InfoVis:Wiki - User contributions [en]

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 06 - Aufgabe 4

2009-01-07T23:15:07Z

UE-InfoVis0809 9730468:

== 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.

== 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 06|Gruppe 06]]

== General description and background ==

=== Field of application ===
The visualization will be used by experts for medical and psychological analysis. The visualization shall provide information of a single patient, and give details about their consumption of several different drinks (water, milk, juice, coke, coffee, and beer), and the thus resulting consumption of sugar, caffeine and alcohol. It shall be possible to visualize both a time-line overview of these consumptions, as well as detailed views for specific periods in time. Additionally, certain periods of life, such as childhood or puberty, and specific events and happenings such as unemployment, wedding(s), or birth of the children, shall be indicated.

=== Analysis of the data set ===

The application displays ''abstract'', ''multi-dimensional'', ''time-oriented'' information:

* '''Drinks''': Consumption of six different drinks (water, milk, juice, coke, coffee, and beer) and sugar cubes by day.
** These dimensions have a ''continuous'' data type.
* '''Ingredients''': The amount of dangerous substances (sugar, caffeine, and alcohol) contained in the drinks and in the lump sugar.
** These dimensions have a ''continuous'' data type.
** These variables are ''derived'' from the drinks and sugar cubes.
* '''Events''': Certain periods of life and specific events (e.g., childhood, unemployment).
** This dimension is of a ''nominal'' data type; they can be distinguished, but they cannot be ordered, as they might be overlapping.

The following table gives a detailed overview on each datum:

{|cellpadding="2" border="1"
! Dimension !! Data type !! Unit
|-
| water || continuous || ml
|-
| milk || continuous || ml
|-
| juice || continuous || ml
|-
| coke || continuous || ml
|-
| coffee || continuous || ml
|-
| beer || continuous || ml
|-
| lump sugar || discrete || pieces
|-
| sugar || continuous || g
|-
| caffeine || continuous || g
|-
| alcohol || continuous || g
|-
| events || nominal || - |
|}

==== Temporal Dimension ====

We can characterize the temporal dimension as follows [Aigner, 2006, pp. 13-17]:

* The application's time domain is ''branching'', because it allows the description of alternative scenarios.
* The time scale is ''discrete'' and days are the smallest granularity used.
* Drinks and ingredients are ''totally ordered'', whereas events are ''partially ordered'', because they might be overlapping.
* The temporal primitives used for drinks and ingredients are ''instants'', whereas ''intervals'' are used for events.
* The application uses the ''Gregorian calendar'' and different temporal granularities like months and year will be made available to the users.

==== Details and Examples ====
The ingredients of the drinks, and their recommended maximum dose per day, are as follows:

{| border="1"
! !! Sugar !! Caffeine !! Alcohol
|-
! Water
| || ||
|-
! Milk
| || ||
|-
! Juice (100ml)
| align="right" | 10g || ||
|-
! Coffee (100ml)
| || align="right" | 80mg ||
|-
! Lump sugar (per piece)
| align="right" | 3g || ||
|-
! Coke (100ml)
| align="right" | 10g || align="right" | 10mg ||
|-
! Beer (100ml)
| align="right" | || || align="right" | 3.6g
|-
|-cellpadding="2"

! Daily dose
| align="right" | 50g || align="right" | 600mg || align="right" | 20g
|}

For the events and periods in life, the data would look like:

{| border="1"
! Start !! End !! Type !! remarks
|-
| 13.4.1965 || 12.4.1972 || Childhood ||
|-
| 7.7.1977 || 7.7.1977 || Wedding ||
|-
| 25.7.1980 || 25.7.1980 || Birth || Bart
|-
| 1.8.1981 || 31.10.1982 || Unemployment ||
|-
| 12.10.1982 || 12.10.1982 || Birth || Lisa
|-
|}

An exemplary data set for the consumption would look as follows:

{| border="1"
! Date !! Water !! Milk !! Juice !! Coffee !! Lump sugar !! Coke !! Beer
|-
| 7.7. 1977 || 0.25 || 0.5 || 0 || 0.75 || 5 || 1 || 5.5
|-
| 25.7. 1980 || 0 || 0 || 0.25 || 1.25 || 12 || 0 || 0
|-
| 1.1. 1993 || 1.0 || 0 || 0.25 || 0.25 || 2 || 0.33 || 1.5
|-
|}

=== Analysis of the target group ===
The intended target group of the visualization are physicians, psychologists and nutritionist. Thanks to their education they can interpret of the data and thus do primary need hints what different values imply but require guidance to navigate through the data and conceive it.

On the other hand it cannot be assumed that the users have technical knowledge of interactive systems or are willing to acquire it, therefore the visualization must be intuitive and as far as possible self descriptive.

Furthermore possible limitations by the target hardware must be taken into account. This could also limit the possibilities of interaction (e.g. screen-size, no keyboard or no mouse but a touch-pad).

=== Goals of the visualization ===
The visualisation shall allow medical doctors to get a fast overview on excess consumption of certain drinks and the thus resulting consumption of substances, potentially to be able to link it with certain symptoms and diseases Homer might suffer from.

For psychologists, it might be more relevant to link the consumption to events, to find patterns in what circumstances Homer tends to have an excess consumption of the certain drinks. It might be especially interesting to spot excess consumption that cannot be linked with a certain event, for example too much beer consumption that isn't in relation to a celebration or a known phase of depression, like unemployment.

Generally, we assume the doctors to use these tools mainly during a physical examination, or maybe a ward round; we thus expect the doctors having a limited amount of time available for inspecting the data, thus they should be able to get a fast overview and fast zooming into specific time periods. Therefore, we want to provide for a simple navigation, and rather focus on displaying data in the context of the whole life span, rather than giving (too) detailed data or even numbers for a specific date.

== Concept ==

=== Visualization ===
==== Type of Visualization ====

Kosara and Miksch [2002] describe three types of applications for Information Visualization in medicine: visualizing measurements, displaying incidents, and planing the future. This application requires the visualization of quantitative values and events. Therefore it combines charts with a LifeLines inspired view of events [Plaisant et al., 1998].

The quantitative data set is composed of multiple instances of several measures taken periodically, i.e. a time series, which is best visualised as a line chart [Few, 2004].
We have several different aspects to visualize
* Consumption of drinks
* Consumption of substances/ingredients
* Individual consumption of caffeine, sugar and alcohol, indicating the source of the substance

The first two can be visualized with normal line charts, while for the last one, we opted for a stacked line chart.

==== Visual Mapping ====
The visual mapping is the same in all our visualisation variants, and rather simple: the time series is mapped to the horizontal axis [Few, 2004], while the vertical axis maps the values at the specific point in time. The scale of the vertical axis either measured in litres consumption per day, or as a percentage of the recommended daily dose. The recommended dose, which is at 100%, is specifically highlighted, to allow for easily spotting over-consumption.

LifeLines [Plaisant et al., 1998]

==== Techniques used ====
* Focus and context [Leung and Apperley, 1994]
* Mouse Actions: we employ actions like clicking and mouse-over.
* Time-Series [Few, 2004a]

* Beschreibung der verwendeten Techniken / angewandten Prinzipien
** inkl. Quellenangaben (siehe z.B. VO Skriptum/Foliensatz)

=== Mockup(s) ===
The imaginary data-set and the charts were produced using Open Office Calc, while the final mockups were created with a drawing-program.

[[Image:Ue08-simpson-overview.png]] 
Figure 1: Overview and standard visualisation, showing the consumption of drinks during the whole life of the patient.

[[Image:Ue08-simpson-detail.png]] 
Figure 2: Detailed view of a specific period of time, using the focus and context technique.

[[Image:Ue08-simpson-ingredients.png]] 
Figure 3: Overview, showing the consumption of substances.

[[Image:Ue08-simpson-caffeine.png]] 
Figure 4: Overview, showing the sources of caffeine consumed.

=== Interaction ===
As the application is meant for medical doctors and psychologists, we kept the interface simple, to avoid situations where the users might get lost in a specific view / selection and not finding their way back.

Still, we provide the following interaction modes
* Switching between different data to visualise: with the tabs on the top-right part of the tool, just above the time-line. This allows the user to select either the consumption of drinks, consumption of substances, or detailed consumption and sources for each of the substances caffeine, sugar and alcohol.
** These tabs are connected with each other: If the user zooms in a tab and then changes to another tab, the same timespan will be focussed.
* Zooming into a specific period of life: this can be initiated by clicking on a specific location of the time-line, and by using the (+) and (-) icons. Zooming uses the "focus and context" technique to always view the selected area in context with the data of the other stages of life of the patient [Leung and Apperley, 1994].
** By clicking on the LifeLine element, the user can move the focus of the visualisation on that time span
* Details on demand
* Switching future scenarios: with selecting one of the options from the drop-down box on the top-right, the user can select one of the future scenarios.

=== Discussion ===
* Wie werden die BenutzerInnen in ihren Aufgaben unterstützt?
* Welche Besonderheiten gibt es?
==== Advantages and drawbacks ====
* Our tool is simple, and clear in presentation, with only showing one chart per screen.
* We focus on showing only the most relevant visualisations of the data, while skipping e.g. summary charts for a specific period, or displaying numerical data. This reduces the needed interaction and number of available system states significantly.
* However, showing more charts per screen would allow the users to visualize several aspects of the data at once, and maybe get some insights that might be more difficult to achieve otherwise. However, clarity and the need for interaction would clearly raise.

=== Future Work ===
As future work on this prototype, we should carry out user studies to back up or change some of the design decisions, namely:
* A comparison of the currently employed "tabs" to change the visualized data with displaying all charts on one screen, with the possibility to collapse/expand each of them, should be carried out.
* Additionally, alternative user-interface elements than the tabs, e.g. radio-buttons, might be tested
* An evaluation of the "focus and context" method, possibly comparing it to other zooming techniques.

Further, it might be worth to provide the users with annotation tools, to support documentation of their own insights as well as facilitating communication and discussion on a specific patient with colleagues.

Finally, visual analytic methods should be utilised to help the users find patterns in the data.

== References ==
* [Aigner, 2006] Wolfgang Aigner. Visualization of Time and Time-Oriented Information: Challenges and Conceptual Design. Ph.D. thesis, Vienna University of Technology, 2006.
* [Few, 2004] Stephen Few. Show Me the Numbers - Designing Tables and Graphs to Enlighten. Analytics Press, Oakland, CA, 2004.
* [Kosara and Miksch, 2002] Robert Kosara and Silvia Miksch. Visualization Methods for Data Analysis and Planning in Medical Applications. ''International Journal of Medical Informatics'', 68(1-3):141-153, December 2002.
* [Leung and Apperley, 1994] Y.K. Leung and M.D. Apperley. A Review and Taxonomy of Distortion-Oriented Presentation Techniques. ''ACM Transactions on Computer-Human Interaction (TOCHI)'', 1(2):126-160, June 1994.
* [Plaisant et al., 1998] Catherine Plaisant, Richard Mushlin, Aaron Snyder, Jia Li, Dan Heller, and Ben Shneiderman. LifeLines: Using Visualization to Enhance Navigation and Analysis of Patient Records. Technical Report HCIL-98-08, University of Maryland, 1998.
* [Pownser and Tufte, 1994] Seth M. Powsner and Edward R. Tufte. Graphical Summary of Patient Status. ''The Lancet'', 344(8919):386-389, August 1994.

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 06 - Aufgabe 4

2009-01-07T23:01:09Z

UE-InfoVis0809 9730468:

== 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.

== 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 06|Gruppe 06]]

== General description and background ==

=== Field of application ===
The visualization will be used by experts for medical and psychological analysis. The visualization shall provide information of a single patient, and give details about their consumption of several different drinks (water, milk, juice, coke, coffee, and beer), and the thus resulting consumption of sugar, caffeine and alcohol. It shall be possible to visualize both a time-line overview of these consumptions, as well as detailed views for specific periods in time. Additionally, certain periods of life, such as childhood or puberty, and specific events and happenings such as unemployment, wedding(s), or birth of the children, shall be indicated.

=== Analysis of the data set ===

The application displays ''abstract'', ''multi-dimensional'', ''time-oriented'' information:

* '''Drinks''': Consumption of six different drinks (water, milk, juice, coke, coffee, and beer) and sugar cubes by day.
** These dimensions have a ''continuous'' data type.
* '''Ingredients''': The amount of dangerous substances (sugar, caffeine, and alcohol) contained in the drinks and in the lump sugar.
** These dimensions have a ''continuous'' data type.
** These variables are ''derived'' from the drinks and sugar cubes.
* '''Events''': Certain periods of life and specific events (e.g., childhood, unemployment).
** This dimension is of a ''nominal'' data type; they can be distinguished, but they cannot be ordered, as they might be overlapping.

The following table gives a detailed overview on each datum:

{|cellpadding="2" border="1"
! Dimension !! Data type !! Unit
|-
| water || continuous || ml
|-
| milk || continuous || ml
|-
| juice || continuous || ml
|-
| coke || continuous || ml
|-
| coffee || continuous || ml
|-
| beer || continuous || ml
|-
| lump sugar || discrete || pieces
|-
| sugar || continuous || g
|-
| caffeine || continuous || g
|-
| alcohol || continuous || g
|-
| events || nominal || - |
|}

==== Temporal Dimension ====

We can characterize the temporal dimension as follows [Aigner, 2006, pp. 13-17]:

* The application's time domain is ''branching'', because it allows the description of alternative scenarios.
* The time scale is ''discrete'' and days are the smallest granularity used.
* Drinks and ingredients are ''totally ordered'', whereas events are ''partially ordered'', because they might be overlapping.
* The temporal primitives used for drinks and ingredients are ''instants'', whereas ''intervals'' are used for events.
* The application uses the ''Gregorian calendar'' and different temporal granularities like months and year will be made available to the users.

The ingredients of the drinks, and their recommended maximum dose per day, are as follows:

{| border="1"
! !! Sugar !! Caffeine !! Alcohol
|-
! Water
| || ||
|-
! Milk
| || ||
|-
! Juice (100ml)
| align="right" | 10g || ||
|-
! Coffee (100ml)
| || align="right" | 80mg ||
|-
! Lump sugar (per piece)
| align="right" | 3g || ||
|-
! Coke (100ml)
| align="right" | 10g || align="right" | 10mg ||
|-
! Beer (100ml)
| align="right" | || || align="right" | 3.6g
|-
|-cellpadding="2"

! Daily dose
| align="right" | 50g || align="right" | 600mg || align="right" | 20g
|}

For the events and periods in life, the data would look like:

{| border="1"
! Start !! End !! Type !! remarks
|-
| 13.4.1965 || 12.4.1972 || Childhood ||
|-
| 7.7.1977 || 7.7.1977 || Wedding ||
|-
| 25.7.1980 || 25.7.1980 || Birth || Bart
|-
| 1.8.1981 || 31.10.1982 || Unemployment ||
|-
| 12.10.1982 || 12.10.1982 || Birth || Lisa
|-
|}

An exemplary data set for the consumption would look as follows:

{| border="1"
! Date !! Water !! Milk !! Juice !! Coffee !! Lump sugar !! Coke !! Beer
|-
| 7.7. 1977 || 0.25 || 0.5 || 0 || 0.75 || 5 || 1 || 5.5
|-
| 25.7. 1980 || 0 || 0 || 0.25 || 1.25 || 12 || 0 || 0
|-
| 1.1. 1993 || 1.0 || 0 || 0.25 || 0.25 || 2 || 0.33 || 1.5
|-
|}

=== Analysis of the target group ===
The intended target group of the visualization are physicians, psychologists and nutritionist. Thanks to their education they can interpret of the data and thus do primary need hints what different values imply but require guidance to navigate through the data and conceive it.

On the other hand it cannot be assumed that the users have technical knowledge of interactive systems or are willing to acquire it, therefore the visualization must be intuitive and as far as possible self descriptive.

Furthermore possible limitations by the target hardware must be taken into account. This could also limit the possibilities of interaction (e.g. screen-size, no keyboard or no mouse but a touch-pad).

=== Goals of the visualization ===
The visualisation shall allow medical doctors to get a fast overview on excess consumption of certain drinks and the thus resulting consumption of substances, potentially to be able to link it with certain symptoms and diseases Homer might suffer from.

For psychologists, it might be more relevant to link the consumption to events, to find patterns in what circumstances Homer tends to have an excess consumption of the certain drinks. It might be especially interesting to spot excess consumption that cannot be linked with a certain event, for example too much beer consumption that isn't in relation to a celebration or a known phase of depression, like unemployment.

Generally, we assume the doctors to use these tools mainly during a physical examination, or maybe a ward round; we thus expect the doctors having a limited amount of time available for inspecting the data, thus they should be able to get a fast overview and fast zooming into specific time periods. Therefore, we want to provide for a simple navigation, and rather focus on displaying data in the context of the whole life span, rather than giving (too) detailed data or even numbers for a specific date.

== Concept ==

=== Visualization ===
==== Type of Visualization ====
The data sets is composed of multiple instances of several measures taken periodically, i.e. a time series, which is best visualised as a line chart [Few, 2004].
We have several different aspects to visualize
* Consumption of drinks
* Consumption of substances/ingredients
* Individual consumption of caffeine, sugar and alcohol, indicating the source of the substance

The first two can be visualized with normal line charts, while for the last one, we opted for a stacked line chart.

Events -> LifeLines [Plaisant et al., 1998]

==== Visual Mapping ====
The visual mapping is the same in all our visualisation variants, and rather simple: the time series is mapped to the horizontal axis [Few, 2004], while the vertical axis maps the values at the specific point in time. The scale of the vertical axis either measured in litres consumption per day, or as a percentage of the recommended daily dose. The recommended dose, which is at 100%, is specifically highlighted, to allow for easily spotting over-consumption.

LifeLines [Plaisant et al., 1998]

==== Techniques used ====
* Focus and context [Leung and Apperley, 1994]
* Mouse Actions: we employ actions like clicking and mouse-over.
* Time-Series [Few, 2004a]

* Beschreibung der verwendeten Techniken / angewandten Prinzipien
** inkl. Quellenangaben (siehe z.B. VO Skriptum/Foliensatz)

=== Mockup(s) ===
The imaginary data-set and the charts were produced using Open Office Calc, while the final mockups were created with a drawing-program.

[[Image:Ue08-simpson-overview.png]] 
Figure 1: Overview and standard visualisation, showing the consumption of drinks during the whole life of the patient.

[[Image:Ue08-simpson-detail.png]] 
Figure 2: Detailed view of a specific period of time, using the focus and context technique.

[[Image:Ue08-simpson-ingredients.png]] 
Figure 3: Overview, showing the consumption of substances.

[[Image:Ue08-simpson-caffeine.png]] 
Figure 4: Overview, showing the sources of caffeine consumed.

=== Interaction ===
As the application is meant for medical doctors and psychologists, we kept the interface simple, to avoid situations where the users might get lost in a specific view / selection and not finding their way back.

Still, we provide the following interaction modes
* Switching between different data to visualise: with the tabs on the top-right part of the tool, just above the time-line. This allows the user to select either the consumption of drinks, consumption of substances, or detailed consumption and sources for each of the substances caffeine, sugar and alcohol.
** These tabs are connected with each other: If the user zooms in a tab and then changes to another tab, the same timespan will be focussed.
* Zooming into a specific period of life: this can be initiated by clicking on a specific location of the time-line, and by using the (+) and (-) icons. Zooming uses the "focus and context" technique to always view the selected area in context with the data of the other stages of life of the patient [Leung and Apperley, 1994].
** By clicking on the LifeLine element, the user can move the focus of the visualisation on that time span
* Details on demand
* Switching future scenarios: with selecting one of the options from the drop-down box on the top-right, the user can select one of the future scenarios.

=== Discussion ===
* Wie werden die BenutzerInnen in ihren Aufgaben unterstützt?
* Welche Besonderheiten gibt es?
==== Advantages and drawbacks ====
* Our tool is simple, and clear in presentation, with only showing one chart per screen.
* We focus on showing only the most relevant visualisations of the data, while skipping e.g. summary charts for a specific period, or displaying numerical data. This reduces the needed interaction and number of available system states significantly.
* However, showing more charts per screen would allow the users to visualize several aspects of the data at once, and maybe get some insights that might be more difficult to achieve otherwise. However, clarity and the need for interaction would clearly raise.

=== Future Work ===
As future work on this prototype, we should carry out user studies to back up or change some of the design decisions, namely:
* A comparison of the currently employed "tabs" to change the visualized data with displaying all charts on one screen, with the possibility to collapse/expand each of them, should be carried out.
* Additionally, alternative user-interface elements than the tabs, e.g. radio-buttons, might be tested
* An evaluation of the "focus and context" method, possibly comparing it to other zooming techniques.

Further, it might be worth to provide the users with annotation tools, to support documentation of their own insights as well as facilitating communication and discussion on a specific patient with colleagues.

Finally, visual analytic methods should be utilised to help the users find patterns in the data.

== References ==
* [Aigner, 2006] Wolfgang Aigner. Visualization of Time and Time-Oriented Information: Challenges and Conceptual Design. Ph.D. thesis, Vienna University of Technology, 2006.
* [Few, 2004] Stephen Few. Show Me the Numbers - Designing Tables and Graphs to Enlighten. Analytics Press, Oakland, CA, 2004.
* [Kosara and Miksch, 2002] Robert Kosara and Silvia Miksch. Visualization Methods for Data Analysis and Planning in Medical Applications. ''International Journal of Medical Informatics'', 68(1-3):141-153, December 2002.
* [Leung and Apperley, 1994] Y.K. Leung and M.D. Apperley. A Review and Taxonomy of Distortion-Oriented Presentation Techniques. ''ACM Transactions on Computer-Human Interaction (TOCHI)'', 1(2):126-160, June 1994.
* [Plaisant et al., 1998] Catherine Plaisant, Richard Mushlin, Aaron Snyder, Jia Li, Dan Heller, and Ben Shneiderman. LifeLines: Using Visualization to Enhance Navigation and Analysis of Patient Records. Technical Report HCIL-98-08, University of Maryland, 1998.
* [Pownser and Tufte, 1994] Seth M. Powsner and Edward R. Tufte. Graphical Summary of Patient Status. ''The Lancet'', 344(8919):386-389, August 1994.

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 06 - Aufgabe 4

2009-01-07T22:36:19Z

UE-InfoVis0809 9730468:

== 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.

== 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 06|Gruppe 06]]

== General description and background ==

=== Field of application ===
The visualization will be used by experts for medical and psychological analysis. The visualization shall provide information of a single patient, and give details about their consumption of several different drinks (water, milk, juice, coke, coffee, and beer), and the thus resulting consumption of sugar, caffeine and alcohol. It shall be possible to visualize both a time-line overview of these consumptions, as well as detailed views for specific periods in time. Additionally, certain periods of life, such as childhood or puberty, and specific events and happenings such as unemployment, wedding(s), or birth of the children, shall be indicated.

=== Analysis of the data set ===

The application displays ''abstract'', ''multi-dimensional'', ''time-oriented'' information:

* '''Drinks''': Consumption of six different drinks (water, milk, juice, coke, coffee, and beer) and sugar cubes by day.
** These dimensions have a ''continuous'' data type.
* '''Ingredients''': The amount of dangerous substances (sugar, caffeine, and alcohol) contained in the drinks and in the sugar cubes.
** These dimensions have a ''continuous'' data type.
** These variables are ''derived'' from the drinks and sugar cubes.
* '''Events''': Certain periods of life and specific events (e.g., childhood, unemployment).
** This dimension is of a ''nominal'' data type; they can be distinguished, but they cannot be ordered, as they might be overlapping.

The following table gives a detailed overview on each datum:

{|cellpadding="2" border="1"
! Dimension !! Data type !! Unit
|-
| water || continuous || ml
|-
| milk || continuous || ml
|-
| juice || continuous || ml
|-
| coke || continuous || ml
|-
| coffee || continuous || ml
|-
| beer || continuous || ml
|-
| sugar cube || discrete || pieces
|-
| sugar || continuous || g
|-
| caffeine || continuous || g
|-
| alcohol || continuous || g
|-
| events || nominal || - |
|}

==== Temporal Dimension ====

We can characterize the temporal dimension as follows [Aigner, 2006, pp. 13-17]:

* The application's time domain is ''branching'', because it allows the description of alternative scenarios.
* The time scale is ''discrete'' and days are the smallest granularity used.
* Drinks and ingredients are ''totally ordered'', whereas events are ''partially ordered'', because they might be overlapping.
* The temporal primitives used for drinks and ingredients are ''instants'', whereas ''intervals'' are used for events.
* The application uses the ''Gregorian calendar'' and different temporal granularities like months and year will be made available to the users.

The ingredients of the drinks, and their recommended maximum dose per day, are as follows:

{| border="1"
! !! Sugar !! Caffeine !! Alcohol
|-
! Water
| || ||
|-
! Milk
| || ||
|-
! Juice (100ml)
| align="right" | 10g || ||
|-
! Coffee (100ml)
| || align="right" | 80mg ||
|-
! Lump sugar (per piece)
| align="right" | 3g || ||
|-
! Coke (100ml)
| align="right" | 10g || align="right" | 10mg ||
|-
! Beer (100ml)
| align="right" | || || align="right" | 3.6g
|-
|-cellpadding="2"

! Daily dose
| align="right" | 50g || align="right" | 600mg || align="right" | 20g
|}

For the events and periods in life, the data would look like:

{| border="1"
! Start !! End !! Type !! remarks
|-
| 13.4.1965 || 12.4.1972 || Childhood ||
|-
| 7.7.1977 || 7.7.1977 || Wedding ||
|-
| 25.7.1980 || 25.7.1980 || Birth || Bart
|-
| 1.8.1981 || 31.10.1982 || Unemployment ||
|-
| 12.10.1982 || 12.10.1982 || Birth || Lisa
|-
|}

An exemplary data set for the consumption would look as follows:

{| border="1"
! Date !! Water !! Milk !! Juice !! Coffee !! Lump sugar !! Coke !! Beer
|-
| 7.7. 1977 || 0.25 || 0.5 || 0 || 0.75 || 5 || 1 || 5.5
|-
| 25.7. 1980 || 0 || 0 || 0.25 || 1.25 || 12 || 0 || 0
|-
| 1.1. 1993 || 1.0 || 0 || 0.25 || 0.25 || 2 || 0.33 || 1.5
|-
|}

=== Analysis of the target group ===
The intended target group of the visualization are physicians, psychologists and nutritionist. Thanks to their education they can interpret of the data and thus do primary need hints what different values imply but require guidance to navigate through the data and conceive it.

On the other hand it cannot be assumed that the users have technical knowledge of interactive systems or are willing to acquire it, therefore the visualization must be intuitive and as far as possible self descriptive.

Furthermore possible limitations by the target hardware must be taken into account. This could also limit the possibilities of interaction (e.g. screen-size, no keyboard or no mouse but a touch-pad).

=== Goals of the visualization ===
The visualisation shall allow medical doctors to get a fast overview on excess consumption of certain drinks and the thus resulting consumption of substances, potentially to be able to link it with certain symptoms and diseases Homer might suffer from.

For psychologists, it might be more relevant to link the consumption to events, to find patterns in what circumstances Homer tends to have an excess consumption of the certain drinks. It might be especially interesting to spot excess consumption that cannot be linked with a certain event, for example too much beer consumption that isn't in relation to a celebration or a known phase of depression, like unemployment.

Generally, we assume the doctors to use these tools mainly during a physical examination, or maybe a ward round; we thus expect the doctors having a limited amount of time available for inspecting the data, thus they should be able to get a fast overview and fast zooming into specific time periods. Therefore, we want to provide for a simple navigation, and rather focus on displaying data in the context of the whole life span, rather than giving (too) detailed data or even numbers for a specific date.

== Concept ==

=== Visualization ===
==== Type of Visualization ====
The data sets is composed of multiple instances of several measures taken periodically, i.e. a time series, which is best visualised as a line chart [Few, 2004].
We have several different aspects to visualize
* Consumption of drinks
* Consumption of substances/ingredients
* Individual consumption of caffeine, sugar and alcohol, indicating the source of the substance

The first two can be visualized with normal line charts, while for the last one, we opted for a stacked line chart.

Events -> LifeLines [Plaisant et al., 1998]

==== Visual Mapping ====
The visual mapping is the same in all our visualisation variants, and rather simple: the time series is mapped to the horizontal axis [Few, 2004], while the vertical axis maps the values at the specific point in time. The scale of the vertical axis either measured in litres consumption per day, or as a percentage of the recommended daily dose. The recommended dose, which is at 100%, is specifically highlighted, to allow for easily spotting over-consumption.

LifeLines [Plaisant et al., 1998]

==== Techniques used ====
* Focus and context
* Mouse Actions: we employ actions like clicking and mouse-over.
* Time-Series [Few, 2004a]

* Beschreibung der verwendeten Techniken / angewandten Prinzipien
** inkl. Quellenangaben (siehe z.B. VO Skriptum/Foliensatz)

=== Mockup(s) ===
The imaginary data-set and the charts were produced using Open Office Calc, while the final mockups were created with a drawing-program.

[[Image:Ue08-simpson-overview.png]] 
Figure 1: Overview and standard visualisation, showing the consumption of drinks during the whole life of the patient.

[[Image:Ue08-simpson-detail.png]] 
Figure 2: Detailed view of a specific period of time, using the focus and context technique.

[[Image:Ue08-simpson-ingredients.png]] 
Figure 3: Overview, showing the consumption of substances.

[[Image:Ue08-simpson-caffeine.png]] 
Figure 4: Overview, showing the sources of caffeine consumed.

=== Interaction ===
As the application is meant for medical doctors and psychologists, we kept the interface simple, to avoid situations where the users might get lost in a specific view / selection and not finding their way back.

Still, we provide the following interaction modes
* Switching between different data to visualise: with the tabs on the top-right part of the tool, just above the time-line. This allows the user to select either the consumption of drinks, consumption of substances, or detailed consumption and sources for each of the substances caffeine, sugar and alcohol.
** These tabs are connected with each other: If the user zooms in a tab and then changes to another tab, the same timespan will be focussed.
* Zooming into a specific period of life: this can be initiated by clicking on a specific location of the time-line, and by using the (+) and (-) icons. Zooming uses the "focus and context" technique to always view the selected area in context with the data of the other stages of life of the patient [Leung and Apperley, 1994].
** By clicking on the LifeLine element, the user can move the focus of the visualisation on that time span
* Details on demand
* Switching future scenarios: with selecting one of the options from the drop-down box on the top-right, the user can select one of the future scenarios.

=== Discussion ===

* Wie werden die BenutzerInnen in ihren Aufgaben unterstützt?
* Welche Besonderheiten gibt es?
* Was sind die Vor- und Nachteile der Technik?

=== Future Work ===

* Erweiterungs- bzw. Verbesserungsmöglichkeiten

== References ==
* [Aigner, 2006] Wolfgang Aigner. Visualization of Time and Time-Oriented Information: Challenges and Conceptual Design. Ph.D. thesis, Vienna University of Technology, 2006.
* [Few, 2004] Stephen Few. Show Me the Numbers - Designing Tables and Graphs to Enlighten. Analytics Press, Oakland, CA, 2004.
* [Kosara and Miksch, 2002] Robert Kosara and Silvia Miksch. Visualization Methods for Data Analysis and Planning in Medical Applications. ''International Journal of Medical Informatics'', 68(1-3):141-153, December 2002.
* [Leung and Apperley, 1994] Y.K. Leung and M.D. Apperley. A Review and Taxonomy of Distortion-Oriented Presentation Techniques. ''ACM Transactions on Computer-Human Interaction (TOCHI)'', 1(2):126-160, June 1994.
* [Plaisant et al., 1998] Catherine Plaisant, Richard Mushlin, Aaron Snyder, Jia Li, Dan Heller, and Ben Shneiderman. LifeLines: Using Visualization to Enhance Navigation and Analysis of Patient Records. Technical Report HCIL-98-08, University of Maryland, 1998.
* [Pownser and Tufte, 1994] Seth M. Powsner and Edward R. Tufte. Graphical Summary of Patient Status. ''The Lancet'', 344(8919):386-389, August 1994.

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 06 - Aufgabe 4

2009-01-07T22:31:22Z

UE-InfoVis0809 9730468:

== 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.

== 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 06|Gruppe 06]]

== General description and background ==

=== Field of application ===
The visualization will be used by experts for medical and psychological analysis. The visualization shall provide information of a single patient, and give details about their consumption of several different drinks (water, milk, juice, coke, coffee, and beer), and the thus resulting consumption of sugar, caffeine and alcohol. It shall be possible to visualize both a time-line overview of these consumptions, as well as detailed views for specific periods in time. Additionally, certain periods of life, such as childhood or puberty, and specific events and happenings such as unemployment, wedding(s), or birth of the children, shall be indicated.

=== Analysis of the data set ===

The application displays ''abstract'', ''multi-dimensional'', ''time-oriented'' information:

* '''Drinks''': Consumption of six different drinks (water, milk, juice, coke, coffee, and beer) and sugar cubes by day.
** These dimensions have a ''continuous'' data type.
* '''Ingredients''': The amount of dangerous substances (sugar, caffeine, and alcohol) contained in the drinks and in the sugar cubes.
** These dimensions have a ''continuous'' data type.
** These variables are ''derived'' from the drinks and sugar cubes.
* '''Events''': Certain periods of life and specific events (e.g., childhood, unemployment).
** This dimension is of a ''nominal'' data type; they can be distinguished, but they cannot be ordered, as they might be overlapping.

The following table gives a detailed overview on each datum:

{|cellpadding="2" border="1"
! Dimension !! Data type !! Unit
|-
| water || continuous || ml
|-
| milk || continuous || ml
|-
| juice || continuous || ml
|-
| coke || continuous || ml
|-
| coffee || continuous || ml
|-
| beer || continuous || ml
|-
| sugar cube || discrete || pieces
|-
| sugar || continuous || g
|-
| caffeine || continuous || g
|-
| alcohol || continuous || g
|-
| events || nominal || - |
|}

==== Temporal Dimension ====

We can characterize the temporal dimension as follows [Aigner, 2006, pp. 13-17]:

* The application's time domain is ''branching'', because it allows the description of alternative scenarios.
* The time scale is ''discrete'' and days are the smallest granularity used.
* Drinks and ingredients are ''totally ordered'', whereas events are ''partially ordered'', because they might be overlapping.
* The temporal primitives used for drinks and ingredients are ''instants'', whereas ''intervals'' are used for events.
* The application uses the ''Gregorian calendar'' and different temporal granularities like months and year will be made available to the users.

The ingredients of the drinks, and their recommended maximum dose per day, are as follows:

{| border="1"
! !! Sugar !! Caffeine !! Alcohol
|-
! Water
| || ||
|-
! Milk
| || ||
|-
! Juice (100ml)
| align="right" | 10g || ||
|-
! Coffee (100ml)
| || align="right" | 80mg ||
|-
! Lump sugar (per piece)
| align="right" | 3g || ||
|-
! Coke (100ml)
| align="right" | 10g || align="right" | 10mg ||
|-
! Beer (100ml)
| align="right" | || || align="right" | 3.6g
|-
|-cellpadding="2"

! Daily dose
| align="right" | 50g || align="right" | 600mg || align="right" | 20g
|}

For the events and periods in life, the data would look like:

{| border="1"
! Start !! End !! Type !! remarks
|-
| 13.4.1965 || 12.4.1972 || Childhood ||
|-
| 7.7.1977 || 7.7.1977 || Wedding ||
|-
| 25.7.1980 || 25.7.1980 || Birth || Bart
|-
| 1.8.1981 || 31.10.1982 || Unemployment ||
|-
| 12.10.1982 || 12.10.1982 || Birth || Lisa
|-
|}

An exemplary data set for the consumption would look as follows:

{| border="1"
! Date !! Water !! Milk !! Juice !! Coffee !! Lump sugar !! Coke !! Beer
|-
| 7.7. 1977 || 0.25 || 0.5 || 0 || 0.75 || 5 || 1 || 5.5
|-
| 25.7. 1980 || 0 || 0 || 0.25 || 1.25 || 12 || 0 || 0
|-
| 1.1. 1993 || 1.0 || 0 || 0.25 || 0.25 || 2 || 0.33 || 1.5
|-
|}

=== Analysis of the target group ===
The intended target group of the visualization are physicians, psychologists and nutritionist. Thanks to their education they can interpret of the data and thus do primary need hints what different values imply but require guidance to navigate through the data and conceive it.

On the other hand it cannot be assumed that the users have technical knowledge of interactive systems or are willing to acquire it, therefore the visualization must be intuitive and as far as possible self descriptive.

Furthermore possible limitations by the target hardware must be taken into account. This could also limit the possibilities of interaction (e.g. screen-size, no keyboard or no mouse but a touch-pad).

=== Goals of the visualization ===
The visualisation shall allow medical doctors to get a fast overview on excess consumption of certain drinks and the thus resulting consumption of substances, potentially to be able to link it with certain symptoms and diseases Homer might suffer from.

For psychologists, it might be more relevant to link the consumption to events, to find patterns in what circumstances Homer tends to have an excess consumption of the certain drinks. It might be especially interesting to spot excess consumption that cannot be linked with a certain event, for example too much beer consumption that isn't in relation to a celebration or a known phase of depression, like unemployment.

Generally, we assume the doctors to use these tools mainly during a physical examination, or maybe a ward round; we thus expect the doctors having a limited amount of time available for inspecting the data, thus they should be able to get a fast overview and fast zooming into specific time periods. Therefore, we want to provide for a simple navigation, and rather focus on displaying data in the context of the whole life span, rather than giving (too) detailed data or even numbers for a specific date.

== Concept ==

=== Visualization ===
==== Type of Visualization ====
The data sets is composed of multiple instances of several measures taken periodically, i.e. a time series, which is best visualised as a line chart [Few, 2004].
We have several different aspects to visualize
* Consumption of drinks
* Consumption of substances/ingredients
* Individual consumption of caffeine, sugar and alcohol, indicating the source of the substance

The first two cam be visualized with normal line charts, while for the last one, we opted for a stacked line chart.

Events -> LifeLines [Plaisant et al., 1998]

==== Visual Mapping ====
The visual mapping is the same in all our visualisation variants, and rather simple: the time series is mapped to the horizontal axis [Few, 2004], while the vertical axis maps the values at the specific point in time. The scale of the vertical axis either measured in litres consumption per day, or as a percentage of the recommended daily dose. The recommended dose, which is at 100%, is specifically highlighted, to allow for easily spotting over-consumption.

LifeLines [Plaisant et al., 1998]

==== Techniques used
* Focus and context
* Mouse Actions: we employ actions like clicking and mouse-over.
* Time-Series [Few, 2004a]

* Beschreibung der verwendeten Techniken / angewandten Prinzipien
** inkl. Quellenangaben (siehe z.B. VO Skriptum/Foliensatz)

=== Mockup(s) ===
The imaginary data-set and the charts were produced using Open Office Calc, while the final mockups were created with a drawing-program.

[[Image:Ue08-simpson-overview.png]] 
Figure 1: Overview and standard visualisation, showing the consumption of drinks during the whole life of the patient.

[[Image:Ue08-simpson-detail.png]] 
Figure 2: Detailed view of a specific period of time, using the focus and context technique.

[[Image:Ue08-simpson-ingredients.png]] 
Figure 3: Overview, showing the consumption of substances.

[[Image:Ue08-simpson-caffeine.png]] 
Figure 4: Overview, showing the sources of caffeine consumed.

=== Interaction ===
As the application is meant for medical doctors and psychologists, we kept the interface simple, to avoid situations where the users might get lost in a specific view / selection and not finding their way back.

Still, we provide the following interaction modes
* Switching between different data to visualise: with the tabs on the top-right part of the tool, just above the time-line. This allows the user to select either the consumption of drinks, consumption of substances, or detailed consumption and sources for each of the substances caffeine, sugar and alcohol.
** These tabs are connected with each other: If the user zooms in a tab and then changes to another tab, the same timespan will be focussed.
* Zooming into a specific period of life: this can be initiated by clicking on a specific location of the time-line, and by using the (+) and (-) icons. Zooming uses the "focus and context" technique to always view the selected area in context with the data of the other stages of life of the patient [Leung and Apperley, 1994].
** click on LifeLine element to move focus on that time span
* Details on demand
* Switching future scenarios

=== Discussion ===

* Wie werden die BenutzerInnen in ihren Aufgaben unterstützt?
* Welche Besonderheiten gibt es?
* Was sind die Vor- und Nachteile der Technik?

=== Future Work ===

* Erweiterungs- bzw. Verbesserungsmöglichkeiten

== References ==
* [Aigner, 2006] Wolfgang Aigner. Visualization of Time and Time-Oriented Information: Challenges and Conceptual Design. Ph.D. thesis, Vienna University of Technology, 2006.
* [Few, 2004] Stephen Few. Show Me the Numbers - Designing Tables and Graphs to Enlighten. Analytics Press, Oakland, CA, 2004.
* [Kosara and Miksch, 2002] Robert Kosara and Silvia Miksch. Visualization Methods for Data Analysis and Planning in Medical Applications. ''International Journal of Medical Informatics'', 68(1-3):141-153, December 2002.
* [Leung and Apperley, 1994] Y.K. Leung and M.D. Apperley. A Review and Taxonomy of Distortion-Oriented Presentation Techniques. ''ACM Transactions on Computer-Human Interaction (TOCHI)'', 1(2):126-160, June 1994.
* [Plaisant et al., 1998] Catherine Plaisant, Richard Mushlin, Aaron Snyder, Jia Li, Dan Heller, and Ben Shneiderman. LifeLines: Using Visualization to Enhance Navigation and Analysis of Patient Records. Technical Report HCIL-98-08, University of Maryland, 1998.
* [Pownser and Tufte, 1994] Seth M. Powsner and Edward R. Tufte. Graphical Summary of Patient Status. ''The Lancet'', 344(8919):386-389, August 1994.

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 06 - Aufgabe 4

2009-01-07T22:12:54Z

UE-InfoVis0809 9730468:

== 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.

== 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 06|Gruppe 06]]

== General description and background ==

=== Field of application ===
The visualization will be used by experts for medical and psychological analysis. The visualization shall provide information of a single patient, and give details about their consumption of several different drinks (water, milk, juice, coke, coffee, and beer), and the thus resulting consumption of sugar, caffeine and alcohol. It shall be possible to visualize both a time-line overview of these consumptions, as well as detailed views for specific periods in time. Additionally, certain periods of life, such as childhood or puberty, and specific events and happenings such as unemployment, wedding(s), or birth of the children, shall be indicated.

=== Analysis of the data set ===

The application displays ''abstract'', ''multi-dimensional'', ''time-oriented'' information:

* '''Drinks''': Consumption of six different drinks (water, milk, juice, coke, coffee, and beer) and sugar cubes by day.
** These dimensions have a ''continuous'' data type.
* '''Ingredients''': The amount of dangerous substances (sugar, caffeine, and alcohol) contained in the drinks and in the sugar cubes.
** These dimensions have a ''continuous'' data type.
** These variables are ''derived'' from the drinks and sugar cubes.
* '''Events''': Certain periods of life and specific events (e.g., childhood, unemployment).
** This dimension is of a ''nominal'' data type; they can be distinguished, but they cannot be ordered, as they might be overlapping.

The following table gives a detailed overview on each datum:

{|cellpadding="2" border="1"
! Dimension !! Data type !! Unit
|-
| water || continuous || ml
|-
| milk || continuous || ml
|-
| juice || continuous || ml
|-
| coke || continuous || ml
|-
| coffee || continuous || ml
|-
| beer || continuous || ml
|-
| sugar cube || discrete || pieces
|-
| sugar || continuous || g
|-
| caffeine || continuous || g
|-
| alcohol || continuous || g
|-
| events || nominal || - |
|}

==== Temporal Dimension ====

We can characterize the temporal dimension as follows [Aigner, 2006, pp. 13-17]:

* The application's time domain is ''branching'', because it allows the description of alternative scenarios.
* The time scale is ''discrete'' and days are the smallest granularity used.
* Drinks and ingredients are ''totally ordered'', whereas events are ''partially ordered'', because they might be overlapping.
* The temporal primitives used for drinks and ingredients are ''instants'', whereas ''intervals'' are used for events.
* The application uses the ''Gregorian calendar'' and different temporal granularities like months and year will be made available to the users.

The ingredients of the drinks, and their recommended maximum dose per day, are as follows:

{| border="1"
! !! Sugar !! Caffeine !! Alcohol
|-
! Water
| || ||
|-
! Milk
| || ||
|-
! Juice (100ml)
| align="right" | 10g || ||
|-
! Coffee (100ml)
| || align="right" | 80mg ||
|-
! Lump sugar (per piece)
| align="right" | 3g || ||
|-
! Coke (100ml)
| align="right" | 10g || align="right" | 10mg ||
|-
! Beer (100ml)
| align="right" | || || align="right" | 3.6g
|-
|-cellpadding="2"

! Daily dose
| align="right" | 50g || align="right" | 600mg || align="right" | 20g
|}

For the events and periods in life, the data would look like:

{| border="1"
! Start !! End !! Type !! remarks
|-
| 13.4.1965 || 12.4.1972 || Childhood ||
|-
| 7.7.1977 || 7.7.1977 || Wedding ||
|-
| 25.7.1980 || 25.7.1980 || Birth || Bart
|-
| 1.8.1981 || 31.10.1982 || Unemployment ||
|-
| 12.10.1982 || 12.10.1982 || Birth || Lisa
|-
|}

An exemplary data set for the consumption would look as follows:

{| border="1"
! Date !! Water !! Milk !! Juice !! Coffee !! Lump sugar !! Coke !! Beer
|-
| 7.7. 1977 || 0.25 || 0.5 || 0 || 0.75 || 5 || 1 || 5.5
|-
| 25.7. 1980 || 0 || 0 || 0.25 || 1.25 || 12 || 0 || 0
|-
| 1.1. 1993 || 1.0 || 0 || 0.25 || 0.25 || 2 || 0.33 || 1.5
|-
|}

=== Analysis of the target group ===
The intended target group of the visualization are physicians, psychologists and nutritionist. Thanks to their education they can interpret of the data and thus do primary need hints what different values imply but require guidance to navigate through the data and conceive it.

On the other hand it cannot be assumed that the users have technical knowledge of interactive systems or are willing to acquire it, therefore the visualization must be intuitive and as far as possible self descriptive.

Furthermore possible limitations by the target hardware must be taken into account. This could also limit the possibilities of interaction (e.g. screen-size, no keyboard or no mouse but a touch-pad).

=== Goals of the visualization ===
The visualisation shall allow medical doctors to get a fast overview on excess consumption of certain drinks and the thus resulting consumption of substances, potentially to be able to link it with certain symptoms and diseases Homer might suffer from.

For psychologists, it might be more relevant to link the consumption to events, to find patterns in what circumstances Homer tends to have an excess consumption of the certain drinks. It might be especially interesting to spot excess consumption that cannot be linked with a certain event, for example too much beer consumption that isn't in relation to a celebration or a known phase of depression, like unemployment.

Generally, we assume the doctors to use these tools mainly during a physical examination, or maybe a ward round; we thus expect the doctors having a limited amount of time available for inspecting the data, thus they should be able to get a fast overview and fast zooming into specific time periods. Therefore, we want to provide for a simple navigation, and rather focus on displaying data in the context of the whole life span, rather than giving (too) detailed data or even numbers for a specific date.

== Concept ==

=== Visualization ===
==== Type of Visualization ====
The data sets is composed of multiple instances of several measures taken periodically, i.e. a time series, which is best visualised as a line chart [Few, 2004].
We have several different aspects to visualize
* Consumption of drinks
* Consumption of substances/ingredients
* Individual consumption of caffeine, sugar and alcohol, indicating the source of the substance

The first two cam be visualized with normal line charts, while for the last one, we opted for a stacked line chart.

==== Visual Mapping ====
The visual mapping is the same in all our visualisation variants, and rather simple: the time series is mapped to the horizontal axis [Few, 2004], while the vertical axis maps the values at the specific point in time. The scale of the vertical axis either measured in litres consumption per day, or as a percentage of the recommended daily dose. The recommended dose, which is at 100%, is specifically highlighted, to allow for easily spotting over-consumption.

* Beschreibung der verwendeten Techniken / angewandten Prinzipien
** inkl. Quellenangaben (siehe z.B. VO Skriptum/Foliensatz)

=== Mockup(s) ===
The imaginary data-set and the charts were produced using Open Office Calc, while the final mockups were created with a drawing-program.

[[Image:Ue08-simpson-overview.png]] 
Figure 1: Overview and standard visualisation, showing the consumption of drinks during the whole life of the patient.

[[Image:Ue08-simpson-detail.png]] 
Figure 2: Detailed view of a specific period of time, using the context and zoom technique.

[[Image:Ue08-simpson-ingredients.png]] 
Figure 3: Overview, showing the consumption of substances.

[[Image:Ue08-simpson-caffeine.png]] 
Figure 4: Overview, showing the sources of caffeine consumed.

=== Interaction ===
As the application is meant for medical doctors and psychologists, we kept the interface simple, to avoid situations where the users might get lost in a specific view / selection and not finding their way back.

Still, we provide the following interaction modes
* Switching between different data to visualise: with the tabs on the top-right part of the tool, just above the time-line. This allows the user to select either the consumption of drinks, consumption of substances, or detailed consumption and sources for each of the substances caffeine, sugar and alcohol.
* Zooming into a specific period of life: this can be initiated by clicking on a specific location of the time-line, and by using the (+) and (-) icons. Zooming uses the "context and zoom" technique to always view the selected area in context with the data of the other stages of life of the patient.

* Interaktionsmöglichkeiten
** Wie erfolgt die Interaktion der BenutzerInnen mit dem System?
** Welche Interaktion dient welchem Zweck?
*** Navigation, Zooming, Highlighting, Dynamic Querying, Selection, Brushing, ...

=== Discussion ===

* Wie werden die BenutzerInnen in ihren Aufgaben unterstützt?
* Welche Besonderheiten gibt es?
* Was sind die Vor- und Nachteile der Technik?

=== Future Work ===

* Erweiterungs- bzw. Verbesserungsmöglichkeiten

== References ==
* [Aigner, 2006] Wolfgang Aigner. Visualization of Time and Time-Oriented Information: Challenges and Conceptual Design. Ph.D. thesis, Vienna University of Technology, 2006.
* [Few, 2004] Stephen Few. Show Me the Numbers - Designing Tables and Graphs to Enlighten. Analytics Press, Oakland, CA, 2004.
* [Kosara and Miksch, 2002] Robert Kosara and Silvia Miksch. Visualization Methods for Data Analysis and Planning in Medical Applications. ''International Journal of Medical Informatics'', 68(1-3):141-153, December 2002.
* [Leung and Apperley, 1994] Y.K. Leung and M.D. Apperley. A Review and Taxonomy of Distortion-Oriented Presentation Techniques. ''ACM Transactions on Computer-Human Interaction (TOCHI)'', 1(2):126-160, June 1994.
* [Plaisant et al., 1998] Catherine Plaisant, Richard Mushlin, Aaron Snyder, Jia Li, Dan Heller, and Ben Shneiderman. LifeLines: Using Visualization to Enhance Navigation and Analysis of Patient Records. Technical Report HCIL-98-08, University of Maryland, 1998.
*[Pownser and Tufte, 1994] Seth M. Powsner and Edward R. Tufte. Graphical Summary of Patient Status. ''The Lancet'', 344(8919):386-389, August 1994.

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 06 - Aufgabe 4

2009-01-07T21:45:00Z

UE-InfoVis0809 9730468: Interaction

== 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.

== 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 06|Gruppe 06]]

== General description and background ==

=== Field of application ===
The visualization will be used by experts for medical and psychological analysis. The visualization shall provide information of a single patient, and give details about their consumption of several different drinks (water, milk, juice, coke, coffee, and beer), and the thus resulting consumption of sugar, caffeine and alcohol. It shall be possible to visualize both a time-line overview of these consumptions, as well as detailed views for specific periods in time. Additionally, certain periods of life, such as childhood or puberty, and specific events and happenings such as unemployment, wedding(s), or birth of the children, shall be indicated.

=== Analysis of the data set ===

The application displays ''abstract'', ''multi-dimensional'', ''time-oriented'' information:

* '''Drinks''': Consumption of six different drinks (water, milk, juice, coke, coffee, and beer) and sugar cubes by day.
** These dimensions have a ''continuous'' data type.
* '''Ingredients''': The amount of dangerous substances (sugar, caffeine, and alcohol) contained in the drinks and in the sugar cubes.
** These dimensions have a ''continuous'' data type.
** These variables are ''derived'' from the drinks and sugar cubes.
* '''Events''': Certain periods of life and specific events (e.g., childhood, unemployment).
** This dimension is of a ''nominal'' data type; they can be distinguished, but they cannot be ordered, as they might be overlapping.

The following table gives a detailed overview on each datum:

{|cellpadding="2" border="1"
! Dimension !! Data type !! Unit
|-
| water || continuous || ml
|-
| milk || continuous || ml
|-
| juice || continuous || ml
|-
| coke || continuous || ml
|-
| coffee || continuous || ml
|-
| beer || continuous || ml
|-
| sugar cube || discrete || pieces
|-
| sugar || continuous || g
|-
| caffeine || continuous || g
|-
| alcohol || continuous || g
|-
| events || nominal || - |
|}

==== Temporal Dimension ====

We can characterize the temporal dimension as follows [Aigner, 2006, pp. 13-17]:

* The application's time domain is ''branching'', because it allows the description of alternative scenarios.
* The time scale is ''discrete'' and days are the smallest granularity used.
* Drinks and ingredients are ''totally ordered'', whereas events are ''partially ordered'', because they might be overlapping.
* The temporal primitives used for drinks and ingredients are ''instants'', whereas ''intervals'' are used for events.
* The application uses the ''Gregorian calendar'' and different temporal granularities like months and year will be made available to the users.

The ingredients of the drinks, and their recommended maximum dose per day, are as follows:

{| border="1"
! !! Sugar !! Caffeine !! Alcohol
|-
! Water
| || ||
|-
! Milk
| || ||
|-
! Juice (100ml)
| align="right" | 10g || ||
|-
! Coffee (100ml)
| || align="right" | 80mg ||
|-
! Lump sugar (per piece)
| align="right" | 3g || ||
|-
! Coke (100ml)
| align="right" | 10g || align="right" | 10mg ||
|-
! Beer (100ml)
| align="right" | || || align="right" | 3.6g
|-
|-cellpadding="2"

! Daily dose
| align="right" | 50g || align="right" | 600mg || align="right" | 20g
|}

For the events and periods in life, the data would look like:

{| border="1"
! Start !! End !! Type !! remarks
|-
| 13.4.1965 || 12.4.1972 || Childhood ||
|-
| 7.7.1977 || 7.7.1977 || Wedding ||
|-
| 25.7.1980 || 25.7.1980 || Birth || Bart
|-
| 1.8.1981 || 31.10.1982 || Unemployment ||
|-
| 12.10.1982 || 12.10.1982 || Birth || Lisa
|-
|}

An exemplary data set for the consumption would look as follows:

{| border="1"
! Date !! Water !! Milk !! Juice !! Coffee !! Lump sugar !! Coke !! Beer
|-
| 7.7. 1977 || 0.25 || 0.5 || 0 || 0.75 || 5 || 1 || 5.5
|-
| 25.7. 1980 || 0 || 0 || 0.25 || 1.25 || 12 || 0 || 0
|-
| 1.1. 1993 || 1.0 || 0 || 0.25 || 0.25 || 2 || 0.33 || 1.5
|-
|}

=== Analysis of the target group ===
The intended target group of the visualization are physicians, psychologists and nutritionist. Thanks to their education they can interpret of the data and thus do primary need hints what different values imply but require guidance to navigate through the data and conceive it.

On the other hand it cannot be assumed that the users have technical knowledge of interactive systems or are willing to acquire it, therefore the visualization must be intuitive and as far as possible self descriptive.

Furthermore possible limitations by the target hardware must be taken into account. This could also limit the possibilities of interaction (e.g. screen-size, no keyboard or no mouse but a touch-pad).

=== Goals of the visualization ===
The visualisation shall allow medical doctors to get a fast overview on excess consumption of certain drinks and the thus resulting consumption of substances, potentially to be able to link it with certain symptoms and diseases Homer might suffer from.

For psychologists, it might be more relevant to link the consumption to events, to find patterns in what circumstances Homer tends to have an excess consumption of the certain drinks. It might be especially interesting to spot excess consumption that cannot be linked with a certain event, for example too much beer consumption that isn't in relation to a celebration or a known phase of depression, like unemployment.

== Concept ==

=== Visualization ===
==== Type of Visualization ====
The data sets is composed of multiple instances of several measures taken periodically, i.e. a time series, which is best visualised as a line chart [Few, 2004].
We have several different aspects to visualize
* Consumption of drinks
* Consumption of substances/ingredients
* Individual consumption of caffeine, sugar and alcohol, indicating the source of the substance

The first two cam be visualized with normal line charts, while for the last one, we opted for a stacked line chart.

* Visual Mapping
** Wie werden die verschiedenen Datendimensionen in visuelle Attribute umgesetzt?
*** z.B. Dimension "Filegröße" --> visuelles Attribut "Fläche"
* Beschreibung der verwendeten Techniken / angewandten Prinzipien
** inkl. Quellenangaben (siehe z.B. VO Skriptum/Foliensatz)

=== Mockup(s) ===
The imaginary data-set and the charts were produced using Open Office Calc, while the final mockups were created with a drawing-program.

[[Image:Ue08-simpson-overview.png]] 
Figure 1: Overview and standard visualisation, showing the consumption of drinks during the whole life of the patient.

[[Image:Ue08-simpson-detail.png]] 
Figure 2: Detailed view of a specific period of time, using the context and zoom technique.

[[Image:Ue08-simpson-ingredients.png]] 
Figure 3: Overview, showing the consumption of substances.

[[Image:Ue08-simpson-caffeine.png]] 
Figure 4: Overview, showing the sources of caffeine consumed.

=== Interaction ===
As the application is meant for medical doctors and psychologists, we kept the interface simple, to avoid situations where the users might get lost in a specific view / selection and not finding their way back.

Still, we provide the following interaction modes
* Switching between different data to visualise: with the tabs on the top-right part of the tool, just above the time-line. This allows the user to select either the consumption of drinks, consumption of substances, or detailed consumption and sources for each of the substances caffeine, sugar and alcohol.
* Zooming into a specific period of life: this can be initiated by clicking on a specific location of the time-line, and by using the (+) and (-) icons. Zooming uses the "context and zoom" technique to always view the selected area in context with the data of the other stages of life of the patient.
*

* Interaktionsmöglichkeiten
** Wie erfolgt die Interaktion der BenutzerInnen mit dem System?
** Welche Interaktion dient welchem Zweck?
*** Navigation, Zooming, Highlighting, Dynamic Querying, Selection, Brushing, ...

=== Discussion ===

* Wie werden die BenutzerInnen in ihren Aufgaben unterstützt?
* Welche Besonderheiten gibt es?
* Was sind die Vor- und Nachteile der Technik?

=== Future Work ===

* Erweiterungs- bzw. Verbesserungsmöglichkeiten

== References ==
* [Aigner, 2006] Wolfgang Aigner. Visualization of Time and Time-Oriented Information: Challenges and Conceptual Design. Ph.D. thesis, Vienna University of Technology, 2006.
* [Few, 2004] Stephen Few. Show Me the Numbers - Designing Tables and Graphs to Enlighten. Analytics Press, Oakland, CA, 2004.
* [Kosara and Miksch, 2002] Robert Kosara and Silvia Miksch. Visualization Methods for Data Analysis and Planning in Medical Applications. ''International Journal of Medical Informatics'', 68(1-3):141-153, December 2002.
* [Leung and Apperley, 1994] Y.K. Leung and M.D. Apperley. A Review and Taxonomy of Distortion-Oriented Presentation Techniques. ''ACM Transactions on Computer-Human Interaction (TOCHI)'', 1(2):126-160, June 1994.
* [Plaisant et al., 1998] Catherine Plaisant, Richard Mushlin, Aaron Snyder, Jia Li, Dan Heller, and Ben Shneiderman. LifeLines: Using Visualization to Enhance Navigation and Analysis of Patient Records. Technical Report HCIL-98-08, University of Maryland, 1998.
*[Pownser and Tufte, 1994] Seth M. Powsner and Edward R. Tufte. Graphical Summary of Patient Status. ''The Lancet'', 344(8919):386-389, August 1994.

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 06 - Aufgabe 4

2009-01-07T21:18:37Z

UE-InfoVis0809 9730468: Teil von concept beschreibung

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 06 - Aufgabe 4

2009-01-07T14:06:59Z

UE-InfoVis0809 9730468: Merged data, added examples, started on goals

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 06 - Aufgabe 4

2009-01-06T17:55:46Z

UE-InfoVis0809 9730468:

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 06 - Aufgabe 4

2009-01-06T17:48:30Z

UE-InfoVis0809 9730468:

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 06 - Aufgabe 4

2009-01-06T17:42:09Z

UE-InfoVis0809 9730468:

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 06 - Aufgabe 4

2009-01-06T17:32:01Z

UE-InfoVis0809 9730468:

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 06 - Aufgabe 4

2009-01-06T17:10:15Z

UE-InfoVis0809 9730468:

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 06 - Aufgabe 1 - Treemap

2008-11-07T07:21:22Z

UE-InfoVis0809 9730468:

[[Image:Shneiderman 1992 tree as graph.png|thumb|300px|Figure 1: Tree as directed graph [Shneiderman, 1992]]]
[[Image:Shneiderman 1992 treemap.png|thumb|300px|Figure 2: Same tree as Treemap [Shneiderman, 1992]]]
[[Image:Shneiderman 1992 treemap colour.png|thumb|300px|Figure 3: Colour-coded Treemap [Shneiderman, 1992]]]
[[Image:Bederson 2002 treemap photo.gif|thumb|300px|Figure 4: Treemap for Images: PhotoMesa [Bederson, 2002]]]
[[Image:Smith 2001 treemap usenet.jpg|thumb|300px|Figure 5: Treemap of the Usenet [Smith, 2001]]]

{{Definition|Treemaps are a 2D space-filling visualisation method for hierarchical, tree like structures. They were introduced by Shneiderman [1992].}}

Treemaps aim at visualising a certain attribute of hierarchically structured data in a 2D space, for example the size of files in various levels of folder hierarchies on a hard-disk (which was the motivation for developing Tremaps in [Shneiderman 1992]). Then traditional ways of visualising hierarchical structures as (directed) graphs, as in Figure 1, can show relations between the nodes, but fail to visualize attributes such as size or importance of nodes. Treemaps try to overcome this by visualising each node as a small rectangle in the 2D space, as in Figure 2.

In detail, the Treemap algorithm has the following properties
*Input: a root node, and a 2D space.
*The number of outgoings edge from the root node determines the number of partitions in the space.
*The sum of weights assigned to each sub-tree determines the size of the partitions.
*Then, recursively the space assigned to each sub-tree will be partitioned.
*The sub-tree are partitioned alternatively horizontally and vertically, with odd levels being horizontally, and even vertically.
*Additional attributes can be visualised with a colour coding (e.g. file-types as in Figure 3).
This algorithm was named "slice and dice".

The Treemap method has been well studied, and a wealth of variations of the original algorithms have been published (compare [Shneiderman 1998]), among them:
* Squarified Treemaps try to render the shapes of nodes rather as squares than as (long and narrow) rectangles, which are difficult to compare. To achieve this, the order of the nodes is disregarded. Squarified Treemaps may lead to stability issues when node sizes change, causing the order of nodes to potentially change dramatically ([Benderson 2002]).
* An attempt to overcome the stability issue is presented in [Shneiderman 2001] by balancing order and visual appeal of squared slices.

Applications of Treemaps include:
*Visualisation of File-system usage [Shneiderman 1992]
*A photo image browser, where the issue of having to display fixed-size content (the images) in shapes with potentially very differently aspect ratios [Benderson 2002]
*A visualisation of the newsgroups in Usenet, and the frequency of specific types of newsgroups [Smith 2001]

== References ==

*[Shneiderman, 1992] Ben Shneiderman. Tree visualization with tree-maps: A 2-D space-filling approach. ''ACM Transactions on Graphics'', 11(1):92-99, 1992.
*[Shneiderman, 1998] Ben Shneiderman. Treemaps for space-constrained visualization of hierarchies. Created: Dec. 26th, 1998, Last retrieved Nov. 5th, 2008. http://www.cs.umd.edu/hcil/treemap-history/
*[Shneiderman, 2001] Ben Shneiderman, Martin Wattenberg. Ordered treemap layouts. In ''Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS 2001), pp 73 - 78.
*[Benderson, 2002] Benjamin B. Bederson, Ben Shneiderman, B. and Martin Wattenberg. Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies. ''ACM Transactions on graphics''
*[Smith 2001] Marc Smith and Andrew Fiore. Visualization components for persistent conversations. In ''Proceedings of the Conference on Human Factors in Computing Systems'', pp 136-143. Seattle, Washington, United States, 2001

Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 06 - Aufgabe 1 - Treemap

2008-11-07T07:16:16Z

UE-InfoVis0809 9730468:

[[Image:Shneiderman 1992 tree as graph.png|thumb|300px|Figure 1: Tree as directed graph [Shneiderman, 1992]]]
[[Image:Shneiderman 1992 treemap.png|thumb|300px|Figure 2: Same tree as Treemap [Shneiderman, 1992]]]
[[Image:Shneiderman 1992 treemap colour.png|thumb|300px|Figure 3: Colour-coded Treemap [Shneiderman, 1992]]]
[[Image:Bederson 2002 treemap photo.gif|thumb|300px|Figure 4: Treemap for Images: PhotoMesa [Bederson, 2002]]]
[[Image:Smith 2001 treemap usenet.jpg|thumb|300px|Figure 5: Treemap of the Usenet [Smith, 2001]]]

{{Definition|Treemaps are a 2D space-filling visualisation method for hierarchical, tree like structures. They were introduced by Shneiderman [1992].}}

Treemaps aim at visualising a certain attribute of hierarchically structured data in a 2D space, for example the size of files in various levels of folder hierarchies on a hard-disk (which was the motivation for developing Tremaps in Shneiderman [1992]). Then traditional ways of visualising hierarchical structures as (directed) graphs, as in Figure 1, can show relations between the nodes, but fail to visualize attributes such as size or importance of nodes. Treemaps try to overcome this by visualising each node as a small rectangle in the 2D space, as in Figure 2.

In detail, the Treemap algorithm has the following properties
*Input: a root node, and a 2D space.
*The number of outgoings edge from the root node determines the number of partitions in the space.
*The sum of weights assigned to each sub-tree determines the size of the partitions.
*Then, recursively the space assigned to each sub-tree will be partitioned.
*The sub-tree are partitioned alternatively horizontally and vertically, with odd levels being horizontally, and even vertically.
*Additional attributes can be visualised with a colour coding (e.g. file-types as in Figure 3).
This algorithm was named "slice and dice".

The Treemap method has been well studied, and a wealth of variations of the original algorithms have been published (compare [Shneiderman 1998]), among them:
* Squarified Treemaps try to render the shapes of nodes rather as squares than as (long and narrow) rectangles, which are difficult to compare. To achieve this, the order of the nodes is disregarded. Squarified Treemaps may lead to stability issues when node sizes change, causing the order of nodes to potentially change dramatically ([Benderson 2002]).
* An attempt to overcome the stability issue is presented in [Shneiderman 2001] by balancing order and visual appeal of squared slices.

Applications of Treemaps include:
*Visualisation of File-system usage [Shneiderman 1992]
*A photo image browser, where the issue of having to display fixed-size content (the images) in shapes with potentially very differently aspect ratios [Benderson 2002]
*A visualisation of the newsgroups in Usenet, and the frequency of specific types of newsgroups [Smith 2001]

== References ==

*[Shneiderman, 1992] Ben Shneiderman. Tree visualization with tree-maps: A 2-D space-filling approach. ''ACM Transactions on Graphics'', 11(1):92-99, 1992.
*[Shneiderman, 1998] Ben Shneiderman. Treemaps for space-constrained visualization of hierarchies. Created: Dec. 26th, 1998, Last retrieved Nov. 5th, 2008. http://www.cs.umd.edu/hcil/treemap-history/
*[Shneiderman, 2001] Ben Shneiderman, Martin Wattenberg. Ordered treemap layouts. In ''Proceedings of the IEEE Symposium on Information Visualization 2001 (INFOVIS 2001), pp 73 - 78.
*[Benderson, 2002] Benjamin B. Bederson, Ben Shneiderman, B. and Martin Wattenberg. Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies. ''ACM Transactions on graphics''
*[Smith 2001] Marc Smith and Andrew Fiore. Visualization components for persistent conversations. In ''Proceedings of the Conference on Human Factors in Computing Systems'', pp 136-143. Seattle, Washington, United States, 2001

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{{Definition|Treemaps are a 2D space-filling visualisation method for hierarchical, tree like structures. They were introduced by Shneiderman [1992].}}

Treemaps aim at visualising a certain attribute of hierarchically structured data in a 2D space, for example the size of files in various levels of folder hierarchies on a hard-disk (which was the motivation for developing Tremaps in Shneiderman [1992]). Then traditional ways of visualising hierarchical structures as (directed) graphs, as in Figure 1, can show relations between the nodes, but fail to visualize attributes such as size or importance of nodes. Treemaps try to overcome this by visualising each node as a small rectangle in the 2D space, as in Figure 2.

[[Image:Shneiderman 1992 tree as graph.png|thumb|300px|Figure 1: Tree as directed graph [Shneiderman, 1992]]]
[[Image:Shneiderman 1992 treemap.png|thumb|300px|Figure 2: Same tree as Treemap [Shneiderman, 1992]]]
[[Image:Shneiderman 1992 treemap colour.png|thumb|300px|Figure 3: Colour-coded Treemap [Shneiderman, 1992]]]

In detail, the Treemap algorithm has the following properties
*Input: a root node, and a 2D space.
*The number of outgoings edge from the root node determines the number of partitions in the space.
*The sum of weights assigned to each sub-tree determines the size of the partitions.
*Then, recursively the space assigned to each sub-tree will be partitioned.
*The sub-tree are partitioned alternatively horizontally and vertically, with odd levels being horizontally, and even vertically.
*Additional attributes can be visualised with a colour coding (e.g. file-types as in Figure 3).

== References ==

*[Shneiderman, 1992] Ben Shneiderman. Tree visualization with tree-maps: A 2-D space-filling approach. ''ACM Transactions on Graphics'', 11(1):92-99, 1992.

File:Shneiderman 1992 treemap colour.png

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UE-InfoVis0809 9730468: New page: {{Definition|Treemaps are a 2D space-filling visualisation method for hierarchical, tree like structures. They were introduced by Shneiderman [1992].}} == References == *[Shneiderma...

{{Definition|Treemaps are a 2D space-filling visualisation method for hierarchical, tree like structures. They were introduced by Shneiderman [1992].}}

== References ==

*[Shneiderman, 1992] Ben Shneiderman. Tree visualization with tree-maps: A 2-D space-filling approach. ''ACM Transactions on Graphics'', 11:92-99, 1992.

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== Rudolf Mayer ==
[[image:Photo_Rudolf_Mayer.jpg]]
* Matrikelnummer: 9730468
* Studium: 066-936
* Email: rudolf.mayer (at) gmail.com
* [[Teaching:TUW - UE InfoVis WS 2008/09 - Gruppe 06|Gruppe 06]]

File:Photo Rudolf Mayer.jpg

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UE-InfoVis0809 9730468: New page: * Rudolf Mayer * 9730468 * 066-936 * rudolf.mayer (at) gmail.com

* Rudolf Mayer
* 9730468
* 066-936
* rudolf.mayer (at) gmail.com