimensional datasets with common coordinate systems human cognition is limited to few dimensions [Inselberg, 1990]. The fourth dimension in 3d-diagramms can be visualized by using different colors, point-sizes or shapes. But this technique does not always point out the features you are interested in. 
Parallel coordinates enable displaying multi dimensional datasets in coordinate systems by placing each dimension on its own axis and arranging these axes parallel to each other. Each point is put on the accordant axis; lines are drawn to connect related data points [Scienceblogs, 2008]. Adding more dimensions in the Parallel Coordinates involves adding more axes. 

Figure1 shows the following three points with four dimensions in a parallel coordinate system: 
*P1(20,28,80,20),P2(30,39,35,31),P3(47,48,46,44) 

[[Image:Pc_figure1.gif|right|200px|thumb|Figure1: Parallel Coordinates ]]

As this figure demonstrates distinctive features can be easily found using this technique.
In this way, distinctive features and the distribution of the data can be explored. One can see immediately, that the values of P1 are somehow diverse. With the help of hiliting or linking and brushing also the data can be explored in more detail.

===Real world example===
An example of Parallel Coordinates can be found when evaluating automobiles: the different axis are the horsepower, acceleration, the weight, cylinders etc. The lines crossing the axes represent the values corresponding to a particular car model. In figure 2 cars with six cylinders were emphasized through brushing, histograms are laid over axes, and one data-point is shown with all details. [Dürsteler, 2008], [vrvis.at].

[[Image:Pc_example_car.gif|right|200px|thumb|Figure2: Car Model ]]

===Extruded Parallel Coordinates===
An extension of Parallel Coordinates are Extruded Parallel Coordinates. For each dataset a new set of axis is created and shifted along the z-axis as seen in figure 3. In interactive environments a clear overview can be given by drawing planes between polylines of two different datasets and tilting or rotating the model/ moving the point of view. [CG2, 1997].

[[Image:Pc_extrude.gif|right|200px|thumb|Figure3: Extruded Parallel Coordinates ]]

== References ==
*[Inselberg, 1990] A. Inselberg, B. Dimsdale, Parallel Coordinates: a tool for visualizing multi-dimensionalgeometry in Proceedings of the First IEEE Conference on Visualization, 1990. Published at Oct 23-26, 1990. Retrieved at Nov 2, 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=146402

*[CG1, 1997] Günter Rubik, Parallele Koordinaten. Created at June 27, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/paracord.htm

*[CG2, 1997] Günter Rubik, Extruded Parallel Coordinates. Created at July 19, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/extruded.htm

*[Scienceblogs, 2008] Jörg Blumtritt, Parallelkoordinaten. Created at Feb 28, 2008. Retrieved at Nov 2, 2008. http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

*[Wikipedia] Retrieved at Nov 2, 2008. http://en.wikipedia.org/wiki/Parallel_coordinates

*[Dürsteler, 2008] Juan C. Dürsteler, Visualisation in the 20th century. Retrieved at Nov 2, 2008. http://www.infovis.net/printMag.php?lang=2&num=112

*[vrvis.at] Helwig Hauser, Angular Brushing for Extended Parallel Coordinates. Retrieved at Nov 2, 2008. http://www.vrvis.at/via/research/ang-brush/index.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-07T08:07:44Z

UE-InfoVis0809 0126665:

Parallel Coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data. [Wikipedia]

===Revealed Information===
When analyzing data, it is almost always the first step to get an overview and understand the data, its properties, distribution and pitfalls. In order to achieve this, usually the data is visualized. By visualizing multidimensional datasets with common coordinate systems human cognition is limited to few dimensions [IEEE, 1990]. The fourth dimension in 3d-diagramms can be visualized by using different colors, point-sizes or shapes. But this technique does not always point out the features you are interested in. 
Parallel coordinates enable displaying multi dimensional datasets in coordinate systems by placing each dimension on its own axis and arranging these axes parallel to each other. Each point is put on the accordant axis; lines are drawn to connect related data points [Scienceblogs, 2008]. Adding more dimensions in the Parallel Coordinates involves adding more axes. 

Figure1 shows the following three points with four dimensions in a parallel coordinate system: 
*P1(20,28,80,20),P2(30,39,35,31),P3(47,48,46,44) 

[[Image:Pc_figure1.gif|right|200px|thumb|Figure1: Parallel Coordinates [Scienceblogs, 2008] ]]

As this figure demonstrates distinctive features can be easily found using this technique.
In this way, distinctive features and the distribution of the data can be explored. One can see immediately, that the values of P1 are somehow diverse. With the help of hiliting or linking and brushing also the data can be explored in more detail.

===Real world example===
An example of Parallel Coordinates can be found when evaluating automobiles: the different axis are the horsepower, acceleration, the weight, cylinders etc. The lines crossing the axes represent the values corresponding to a particular car model. In this figure cars with six cylinders were emphasized through brushing, histograms are laid over axes, and one data-point is shown with all details. [infovis.net], [vrvis.at].

[[Image:Pc_example_car.gif|right|200px|thumb|Figure2: Car Model [vrvis.at] ]]

===Extruded Parallel Coordinates===
An extension of Parallel Coordinates are Extruded Parallel Coordinates. For each dataset a new set of axis is created and shifted along the z-axis. In interactive environments a clear overview can be given by drawing planes between polylines of two different datasets and tilting or rotating the model/ moving the point of view. [CG2, 1997].

[[Image:Pc_extrude.gif|right|200px|thumb|Figure3: Extruded Parallel Coordinates [CG2, 1997] ]]

== References ==
*[IEEE, 1990] A. Inselberg, B. Dimsdale, Parallel Coordinates: a tool for visualizing multi-dimensionalgeometry. Published at Oct 23-26, 1990. Retrieved at Nov 2, 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=146402

*[CG1, 1997] Günter Rubik, Parallele Koordinaten. Created at June 27, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/paracord.htm.

*[CG2, 1997] Günter Rubik, Extruded Parallel Coordinates. Created at July 19, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/extruded.htm

*[Scienceblogs, 2008] Jörg Blumtritt, Parallelkoordinaten. Created at Feb 28, 2008. Retrieved at Nov 2, 2008. http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

*[Wikipedia] Retrieved at Nov 2, 2008. http://en.wikipedia.org/wiki/Parallel_coordinates

*[infovis.net] Juan C. Dürsteler, Visualisation in the 20th century. Retrieved at Nov 2, 2008. http://www.infovis.net/printMag.php?lang=2&num=112

*[vrvis.at] Helwig Hauser, Angular Brushing for Extended Parallel Coordinates. Retrieved at Nov 2, 2008. http://www.vrvis.at/via/research/ang-brush/index.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-06T18:32:29Z

UE-InfoVis0809 0126665:

Parallel Coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data.

===Revealed Information===
When analyzing data, it is almost always the first step to get an overview and understand the data, its properties, distribution and pitfalls. In order to achieve this, usually the data is visualized. By visualizing multidimensional datasets with common coordinate systems human cognition is limited to few dimensions [IEEE, 1990]. The fourth dimension in 3d-diagramms can be visualized by using different colors, point-sizes or shapes. But this technique does not always point out the features you are interested in. 
Parallel coordinates enable displaying multi dimensional datasets in coordinate systems by placing each dimension on its own axis and arranging these axes parallel to each other. Each point is put on the accordant axis; lines are drawn to connect related data points [Scienceblogs, 2008]. Adding more dimensions in the Parallel Coordinates involves adding more axes. 

Figure1 shows the following three points with four dimensions in a parallel coordinate system: 
*P1(20,28,80,20),P2(30,39,35,31),P3(47,48,46,44) 

[[Image:Pc_figure1.gif|right|200px|thumb|Figure1: Parallel Coordinates [Scienceblogs, 2008] ]]

As this figure demonstrates distinctive features can be easily found using this technique.
In this way, distinctive features and the distribution of the data can be explored. One can see immediately, that the values of P1 are somehow diverse. With the help of hiliting or linking and brushing also the data can be explored in more detail.

===Real world example===
An example of Parallel Coordinates can be found when evaluating automobiles: the different axis are the horsepower, acceleration, the weight, cylinders etc. The lines crossing the axes represent the values corresponding to a particular car model. In this figure cars with six cylinders were emphasized through brushing, histograms are laid over axes, and one data-point is shown with all details. [infovis.net], [vrvis.at].

[[Image:Pc_example_car.gif|right|200px|thumb|Figure2: Car Model [vrvis.at] ]]

===Extruded Parallel Coordinates===
An extension of Parallel Coordinates are Extruded Parallel Coordinates. For each dataset a new set of axis is created and shifted along the z-axis. In interactive environments a clear overview can be given by drawing planes between polylines of two different datasets and tilting or rotating the model/ moving the point of view. [CG2, 1997].

[[Image:Pc_extrude.gif|right|200px|thumb|Figure3: Extruded Parallel Coordinates [CG2, 1997] ]]

== References ==
*[IEEE, 1990] A. Inselberg, B. Dimsdale, Parallel Coordinates: a tool for visualizing multi-dimensionalgeometry. Published at Oct 23-26, 1990. Retrieved at Nov 2, 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=146402

*[CG1, 1997] Günter Rubik, Parallele Koordinaten. Created at June 27, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/paracord.htm.

*[CG2, 1997] Günter Rubik, Extruded Parallel Coordinates. Created at July 19, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/extruded.htm

*[Scienceblogs, 2008] Jörg Blumtritt, Parallelkoordinaten. Created at Feb 28, 2008. Retrieved at Nov 2, 2008. http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

*[Wikipedia] Retrieved at Nov 2, 2008. http://en.wikipedia.org/wiki/Parallel_coordinates

*[infovis.net] Juan C. Dürsteler, Visualisation in the 20th century. Retrieved at Nov 2, 2008. http://www.infovis.net/printMag.php?lang=2&num=112

*[vrvis.at] Helwig Hauser, Angular Brushing for Extended Parallel Coordinates. Retrieved at Nov 2, 2008. http://www.vrvis.at/via/research/ang-brush/index.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-06T18:30:54Z

UE-InfoVis0809 0126665:

Parallel coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data.

===Revealed Information===
When analyzing data, it is almost always the first step to get an overview and understand the data, its properties, distribution and pitfalls. In order to achieve this, usually the data is visualized. By visualizing multidimensional datasets with common coordinate systems human cognition is limited to few dimensions [IEEE, 1990]. The fourth dimension in 3d-diagramms can be visualized by using different colors, point-sizes or shapes. But this technique does not always point out the features you are interested in. 
Parallel coordinates enable displaying multi dimensional datasets in coordinate systems by placing each dimension on its own axis and arranging these axes parallel to each other. Each point is put on the accordant axis; lines are drawn to connect related data points [Scienceblogs, 2008]. Adding more dimensions in the parallel coordinates involves adding more axes. 

Figure1 shows the following three points with four dimensions in a parallel coordinate system: 
*P1(20,28,80,20),P2(30,39,35,31),P3(47,48,46,44) 

[[Image:Pc_figure1.gif|right|200px|thumb|Figure1: Parallel Coordinates [Scienceblogs, 2008] ]]

As this figure demonstrates distinctive features can be easily found using this technique.
In this way, distinctive features and the distribution of the data can be explored. One can see immediately, that the values of P1 are somehow diverse. With the help of hiliting or linking and brushing also the data can be explored in more detail.

===Real world example===
An example of Parallel Coordinates can be found when evaluating automobiles: the different axis are the horsepower, acceleration, the weight, cylinders etc. The lines crossing the axes represent the values corresponding to a particular car model. In this figure cars with six cylinders were emphasized through brushing, histograms are laid over axes, and one data-point is shown with all details. [infovis.net], [vrvis.at].

[[Image:Pc_example_car.gif|right|200px|thumb|Figure2: Car Model [vrvis.at] ]]

===Extruded Parallel Coordinates===
An extension of Parallel Coordinates are Extruded Parallel Coordinates. For each dataset a new set of axis is created and shifted along the z-axis. In interactive environments a clear overview can be given by drawing planes between polylines of two different datasets and tilting or rotating the model/ moving the point of view. [CG2, 1997].

[[Image:Pc_extrude.gif|right|200px|thumb|Figure3: Extruded Parallel Coordinates [CG2, 1997] ]]

== References ==
*[IEEE, 1990] A. Inselberg, B. Dimsdale, Parallel coordinates: a tool for visualizing multi-dimensionalgeometry. Published at Oct 23-26, 1990. Retrieved at Nov 2, 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=146402

*[CG1, 1997] Günter Rubik, Parallele Koordinaten. Created at June 27, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/paracord.htm.

*[CG2, 1997] Günter Rubik, Extruded Parallel Coordinates. Created at July 19, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/extruded.htm

*[Scienceblogs, 2008] Jörg Blumtritt, Parallelkoordinaten. Created at Feb 28, 2008. Retrieved at Nov 2, 2008. http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

*[Wikipedia] Retrieved at Nov 2, 2008. http://en.wikipedia.org/wiki/Parallel_coordinates

*[infovis.net] Juan C. Dürsteler, Visualisation in the 20th century. Retrieved at Nov 2, 2008. http://www.infovis.net/printMag.php?lang=2&num=112

*[vrvis.at] Helwig Hauser, Angular Brushing for Extended Parallel Coordinates. Retrieved at Nov 2, 2008. http://www.vrvis.at/via/research/ang-brush/index.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-06T18:29:37Z

UE-InfoVis0809 0126665:

Parallel coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data.

===Revealed Information===
When analyzing data, it is almost always the first step to get an overview and understand the data, its properties, distribution and pitfalls. In order to achieve this, usually the data is visualized. By visualizing multidimensional datasets with common coordinate systems human cognition is limited to few dimensions [IEEE, 1990]. The fourth dimension in 3d-diagramms can be visualized by using different colors, point-sizes or shapes. But this technique does not always point out the features you are interested in. 
Parallel coordinates enable displaying multi dimensional datasets in coordinate systems by placing each dimension on its own axis and arranging these axes parallel to each other. Each point is put on the accordant axis; lines are drawn to connect related data points [Scienceblogs, 2008]. Adding more dimensions in the parallel coordinates involves adding more axes. 

Figure1 shows the following three points with four dimensions in a parallel coordinate system: 
*P1(20,28,80,20),P2(30,39,35,31),P3(47,48,46,44) 

[[Image:Pc_figure1.gif|right|200px|thumb|Figure1: Parallel Coordinates [Scienceblogs, 2008] ]]

As this figure demonstrates distinctive features can be easily found using this technique.
In this way, distinctive features and the distribution of the data can be explored. One can see immediately, that the values of P1 are somehow diverse. With the help of hiliting or linking and brushing also the data can be explored in more detail.

===Example===
An example of Parallel Coordinates can be found when evaluating automobiles: the different axis are the horsepower, acceleration, the weight, cylinders etc. The lines crossing the axes represent the values corresponding to a particular car model. In this figure cars with six cylinders were emphasized through brushing, histograms are laid over axes, and one data-point is shown with all details. [infovis.net], [vrvis.at].

[[Image:Pc_example_car.gif|right|200px|thumb|Figure2: Car Model [vrvis.at] ]]

===Extruded Parallel Coordinates===
An extension of Parallel Coordinates are Extruded Parallel Coordinates. For each dataset a new set of axis is created and shifted along the z-axis. In interactive environments a clear overview can be given by drawing planes between polylines of two different datasets and tilting or rotating the model/ moving the point of view. [CG2, 1997].

[[Image:Pc_extrude.gif|right|200px|thumb|Figure3: Extruded Parallel Coordinates [CG2, 1997] ]]

== References ==
*[IEEE, 1990] A. Inselberg, B. Dimsdale, Parallel coordinates: a tool for visualizing multi-dimensionalgeometry. Published at Oct 23-26, 1990. Retrieved at Nov 2, 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=146402

*[CG1, 1997] Günter Rubik, Parallele Koordinaten. Created at June 27, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/paracord.htm.

*[CG2, 1997] Günter Rubik, Extruded Parallel Coordinates. Created at July 19, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/extruded.htm

*[Scienceblogs, 2008] Jörg Blumtritt, Parallelkoordinaten. Created at Feb 28, 2008. Retrieved at Nov 2, 2008. http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

*[Wikipedia] Retrieved at Nov 2, 2008. http://en.wikipedia.org/wiki/Parallel_coordinates

*[infovis.net] Juan C. Dürsteler, Visualisation in the 20th century. Retrieved at Nov 2, 2008. http://www.infovis.net/printMag.php?lang=2&num=112

*[vrvis.at] Helwig Hauser, Angular Brushing for Extended Parallel Coordinates. Retrieved at Nov 2, 2008. http://www.vrvis.at/via/research/ang-brush/index.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-06T18:10:26Z

UE-InfoVis0809 0126665:

Parallel coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data.

===Revealed Information===
When analyzing data, it is almost always the first step to get an overview and understand the data, its properties, distribution and pitfalls. In order to achieve this, usually the data is visualized. By visualizing multidimensional datasets with common coordinate systems human cognition is limited to few dimensions [IEEE, 1990]. The fourth dimension in 3d-diagramms can be visualized by using different colors, point-sizes or shapes. But this technique does not always point out the features you are interested in. 
Parallel coordinates enable displaying multi dimensional datasets in coordinate systems by placing each dimension on its own axis and arranging these axes parallel to each other. Each point is put on the accordant axis; lines are drawn to connect related data points [Scienceblogs, 2008]. Adding more dimensions in the parallel coordinates involves adding more axes. 

Figure1 shows the following three points with four dimensions in a parallel coordinate system: 
*P1(20,28,80,20),P2(30,39,35,31),P3(47,48,46,44) 

[[Image:Pc_figure1.gif|right|200px|thumb|Figure1: Parallel Coordinates]]

As this figure demonstrates distinctive features can be easily found using this technique.
In this way, distinctive features and the distribution of the data can be explored. One can see immediately, that the values of P1 are somehow diverse. With the help of hiliting or linking and brushing also the data can be explored in more detail.

===Example===
An example of Parallel Coordinates can be found when evaluating automobiles: the different axis are the horsepower, acceleration, the weight, cylinders etc. The lines crossing the axes represent the values corresponding to a particular car model. In this figure cars with six cylinders were emphasized through brushing, histograms are laid over axes, and one data-point is shown with all details. [infovis.net], [vrvis.at].
[[Image:Pc_example_car.gif|right|200px|thumb|Figure2: Car model]]

===Extruded Parallel Coordinates===
An extension of Parallel Coordinates are Extruded Parallel Coordinates. For each dataset a new set of axis is created and shifted along the z-axis. In interactive environments a clear overview can be given by drawing planes between polylines of two different datasets and tilting or rotating the model/ moving the point of view. [CG2, 1997].
[[Image:Pc_extrude.gif|right|200px|thumb|Figure3: Extruded Parallel Coordinates]]

== References ==
*[IEEE, 1990] A. Inselberg, B. Dimsdale, Parallel coordinates: a tool for visualizing multi-dimensionalgeometry. Published at Oct 23-26, 1990. Retrieved at Nov 2, 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=146402

*[CG1, 1997] Günter Rubik, Parallele Koordinaten. Created at June 27, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/paracord.htm.

*[CG2, 1997] Günter Rubik, Extruded Parallel Coordinates. Created at July 19, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/extruded.htm

*[Scienceblogs, 2008] Jörg Blumtritt, Parallelkoordinaten. Created at Feb 28, 2008. Retrieved at Nov 2, 2008. http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

*[Wikipedia] Retrieved at Nov 2, 2008. http://en.wikipedia.org/wiki/Parallel_coordinates

*[infovis.net] Juan C. Dürsteler, Visualisation in the 20th century. Retrieved at Nov 2, 2008. http://www.infovis.net/printMag.php?lang=2&num=112

*[vrvis.at] Helwig Hauser, Angular Brushing for Extended Parallel Coordinates. Retrieved at Nov 2, 2008. http://www.vrvis.at/via/research/ang-brush/index.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-06T18:09:47Z

UE-InfoVis0809 0126665:

Parallel coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data.

===Revealed Information===
When analyzing data, it is almost always the first step to get an overview and understand the data, its properties, distribution and pitfalls. In order to achieve this, usually the data is visualized. By visualizing multidimensional datasets with common coordinate systems human cognition is limited to few dimensions [IEEE, 1990]. The fourth dimension in 3d-diagramms can be visualized by using different colors, point-sizes or shapes. But this technique does not always point out the features you are interested in. 
Parallel coordinates enable displaying multi dimensional datasets in coordinate systems by placing each dimension on its own axis and arranging these axes parallel to each other. Each point is put on the accordant axis; lines are drawn to connect related data points [Scienceblogs, 2008]. Adding more dimensions in the parallel coordinates involves adding more axes. 

Figure1 shows the following three points with four dimensions in a parallel coordinate system: 
*P1(20,28,80,20),P2(30,39,35,31),P3(47,48,46,44) 

[[Image:Pc_figure1.gif|right|200px|thumb|Figure1: Parallel Coordinates]]

As this figure demonstrates distinctive features can be easily found using this technique.
In this way, distinctive features and the distribution of the data can be explored. One can see immediately, that the values of P1 are somehow diverse. With the help of hiliting or linking and brushing also the data can be explored in more detail.

===Example===
An example of Parallel Coordinates can be found when evaluating automobiles: the different axis are the horsepower, acceleration, the weight, cylinders etc. The lines crossing the axes represent the values corresponding to a particular car model. In this figure cars with six cylinders were emphasized through brushing, histograms are laid over axes, and one data-point is shown with all details. [infovis.net], [vrvis.at].
[[Image:Pc_example_car.gif|right|200px|thumb|"Figure2: Car model".]]

===Extruded Parallel Coordinates===
An extension of Parallel Coordinates are Extruded Parallel Coordinates. For each dataset a new set of axis is created and shifted along the z-axis. In interactive environments a clear overview can be given by drawing planes between polylines of two different datasets and tilting or rotating the model/ moving the point of view. [CG2, 1997].
[[Image:Pc_extrude.gif|right|200px|thumb|Figure3: Extruded Parallel Coordinates.]]

== References ==
*[IEEE, 1990] A. Inselberg, B. Dimsdale, Parallel coordinates: a tool for visualizing multi-dimensionalgeometry. Published at Oct 23-26, 1990. Retrieved at Nov 2, 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=146402

*[CG1, 1997] Günter Rubik, Parallele Koordinaten. Created at June 27, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/paracord.htm.

*[CG2, 1997] Günter Rubik, Extruded Parallel Coordinates. Created at July 19, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/extruded.htm

*[Scienceblogs, 2008] Jörg Blumtritt, Parallelkoordinaten. Created at Feb 28, 2008. Retrieved at Nov 2, 2008. http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

*[Wikipedia] Retrieved at Nov 2, 2008. http://en.wikipedia.org/wiki/Parallel_coordinates

*[infovis.net] Juan C. Dürsteler, Visualisation in the 20th century. Retrieved at Nov 2, 2008. http://www.infovis.net/printMag.php?lang=2&num=112

*[vrvis.at] Helwig Hauser, Angular Brushing for Extended Parallel Coordinates. Retrieved at Nov 2, 2008. http://www.vrvis.at/via/research/ang-brush/index.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-03T11:16:07Z

UE-InfoVis0809 0126665:

Parallel coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data.

===Revealed Information===
When analyzing data, it is almost always the first step to get an overview and understand the data, its properties, distribution and pitfalls. In order to achieve this, usually the data is visualized. By visualizing multidimensional datasets with common coordinate systems human cognition is limited to few dimensions [IEEE, 1990]. The fourth dimension in 3d-diagramms can be visualized by using different colors, point-sizes or shapes. But this technique does not always point out the features you are interested in. 
Parallel coordinates enable displaying multi dimensional datasets in coordinate systems by placing each dimension on its own axis and arranging these axes parallel to each other. Each point is put on the accordant axis; lines are drawn to connect related data points [Scienceblogs, 2008]. Adding more dimensions in the parallel coordinates involves adding more axes. 

Figure1 shows the following three points with four dimensions in a parallel coordinate system: 
*P1(20,28,80,20),P2(30,39,35,31),P3(47,48,46,44) 

[[Image:Pc_figure1.gif|right|200px|thumb|Figure1: Parallel Coordinates]]

As this figure demonstrates distinctive features can be easily found using this technique.
In this way, distinctive features and the distribution of the data can be explored. One can see immediately, that the values of P1 are somehow diverse. With the help of hiliting or linking and brushing also the data can be explored in more detail.

===Example===
An example of Parallel Coordinates can be found when evaluating automobiles: the different axis are the horsepower, acceleration, the weight, cylinders etc. The lines crossing the axes represent the values corresponding to a particular car model. In this figure cars with six cylinders were emphasized through brushing, histograms are laid over axes, and one data-point is shown with all details. [infovis.net], [vrvis.at].
[[Image:Pc_example_car.gif|right|200px|thumb|Example "Figure2: Car model".]]

===Extruded Parallel Coordinates===
An extension of Parallel Coordinates are Extruded Parallel Coordinates. For each dataset a new set of axis is created and shifted along the z-axis. In interactive environments a clear overview can be given by drawing planes between polylines of two different datasets and tilting or rotating the model/ moving the point of view. [CG2, 1997].
[[Image:Pc_extrude.gif|right|200px|thumb|Figure3: Extruded Parallel Coordinates.]]

== References ==
*[IEEE, 1990] A. Inselberg, B. Dimsdale, Parallel coordinates: a tool for visualizing multi-dimensionalgeometry. Published at Oct 23-26, 1990. Retrieved at Nov 2, 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=146402

*[CG1, 1997] Günter Rubik, Parallele Koordinaten. Created at June 27, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/paracord.htm.

*[CG2, 1997] Günter Rubik, Extruded Parallel Coordinates. Created at July 19, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/extruded.htm

*[Scienceblogs, 2008] Jörg Blumtritt, Parallelkoordinaten. Created at Feb 28, 2008. Retrieved at Nov 2, 2008. http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

*[Wikipedia] Retrieved at Nov 2, 2008. http://en.wikipedia.org/wiki/Parallel_coordinates

*[infovis.net] Juan C. Dürsteler, Visualisation in the 20th century. Retrieved at Nov 2, 2008. http://www.infovis.net/printMag.php?lang=2&num=112

*[vrvis.at] Helwig Hauser, Angular Brushing for Extended Parallel Coordinates. Retrieved at Nov 2, 2008. http://www.vrvis.at/via/research/ang-brush/index.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-03T11:06:02Z

UE-InfoVis0809 0126665:

Parallel coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data.

===Revealed Information===
When analyzing data, it is almost always the first step to get an overview and understand the data, its properties, distribution and pitfalls. In order to achieve this, usually the data is visualized. By visualizing multidimensional datasets with common coordinate systems human cognition is limited to few dimensions [IEEE, 1990]. The fourth dimension in 3d-diagramms can be visualized by using different colors, point-sizes or shapes. But this technique does not always point out the features you are interested in. 
Parallel coordinates enable displaying multi dimensional datasets in coordinate systems by placing each dimension on its own axis and arranging these axes parallel to each other. Each point is put on the accordant axis; lines are drawn to connect related data points [Scienceblogs, 2008]. Adding more dimensions in the parallel coordinates involves adding more axes. 
Figure1 shows the following three points with four dimensions in a parallel coordinate system: 
P1(20,28,80,20) 
P2(30,39,35,31) 
P3(47,48,46,44) 

[[Image:Pc_figure1.gif|right|200px|thumb|Figure1: Parallel Coordinates]]

As this figure demonstrates distinctive features can be easily found using this technique.
In this way, distinctive features and the distribution of the data can be explored. One can see immediately, that the values of P1 are somehow diverse. With the help of hiliting or linking and brushing also the data can be explored in more detail.

===Example===
An example of Parallel Coordinates can be found when evaluating automobiles: the different axis are the horsepower, acceleration, the weight, cylinders etc. The lines crossing the axes represent the values corresponding to a particular car model. In this figure cars with six cylinders were emphasized through brushing, histograms are laid over axes, and one data-point is shown with all details. [infovis.net], [vrvis.at].
[[Image:Pc_example_car.gif|right|200px|thumb|Example "Figure2: Car model".]]

===Extruded Parallel Coordinates===
An extension of Parallel Coordinates are Extruded Parallel Coordinates. For each dataset a new set of axis is created and shifted along the z-axis. In interactive environments a clearly overview can be given by drawing planes between polylines of two different datasets [CG2, 1997].
[[Image:Pc_extrude.gif|right|200px|thumb|Figure3: Extruded Parallel Coordinates.]]

== References ==
*[IEEE, 1990] A. Inselberg, B. Dimsdale, Parallel coordinates: a tool for visualizing multi-dimensionalgeometry. Published at Oct 23-26, 1990. Retrieved at Nov 2, 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=146402

*[CG1, 1997] Günter Rubik, Parallele Koordinaten. Created at June 27, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/paracord.htm.

*[CG2, 1997] Günter Rubik, Extruded Parallel Coordinates. Created at July 19, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/extruded.htm

*[Scienceblogs, 2008] Jörg Blumtritt, Parallelkoordinaten. Created at Feb 28, 2008. Retrieved at Nov 2, 2008. http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

*[Wikipedia] Retrieved at Nov 2, 2008. http://en.wikipedia.org/wiki/Parallel_coordinates

*[infovis.net] Juan C. Dürsteler, Visualisation in the 20th century. Retrieved at Nov 2, 2008. http://www.infovis.net/printMag.php?lang=2&num=112

*[vrvis.at] Helwig Hauser, Angular Brushing for Extended Parallel Coordinates. Retrieved at Nov 2, 2008. http://www.vrvis.at/via/research/ang-brush/index.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-03T11:00:54Z

UE-InfoVis0809 0126665:

Parallel coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data.

===Revealed Information===
When analyzing data, it is almost always the first step to get an overview and understand the data, its properties, distribution and pitfalls. In order to achieve this, usually the data is visualized. By visualizing multidimensional datasets with common coordinate systems human cognition is limited to few dimensions [IEEE, 1990]. The fourth dimension in 3d-diagramms can be visualized by using different colors, point-sizes or shapes. But this technique does not always point out the features you are interested in. 
Parallel coordinates enable displaying multi dimensional datasets in coordinate systems by placing each dimension on its own axis and arranging these axes parallel to each other. Each point is put on the accordant axis; lines are drawn to connect related data points [Scienceblogs, 2008]. 
Figure1 shows the following three points with four dimensions in a parallel coordinate system: 
P1(20,28,80,20) 
P2(30,39,35,31) 
P3(47,48,46,44) 

[[Image:Pc_figure1.gif|right|200px|thumb|Figure1: Parallel Coordinates]]

As this figure demonstrates distinctive features can be easily found using this technique.
In this way, distinctive features and the distribution of the data can be explored. One can see immediately, that the values of P1 are somehow diverse. With the help of hiliting or linking and brushing also the data can be explored in more detail.

===Example===
An example of Parallel Coordinates can be found when evaluating automobiles: the different axis are the horsepower, acceleration, the weight, cylinders etc. The lines crossing the axes represent the values corresponding to a particular car model. In this figure cars with six cylinders were emphasized through brushing, histograms are laid over axes, and one data-point is shown with all details. [infovis.net], [vrvis.at].
[[Image:Pc_example_car.gif|right|200px|thumb|Example "Figure2: Car model".]]

===Extruded Parallel Coordinates===
An extension of Parallel Coordinates are Extruded Parallel Coordinates. For each dataset a new set of axis is created and shifted along the z-axis. In interactive environments a clearly overview can be given by drawing planes between polylines of two different datasets [CG2, 1997].
[[Image:Pc_extrude.gif|right|200px|thumb|Figure3: Extruded Parallel Coordinates.]]

== References ==
:[IEEE, 1990] A. Inselberg, B. Dimsdale, Parallel coordinates: a tool for visualizing multi-dimensionalgeometry. Published at Oct 23-26, 1990. Retrieved at Nov 2, 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=146402

:[CG1, 1997] Günter Rubik, Parallele Koordinaten. Created at June 27, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/paracord.htm.

:[CG2, 1997] Günter Rubik, Extruded Parallel Coordinates. Created at July 19, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/extruded.htm

:[Scienceblogs, 2008] Jörg Blumtritt, Parallelkoordinaten. Created at Feb 28, 2008. Retrieved at Nov 2, 2008. http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

:[Wikipedia] Retrieved at Nov 2, 2008. http://en.wikipedia.org/wiki/Parallel_coordinates

:[infovis.net] Juan C. Dürsteler, Visualisation in the 20th century. Retrieved at Nov 2, 2008. http://www.infovis.net/printMag.php?lang=2&num=112

:[vrvis.at] Helwig Hauser, Angular Brushing for Extended Parallel Coordinates. Retrieved at Nov 2, 2008. http://www.vrvis.at/via/research/ang-brush/index.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-03T10:59:23Z

UE-InfoVis0809 0126665:

Parallel coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data.

===Revealed Information===
When analyzing data, it is almost always the first step to get an overview and understand the data, its properties, distribution and pitfalls. In order to achieve this, usually the data is visualized. When visualizing multidimensional datasets with common coordinate systems human cognition is limited to few dimensions [IEEE, 1990]. The fourth dimension in 3d-diagramms can be visualized by using different colors, point-sizes or shapes. But this technique does not always point out the features you are interested in. 
Parallel coordinates enable displaying multi dimensional datasets in coordinate systems by placing each dimension on its own axis and arranging these axes parallel to each other. Each point is put on the accordant axis; lines are drawn to connect related data points [Scienceblogs, 2008]. 
Figure1 shows the following three points with four dimensions in a parallel coordinate system: 
P1(20,28,80,20) 
P2(30,39,35,31) 
P3(47,48,46,44) 

[[Image:Pc_figure1.gif|right|200px|thumb|Figure1: Parallel Coordinates]]

As this figure demonstrates distinctive features can be easily found using this technique.
In this way, distinctive features and the distribution of the data can be explored. One can see immediately, that the values of P1 are somehow diverse. With the help of hiliting or linking and brushing also the data can be explored in more detail.

===Example===
An example of Parallel Coordinates can be found when evaluating automobiles: the different axis are the horsepower, acceleration, the weight, cylinders etc. The lines crossing the axes represent the values corresponding to a particular car model. In this figure cars with six cylinders were emphasized through brushing, histograms are laid over axes, and one data-point is shown with all details. [infovis.net], [vrvis.at].
[[Image:Pc_example_car.gif|right|200px|thumb|Example "Figure2: Car model".]]

===Extruded Parallel Coordinates===
An extension of Parallel Coordinates are Extruded Parallel Coordinates. For each dataset a new set of axis is created and shifted along the z-axis. In interactive environments a clearly overview can be given by drawing planes between polylines of two different datasets [CG2, 1997].
[[Image:Pc_extrude.gif|right|200px|thumb|Figure3: Extruded Parallel Coordinates.]]

== References ==
:[IEEE, 1990] A. Inselberg, B. Dimsdale, Parallel coordinates: a tool for visualizing multi-dimensionalgeometry. Published at Oct 23-26, 1990. Retrieved at Nov 2, 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=146402

:[CG1, 1997] Günter Rubik, Parallele Koordinaten. Created at June 27, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/paracord.htm.

:[CG2, 1997] Günter Rubik, Extruded Parallel Coordinates. Created at July 19, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/extruded.htm

:[Scienceblogs, 2008] Jörg Blumtritt, Parallelkoordinaten. Created at Feb 28, 2008. Retrieved at Nov 2, 2008. http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

:[Wikipedia] Retrieved at Nov 2, 2008. http://en.wikipedia.org/wiki/Parallel_coordinates

:[infovis.net] Juan C. Dürsteler, Visualisation in the 20th century. Retrieved at Nov 2, 2008. http://www.infovis.net/printMag.php?lang=2&num=112

:[vrvis.at] Helwig Hauser, Angular Brushing for Extended Parallel Coordinates. Retrieved at Nov 2, 2008. http://www.vrvis.at/via/research/ang-brush/index.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-03T10:53:46Z

UE-InfoVis0809 0126665:

Parallel coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data.

===Revealed Information===
When visualizing multidimensional datasets with common coordinate systems human cognition is limited to few dimensions [IEEE, 1990]. The fourth dimension in 3d-diagramms can be visualized by using different colors, point-sizes or shapes. But this technique does not always point out the features you are interested in. 
Parallel coordinates enable displaying multi dimensional datasets in coordinate systems by placing each dimension on its own axis and arranging these axes parallel to each other. Each point is put on the accordant axis; lines are drawn to connect related data points [Scienceblogs, 2008]. 
Figure1 shows the following three points with four dimensions in a parallel coordinate system: 
P1(20,28,80,20) 
P2(30,39,35,31) 
P3(47,48,46,44) 

[[Image:Pc_figure1.gif|right|200px|thumb|Figure1: Parallel Coordinates]]

As this figure demonstrates distinctive features can be easily found using this technique.
In this way, distinctive features and the distribution of the data can be explored. One can see immediately, that the values of P1 are somehow diverse. With the help of hiliting or linking and brushing also the data can be explored in more detail.

===Example===
An example of Parallel Coordinates can be found when evaluating automobiles: the different axis are the horsepower, acceleration, the weight, cylinders etc. The lines crossing the axes represent the values corresponding to a particular car model. In this figure cars with six cylinders were emphasized through brushing, histograms are laid over axes, and one data-point is shown with all details. [infovis.net], [vrvis.at].
[[Image:Pc_example_car.gif|right|200px|thumb|Example "Figure2: Car model".]]

===Extruded Parallel Coordinates===
An extension of Parallel Coordinates are Extruded Parallel Coordinates. For each dataset a new set of axis is created and shifted along the z-axis. In interactive environments a clearly overview can be given by drawing planes between polylines of two different datasets [CG2, 1997].
[[Image:Pc_extrude.gif|right|200px|thumb|Figure3: Extruded Parallel Coordinates.]]

== References ==
:[IEEE, 1990] A. Inselberg, B. Dimsdale, Parallel coordinates: a tool for visualizing multi-dimensionalgeometry. Published at Oct 23-26, 1990. Retrieved at Nov 2, 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=146402

:[CG1, 1997] Günter Rubik, Parallele Koordinaten. Created at June 27, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/paracord.htm.

:[CG2, 1997] Günter Rubik, Extruded Parallel Coordinates. Created at July 19, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/extruded.htm

:[Scienceblogs, 2008] Jörg Blumtritt, Parallelkoordinaten. Created at Feb 28, 2008. Retrieved at Nov 2, 2008. http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

:[Wikipedia] Retrieved at Nov 2, 2008. http://en.wikipedia.org/wiki/Parallel_coordinates

:[infovis.net] Juan C. Dürsteler, Visualisation in the 20th century. Retrieved at Nov 2, 2008. http://www.infovis.net/printMag.php?lang=2&num=112

:[vrvis.at] Helwig Hauser, Angular Brushing for Extended Parallel Coordinates. Retrieved at Nov 2, 2008. http://www.vrvis.at/via/research/ang-brush/index.html

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-03T10:53:22Z

UE-InfoVis0809 0126665:

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-03T10:49:30Z

UE-InfoVis0809 0126665:

Parallel coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data.

===Revealed Information===
When visualizing multidimensional datasets with common coordinate systems human cognition is limited to few dimensions [IEEE, 1990]. The fourth dimension in 3d-diagramms can be visualized by using different colors, point-sizes or shapes. But this technique does not always point out the features you are interested in. 
Parallel coordinates enable displaying multi dimensional datasets in coordinate systems by placing each dimension on its own axis and arranging these axes parallel to each other. Each point is put on the accordant axis; lines are drawn to connect related data points [Scienceblogs, 2008]. 
Figure1 shows the following three points with four dimensions in a parallel coordinate system: 
P1(20,28,80,20) 
P2(30,39,35,31) 
P3(47,48,46,44) 

[[Image:Pc_figure1.gif|right|200px|thumb|Figure1: Parallel Coordinates]]

As this figure demonstrates distinctive features can be easily found using this technique.

===Example===
An example of Parallel Coordinates can be found when evaluating automobiles: the different axis are the horsepower, acceleration, the weight, cylinders etc. The lines crossing the axes represent the values corresponding to a particular car model. In this figure cars with six cylinders were emphasized through brushing, histograms are laid over axes, and one data-point is shown with all details. [infovis.net], [vrvis.at].
[[Image:Pc_example_car.gif|right|200px|thumb|Example "Figure2: Car model".]]

===Extruded Parallel Coordinates===
An extension of Parallel Coordinates are Extruded Parallel Coordinates. For each dataset a new set of axis is created and shifted along the z-axis. In interactive environments a clearly overview can be given by drawing planes between polylines of two different datasets [CG2, 1997].
[[Image:Pc_extrude.gif|right|200px|thumb|Figure3: Extruded Parallel Coordinates.]]

== References ==
:[IEEE, 1990] A. Inselberg, B. Dimsdale, Parallel coordinates: a tool for visualizing multi-dimensionalgeometry. Published at Oct 23-26, 1990. Retrieved at Nov 2, 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=146402

:[CG1, 1997] Günter Rubik, Parallele Koordinaten. Created at June 27, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/paracord.htm.

:[CG2, 1997] Günter Rubik, Extruded Parallel Coordinates. Created at July 19, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/extruded.htm

:[Scienceblogs, 2008] Jörg Blumtritt, Parallelkoordinaten. Created at Feb 28, 2008. Retrieved at Nov 2, 2008. http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

:[Wikipedia] Retrieved at Nov 2, 2008. http://en.wikipedia.org/wiki/Parallel_coordinates

:[infovis.net] Juan C. Dürsteler, Visualisation in the 20th century. Retrieved at Nov 2, 2008. http://www.infovis.net/printMag.php?lang=2&num=112

:[vrvis.at] Helwig Hauser, Angular Brushing for Extended Parallel Coordinates. Retrieved at Nov 2, 2008. http://www.vrvis.at/via/research/ang-brush/index.html

* [http://www.cg.tuwien.ac.at/~rubik/paracord.htm Parallel Coordinates]
* [http://www.cg.tuwien.ac.at/~rubik/extruded.htm Extruded Parallel Coordinates]
* [http://en.wikipedia.org/wiki/Parallel_coordinates Wikipedia Prallel Coordinates]
http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-03T10:38:14Z

UE-InfoVis0809 0126665:

Parallel coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data.

===Revealed Information===
When visualizing multidimensional datasets with common coordinate systems human cognition is limited to few dimensions [IEEE, 1990]. The fourth dimension in 3d-diagramms can be visualized by using different colors, point-sizes or shapes. But this technique does not always point out the features you are interested in. 
Parallel coordinates enable displaying multi dimensional datasets in coordinate systems by placing each dimension on its own axis and arranging these axes parallel to each other. Each point is put on the accordant axis; lines are drawn to connect related data points [Scienceblogs, 2008].
The figure to the left shows three points with four dimensions in a parallel coordinate system. [Scienceblogs, 2008]
[[Image:Pc_figure1.gif|right|200px|thumb|Figure1.]]

As this figure demonstrates distinctive features can be easily found using this technique.

===Example===
Another example of usage of this kind of diagrams is when evaluating automobiles: the different axis could be the horsepower, acceleration, the weight, etc. The lines crossing the axes represent the values corresponding to a particular car model [infovis.net], [vrvis.at].
[[Image:Pc_example_car.gif|right|200px|thumb|Example.]]

===Extruded Parallel Coordinates===
An extension of Parallel Coordinates are Extruded Parallel Coordinates. For each dataset a new set of axis is created and shifted along the z-axis. In interactive environments a clearly overview can be given by drawing planes between polylines of two different datasets [CG2, 1997].
[[Image:Pc_extrude.gif|right|200px|thumb|Figure1.]]

== References ==
:[IEEE, 1990] A. Inselberg, B. Dimsdale, Parallel coordinates: a tool for visualizing multi-dimensionalgeometry. Published at Oct 23-26, 1990. Retrieved at Nov 2, 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=146402

:[CG1, 1997] Günter Rubik, Parallele Koordinaten. Created at June 27, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/paracord.htm.

:[CG2, 1997] Günter Rubik, Extruded Parallel Coordinates. Created at July 19, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/extruded.htm

:[Scienceblogs, 2008] Jörg Blumtritt, Parallelkoordinaten. Created at Feb 28, 2008. Retrieved at Nov 2, 2008. http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

:[Wikipedia] Retrieved at Nov 2, 2008. http://en.wikipedia.org/wiki/Parallel_coordinates

:[infovis.net] Juan C. Dürsteler, Visualisation in the 20th century. Retrieved at Nov 2, 2008. http://www.infovis.net/printMag.php?lang=2&num=112

:[vrvis.at] Helwig Hauser, Angular Brushing for Extended Parallel Coordinates. Retrieved at Nov 2, 2008. http://www.vrvis.at/via/research/ang-brush/index.html

* [http://www.cg.tuwien.ac.at/~rubik/paracord.htm Parallel Coordinates]
* [http://www.cg.tuwien.ac.at/~rubik/extruded.htm Extruded Parallel Coordinates]
* [http://en.wikipedia.org/wiki/Parallel_coordinates Wikipedia Prallel Coordinates]
http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

File:Pc extrude.gif

2008-11-03T10:35:50Z

UE-InfoVis0809 0126665: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

File:Pc figure1.gif

2008-11-03T10:34:14Z

UE-InfoVis0809 0126665: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

File:Pc example car.gif

2008-11-03T10:30:35Z

UE-InfoVis0809 0126665: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-11-03T10:26:35Z

UE-InfoVis0809 0126665:

Parallel coordinates is a common way of visualizing high-dimensional geometry and analyzing multivariate data.

===Revealed Information===
When visualizing multidimensional datasets with common coordinate systems human cognition is limited to few dimensions [IEEE, 1990]. The fourth dimension in 3d-diagramms can be visualized by using different colors, point-sizes or shapes. But this technique does not always point out the features you are interested in.
Parallel coordinates enable displaying multi dimensional datasets in coordinate systems by placing each dimension on its own axis and arranging these axes parallel to each other. Each point is put on the accordant axis; lines are drawn to connect related data points [Scienceblogs, 2008].
The figure to the left shows three points with four dimensions in a parallel coordinate system. [Scienceblogs, 2008]

As this figure demonstrates distinctive features can be easily found using this technique.

===Example===
Another example of usage of this kind of diagrams is when evaluating automobiles: the different axis could be the horsepower, acceleration, the weight, etc. The lines crossing the axes represent the values corresponding to a particular car model [infovis.net], [vrvis.at].

===Extruded Parallel Coordinates===
An extension of Parallel Coordinates are Extruded Parallel Coordinates. For each dataset a new set of axis is created and shifted along the z-axis. In interactive environments a clearly overview can be given by drawing planes between polylines of two different datasets [CG2, 1997].

== References ==
:[IEEE, 1990] A. Inselberg, B. Dimsdale, Parallel coordinates: a tool for visualizing multi-dimensionalgeometry. Published at Oct 23-26, 1990. Retrieved at Nov 2, 2008. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=146402

:[CG1, 1997] Günter Rubik, Parallele Koordinaten. Created at June 27, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/paracord.htm.

:[CG2, 1997] Günter Rubik, Extruded Parallel Coordinates. Created at July 19, 1997. Retrieved at Nov 2, 2008. http://www.cg.tuwien.ac.at/~rubik/extruded.htm

:[Scienceblogs, 2008] Jörg Blumtritt, Parallelkoordinaten. Created at Feb 28, 2008. Retrieved at Nov 2, 2008. http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

:[Wikipedia] Retrieved at Nov 2, 2008. http://en.wikipedia.org/wiki/Parallel_coordinates

:[infovis.net] Juan C. Dürsteler, Visualisation in the 20th century. Retrieved at Nov 2, 2008. http://www.infovis.net/printMag.php?lang=2&num=112

:[vrvis.at] Helwig Hauser, Angular Brushing for Extended Parallel Coordinates. Retrieved at Nov 2, 2008. http://www.vrvis.at/via/research/ang-brush/index.html

* [http://www.cg.tuwien.ac.at/~rubik/paracord.htm Parallel Coordinates]
* [http://www.cg.tuwien.ac.at/~rubik/extruded.htm Extruded Parallel Coordinates]
* [http://en.wikipedia.org/wiki/Parallel_coordinates Wikipedia Prallel Coordinates]
http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1 - Parallel Coordinates

2008-10-30T00:15:28Z

UE-InfoVis0809 0126665: New page: * [http://www.cg.tuwien.ac.at/~rubik/paracord.htm Parallel Coordinates] * [http://www.cg.tuwien.ac.at/~rubik/extruded.htm Extruded Parallel Coordinates] * [http://en.wikipedia.org/wiki/Par...

* [http://www.cg.tuwien.ac.at/~rubik/paracord.htm Parallel Coordinates]
* [http://www.cg.tuwien.ac.at/~rubik/extruded.htm Extruded Parallel Coordinates]
* [http://en.wikipedia.org/wiki/Parallel_coordinates Wikipedia Prallel Coordinates]
http://www.scienceblogs.de/zahlenbilder/2008/02/parallelkoordinaten.php

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02

2008-10-29T23:13:58Z

UE-InfoVis0809 0126665:

== Gruppenmitglieder ==

*[[User:UE-InfoVis0809 0126665|Beranek, Karl]]
*[[User:UE-InfoVis0809 9607701|Goldmann, Axel]]
*[[User:UE-InfoVis0809 0409282|Schiffl, Katharina]]

== Aufgaben ==

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 0|Aufgabe 0]]
*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1|Aufgabe 1]]
*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 2|Aufgabe 2]]
*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 3|Aufgabe 3]]
*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 4|Aufgabe 4]]

== Nützliche Links ==

*[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgaben.html InfoVis Aufgabenstellungen]
* [http://meta.wikimedia.org/ Meta-Wiki] Doku Wiki über Wikipedia und MediaWiki
* Hauptnachschlagewerk zur [http://meta.wikimedia.org/wiki/Help:Editing Wiki Syntax]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02

2008-10-29T23:12:44Z

UE-InfoVis0809 0126665:

== Gruppenmitglieder ==

*[[User:UE-InfoVis0809 0126665|Beranek, Karl]]
*[[User:UE-InfoVis0809 9607701|Goldmann, Axel]]
*[[User:UE-InfoVis0809 0409282|Schiffl, Katharina]]

== Aufgaben ==

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 0|Aufgabe 0]]
*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1|Aufgabe 1]]
*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 2|Aufgabe 2]]
*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 3|Aufgabe 3]]
*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 4|Aufgabe 4]]

== Nützliche Links ==

*[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe.html InfoVis Aufgabenstellungen]
* [http://meta.wikimedia.org/ Meta-Wiki] Doku Wiki über Wikipedia und MediaWiki
* Hauptnachschlagewerk zur [http://meta.wikimedia.org/wiki/Help:Editing Wiki Syntax]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02

2008-10-29T23:08:40Z

UE-InfoVis0809 0126665:

== Gruppenmitglieder ==

*[[User:UE-InfoVis0809 0126665|Beranek, Karl]]
*[[User:UE-InfoVis0809 9607701|Goldmann, Axel]]
*[[User:UE-InfoVis0809 0409282|Schiffl, Katharina]]

== Aufgaben ==

*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 0|Aufgabe 0]]
*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1|Aufgabe 1]]
*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 2|Aufgabe 2]]
*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 3|Aufgabe 3]]
*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 4|Aufgabe 4]]

== Nützliche Links ==

* [http://meta.wikimedia.org/ Meta-Wiki] Doku Wiki über Wikipedia und MediaWiki
* Hauptnachschlagewerk zur [http://meta.wikimedia.org/wiki/Help:Editing Wiki Syntax]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02

2008-10-16T07:47:24Z

UE-InfoVis0809 0126665:

== Gruppenmitglieder ==

*[[User:UE-InfoVis0809 0126665|Beranek, Karl]]
*[[User:UE-InfoVis0809 9607701|Goldmann, Axel]]
*[[User:UE-InfoVis0809 0409282|Schiffl, Katharina]]

== Aufgaben ==

*[http://ieg.ifs.tuwien.ac.at/~gschwand/teaching/infovis_ue_ws08/infovis_ue_aufgabe0.html Aufgabe 0]
*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 1|Aufgabe 1]]
*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 2|Aufgabe 2]]
*[[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02 - Aufgabe 3|Aufgabe 3]]

== Nützliche Links ==

* [http://meta.wikimedia.org/ Meta-Wiki] Doku Wiki über Wikipedia und MediaWiki
* Hauptnachschlagewerk zur [http://meta.wikimedia.org/wiki/Help:Editing Wiki Syntax]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02

2008-10-16T07:37:40Z

UE-InfoVis0809 0126665:

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02

2008-10-16T07:36:32Z

UE-InfoVis0809 0126665: New page: == Gruppenmitglieder == *Beranek, Karl *Goldmann, Axel *Schiffl, Katharina == Aufgaben ...

User:UE-InfoVis0809 0126665

2008-10-15T23:13:51Z

UE-InfoVis0809 0126665:

'''Karl Beranek ''' (* [[18. April]] [[1981]] in [[Wien]], [[Österreich]])

[[Image:BeranekFoto.JPG|right|150px|thumb|"Me?!"]]
 
* email: beranek.karl (at) gmail.com
* Studium: [[TU-Wien]] - Knowledge Management
* MatrNr: 0126665
* StudKennz: 066 933
 
* InfoVis-Gruppe: [[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 02|Gruppe 02]] ([[User:UE-InfoVis0809 0126665|Beranek]], [[User:UE-InfoVis0809 9607701|Goldmann]], [[User:UE-InfoVis0809 0409282|Schiffl]])

User:UE-InfoVis0809 0126665

2008-10-15T23:00:41Z

UE-InfoVis0809 0126665:

'''Karl Beranek ''' (* [[18. April]] [[1981]] in [[Wien]], [[Österreich]])

[[Image:BeranekFoto.JPG|right|150px|thumb|"Me?!"]]
 
* email: beranek.karl@gmail.com
* Studium: [[TU-Wien]] - Knowledge Management
* MatrNr: 0126665
* StudKennz: 066 933

[[Category:Persons]]

User:UE-InfoVis0809 0126665

2008-10-15T22:43:04Z

UE-InfoVis0809 0126665:

'''Karl Beranek ''' (* [[18. April]] [[1981]] in [[Wien]], [[Österreich]])

[[Image:BeranekFoto.jpg|right]]

* email: beranek.karl@gmail.com
* Studium: [[TU-Wien]] - Knowledge Management
* MatrNr: 0126665
* StudKennz: 066 933

File:BeranekFoto.JPG

2008-10-15T22:42:09Z

UE-InfoVis0809 0126665: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

File:Beranek foto.JPG

2008-10-15T22:29:16Z

UE-InfoVis0809 0126665: New page: == Summary == == Copyright status == == Source ==

== Summary ==

== Copyright status ==

== Source ==

User:UE-InfoVis0809 0126665

2008-10-15T22:09:46Z

UE-InfoVis0809 0126665:

'''Karl Beranek ''' (* [[18. April]] [[1981]] in [[Wien]], [[Österreich]])
* email: beranek.karl@gmail.com
* Studium: [[TU-Wien]] - Knowledge Management
* MatrNr: 0126665
* StudKennz: 066 933

User:UE-InfoVis0809 0126665

2008-10-15T22:06:56Z

UE-InfoVis0809 0126665: New page: == Karl Beranek == '''Karl Beranek ''' (* 18. April 1981 in Wien, Österreich) * email: beranek.karl@gmail.com * Studium: TU-Wien - Knowledge Management * MatrNr: 0126...

== Karl Beranek ==

'''Karl Beranek ''' (* [[18. April]] [[1981]] in [[Wien]], [[Österreich]])
* email: beranek.karl@gmail.com
* Studium: [[TU-Wien]] - Knowledge Management
* MatrNr: 0126665
* StudKennz: 066 933