Overview - Visual Analyticsieg.ifs.tuwien.ac.at/~aigner/presentations/tempdatavis03.pdfOrganization...
Transcript of Overview - Visual Analyticsieg.ifs.tuwien.ac.at/~aigner/presentations/tempdatavis03.pdfOrganization...
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interactive visualization oftemporal data
Wolfgang Aigner
[email protected]://www.asgaard.tuwien.ac.at/~aigner/
Version 1.024. 11. 2003
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:2
Overview
introductionwhat is special about the time dimension?what is temporal data?visualization roots
excursus: art background
taxonomyimportant concepts of timetasks for temporal datavisualization classification
infoVis techniquespresentation and discussion
Section A:
introduction
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Data types
1-dimensional
2-dimensional
3-dimensional
Temporal
Multi-dimensional
Tree
Network
= 4D space“the world we are living in”
[Shneiderman, 1996]
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Spatial + temporaldimensionsEvery data element we measure is related and
often only meaningful when related tospace + time
Example: price of a computerwhere?
when?
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Differences betweenspace and timeSpace can be traversered “arbitrarily”
we can move back to where we came from
Time is unidirectionalwe can’t go back or forward in time
Humans have senses for perceiving spacevisually, touch
Humans don’t have senses for perceiving time
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:7
…travel in timevirtually.
InteractivevisualizationGives us the ability to…
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What do we consideras temporal data?General:
Main focus on change over time of data elements
More formally:Data elements are a function of timed = f(t)
For discrete time steps:D = {(t1,d1), (t2, d2), …, (tn, dn)}di = f(ti)
[Schumann, 2003]
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Visualization roots
StatisticsVisualization of time-series.
The time-series plot is themost frequently used formof graphic design. [Tufte, 1983]
Mostly one parameter over time.
Artincorporating timemore later
t
y
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Early time-series plot
Part of a text for monastery schools10th or 11th century (!)Inclinations of the planetary orbits over time800 years before other time-series plots appeared
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Train schedule
Paris to Lyon (1880s)
Excursus:
art background
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Renaissance
[Masaccio and Masolino, Scenes from the Life of St. Peter, c.1426-7, Brancacci Chapel, Florence]
Multiple appearences of the same person within a single scene
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CubismThe first documented occurrenceof the fourth dimension beingused in art appeared in 1910 inParis.
Origin: mathematics + physics(n-dimensional spaces)
At this point, the fourthdimension was thought as time.
Person walking down stairs -->
Furth dimension in the paintingby picturing different stages ofthe person’s descent
[Marcel Duchamp, Nude Descending a Staircase, 1912]
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Cubism
New ideas about thefourth dimension into thestatic domain of pictures.
Overlays many differentobservations.
Emphasizes process oflooking and recordingover time.
[Picasso, Portrait of Vollard, 1910]WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:16
Comics
Visual story tellingover time.
Many interestingtechniques /paradigms.
If you want to knowmore, start here:[Scott McCloud,UnderstandingComics, 1994]
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Section B:
taxonomy
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Reference to time
Time reference ofdata
Time reference ofpresentation
Example:
temperature change of alake is continuous overtime
--> continuous change inreal world
temperature measuringtwice a year
--> discrete time points inpresentation
vs.
[Schumann and Müller, 2000]
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Classification of visualrepresentations
Static representationsNot time-dependentDoes not automatically change over time
Dynamic representationsTime-dependentChanges dynamically over timeIs a function of time
Event-based representations
[Schumann and Müller, 2003]
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InteractivityDefinitions:
interactive information system An information system in which the user communicates with the
computing facility through a terminal and receives rapidresponses which can be used to prepare the next input.[McGraw-Hill Online Science Dictionary]
interactiveOf or relating to a program that responds to user activity.
[American Heritage Online Dictionary]Of, relating to, or being a two-way electronic communication
system (as a telephone, cable television, or a computer) thatinvolves a user's orders (as for information or merchandise) orresponses (as to a poll)[Merriam-Webster Online Dictionary]
Interactive visualization != AnimationUser controlled vs. data controlled
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Tasks / Questions 1/2
Existence of a data elementDoes a data element exist at a specific time?
Temporal locationWhen does a data element exist on time?Is there any cyclic behavior?
Time intervalHow long is the time span from beginning to end of the
data element?
Temporal textureHow often does a data element occur?
[McEachren, 1995]
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Tasks / Questions 2/2
Rate of changeHow fast is a data element changing or how much
difference is there from data element to data elementover time?
SequenceIn what order do data elements appear?
SynchronizationDo data elements exist together?
[McEachren, 1995]
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ConceptualOrganization
Time seriesTime treated as linear sequence
Don’t confuse with linear time scale
Time cycleTime treated as repeating cycle
Many processes in nature and sciencehave cyclic behaviore.g. days, years, seasons, …
[McEachren, 1995]
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Temporal dimensions
PastDefinite time - data element assignment
PresentCurrently valid
state
FuturePlanning
Temporal uncertainty
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Section C:
infoVis techniquesContents:
ThemeRiverTM
TimeWheelLexis Pencil
GANTT chartsLifeLinesPerspective WallCalendar toolsSpiraClock
Serial Periodic DataSpiral Graph + HelixIntrusion Detection
Time-wheelSW-Evolution
Analysis
Music Animation Machine
Temporal ObjectsTime GlyphPaint stripsSOPOs
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GANTT charts 1/2
Project management, project planning
Tasks and their temporal attributes (location, duration)
Milestones
Past + present + future
Hierarchical decomposition
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GANTT charts 2/2
Pros:Well known representation
Collapsable hierarchical decompostion
Easy to comprehend
Hundreds of tools available (i.e. MS Project)
Cons:No uncertainty
Space consumption (diagonal layout)
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LifeLines 1/2
Based on Time Lines
Facets
Visualizing personalhistories and patientinformation
Horizontal barsshowing temporallocation and durationof data elements
Past + Present
[Plaisant et al., 1996, Plaisant et al., 1998]
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LifeLines 2/2
Pros:Simple and easy to comprehend
Better layout than GANTT
Use of vertical dimension
Interactive time scale (zoom, pan)
Cons:No hierarchical decomposition (only Facets)
(Just past and present)
[Plaisant et al., 1996] Plaisant, C., Milash, B., Rose, A., Wido , S., and Shneiderman,
B. (1996). LifeLines: Visualizing Personal Histories. In Proceedings CHI'96
ACM Conference on Human Factors in Computing Systems, pages 221{227, New
York. ACM Press.
[Plaisant et al., 1998] Plaisant, C., Mushlin, R., Snyder, A., Li, J., Heller, D., and
Shneiderman, B. (1998). LifeLines: Using Visualization to Enhance Navigation
and Analysis of Patient Records. In Proceedings of the 1998 American Medical
Informatic Association Annual Fall Symposium, pages 76-80.
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:30
Perspective Wall
Large collections of documents
Focus + Context of elements over time
Intuitive 3D metaphor for distorting 2D layout
Color coding
Smooth transitions, 3D interactive animation
[Mackinlay et al., 1991]
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:31
Calendar Tools
Past + present + future
Calendar scale
Events over time, repeating events
Icons, Reminder
Very well known (MS Outlook, iCal, …)
Interactive Techniques:Overview + DetailZoomFilterDetails on DemandMultiple ViewsFocus + Context
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:32
SpiraClock 1/2
Visualization technique for nearby events.
Intention: fill gap between static calendar and pop-upreminders.
Continuous and non-intrusive feedback.
Analog clock with white spiralinside representing near future.
[Dragicevic and Huot, 2002]
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SpiraClock 2/2
Interaction:Change time by moving hands.Adjust number of spiral revolutions
(visibility of future events)
Range: 1 hour - several days
Not suited for all kinds of eventsi.e. conference, 20. - 25. October
Java applets and applications:http://www.emn.fr/spiraclock
Bus schedule, MS Outlook and vCal import
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Temporal Objects 1/2
Depict future planning data with temporal uncertaintyStarting instant (earliest start, latest start)Ending instant (earliest end, latest end)Maximum durationMinimum duration
Based on LifeLines
Two encapsulated bars with caps at each end
[Combi et al., 1999]
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Temporal Objects 2/2
Pros:Simple representation for complex time
annotationsTemporal uncertaintyEasy to comprehend
Cons:Only presentation, no interactionNo direct manipulation
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:36
Time Glyph 1/2
Similar to “Temporal Objects”
Additionally / Improvements:Time points are relative (Reference point)Notion for temporal granularityNotion for missing values / incomplete specificationsMetaphor of bar lying on diamonds (preventing invalid constellations)User interaction / can be manipulated
[Kosara and Miksch, 1999]
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Time Glyph 2/2
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Paint Strips
Metaphor of paint rollers
Paint roller at the end of a line = line can expand
Wall = expansion limit
Smaller set of temporal attributes as “Temporal Objects” and“Time Glyph”
Combination of strips (rope)
Starting and finishing interval can’t be defined independentlyfrom duration
[Chittaro and Combi, 2001]
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:39
SOPOs 1/2
2D technique
Area depicts set of valid(start, end) tuples
Designed for easy graphicalpropagation of temporalconstraints
Cons:Representation morecomplicated than LifeLinebased onesSpace consumption
[Messner, 2000]
R i t ’s Set ofPossible
Occurences
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SOPOs 2/2
Start interval: x-axis
End interval: y-axis
Minimum duration,maximum duration:constrainingborders parallel to45° time flow axis
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Intrusion Detection
Visualization of user access tomachines over time.
Mapping:Time: circumference
User: cylinder slice
Machines: cubes on top
Access: connection lines
Annotations via tool tips(mouse hovering)
[Muniandy, 2001]
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ThemeRiverTM 1 / 3
Visualize thematic variations over time.
Across a large collection of documents.
River Metaphor: the “river” flows through time.
Changing width to depict changes.
Themes or topics are colored “currents”.
[Havre et al., 2000]
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ThemeRiverTM 2 / 3
Continuous flow
Interpolation, approximation
Easy to follow a single current(curving continous lines)
Discrete values
Exact values
Hard to follow a singlecurrent
Histogram vs. ThemeRiverTM:
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ThemeRiverTM 3 / 3
User interaction:Hide or display
topic + event labelstime + event grid linesraw data points
Choose alternate algorithms for line drawingPan + Zoom
Color relationsRelated themes are associated to the same color family
Improvements:Parallel riversDisplay of numeric values (on demand)Total number of documentsAccess documents directlyUser defined ordering
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TimeWheel / Zeitrad 1/2
Time axis in the center
Variable axis arranged circularly
Lines connecting time andfeature values
Similar to parallel coordinates
Variables parallel to time axis (upper and lower) canbe explored most effectively
Focus + Context by shortening of rotated axis andcolor fading
[Tominski et al., 2003]
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:46
TimeWheel / Zeitrad 2/2
User interaction:Rotation of variable axes(moving axes of interest into a position parallel to the time axis)
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:47
MultiCombs
Axis based technique
Multiple parameters on multiple time axis, circularly arranged
Outward from the center of star-shaped
Aggregated view of “past” values in the center
[Müller and Schumann, 2003]
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:48
Lexis Pencil
Pencil-like geometricobjects
Mapping time-dependent variablesonto faces of the pencil
Heterogenous data
[Francis and Pritchard, 1997]
Can be located in 3D spaceto show the spatial context
Tip allows exact positioningProblem: Occlusion
Focus + ContextOn pencil: by radialarrangementIn 3D space: enlarging pencilin focus
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Serial Periodic Data 1/6
Visualize both, serial + periodicproperties to reveal certainpatterns
Time continues serially, but weeks,month, and years are periods thatreoccur
Map time onto a spiral + spokesfor orientation
Data values are mapped to blotson spiral
Area of blot proportional to value
[Carlis and Konstan, 1998]
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:50
Serial Periodic Data 2/6
Pureserial periodic data
Periods with constantdurations
Event-anchoredserial periodic data
Periods with differentdurations
Start of a new period isindicated by an event
Examples:Multi day racing dataProject based timetracking
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:51
Serial Periodic Data 3/6Extension to 3D:
Z-axis for different sets of dataNo quantitative meaning of z-axis
Color coding of data sets
Lidless, hollow “cans”Instead of blotsPrevent occlusion
Volume of can is proportional to data value
Pro: good overview
Cons:OcclusionClutterZ-position meaninglessDouble mapping (z-pos + color)
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:52
User control:Rotation, zoom, pan, tilt
Annotation features:Align different spirals verticallyDefinition of data derived borderlines
Display of several data setssimultaneously
Using bar chartsColor coded
Multiple, linked spirals
Serial Periodic Data 4/6
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Serial Periodic Data 5/6
Interval dataOnly duration of element
Periodicity unknownAnimation
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Serial Periodic Data 6/6
User experience findings:+ Users quickly accept the notion of serial periodic
data on a spiral
+ Users react to the spiral displaysWhen they saw patterns, they tried to explain them by
telling stories
+ Users want moreVisualization sparked interest for further investigation
- Tool not self explanatoryTrained operator needed
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:55
Spiral Graph 1/3
Main intension: detection ofperiodic behavior
Mapping data onto a spiralMapping of data values to
– color and
– thickness of line
Nominal + ordinal +quantitative data
1 cycle =period length
[Weber et al., 2001]
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:56
Spiral Graph 2/3
Two possibilities to detect periodic behavior:
1. Computational:Compute frequencies with higher amplitudes via Fourier Transformation
2. Visually:Utilize the visual system of a human observer to discover structures
Spiral is animated by continously changing the cycle length
Periodic behavior becomes immediately apparent(changing from unstructured to structured)
User can stop animation when period is spotted
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Spiral Graph 3/3
Extensions:
Multi SpiralsCompare a data set with cyclic patternsin other data.Rendering intertwined Spiral Graphs.
3D extensionProblem: space mapping onto a helix.Brushing integrated.Selected region is displayed in 2Dspiral.3D helix best used for navigation only.
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:58
Time-wheel 1/3
Visualization of softwareprojects over time
Multiple time-series placed in acircle
Data attributes are color coded
Global trends
Helps to examine differenttrends within one object
Easy recognition of two trends:Increasing trend
Tapering trend
[Chuah and Eick, 1997]
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:59
Time-wheel 2/3
Increasing trend Tapering trend
„Prickly fruit“ „Hairy fruit“
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Time-wheel 3/3
Extension to 3D:
Encodes the same attributes as theTime-wheel
Uses height dimension to encode time
Variables are encoded as slices of abase circle
Pro: Easier to identify overall trends
Cons:Occlusion
Perspective
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Software EvolutionAnalysis
Analyzing evolution of SW-systems / product families
3D visualization
Colors encode versions
Changes of parts over time
Hierarchical decomposition
Pattern analysis
Not as information rich as Time-wheel
[Jazayeri et al., 1999]
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Music AnimationMachine (M.A.M.) 1/2
Visualization of music
Dynamic representation
Relate audio to visualstructure
Simple representation formusic
extremely complex system
Complex patterns
Online:http://www.well.com/user/smalin/mam.html
[Malinowski]
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Music AnimationMachine (M.A.M.) 2/2
Each note is representedby a colored bar
Each bar lights up as itsnote sounds
The length of each barcorresponds exactly to theduration of its note as performed
The vertical position of the bar corresponds to the pitch
The horizontal position indicates the note's timing
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:64
Roundup
Setting the sceneProperties of “time”
What are we talking about
Tales about the pastEarly statistical graphics
Time in art
Looking backstageIdeas, concepts, definitions
Opening the curtainState-of-the-art InfoVis techniques
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ConclusionsTemporal data covers a very broad field
A lot of different techniques available
Visualizations are task driven
Cyclic/periodic behaviour is very common but relativelyunderexplored
i.e. event-anchored data
Not many dynamic techniques availableOnly very limited use of animation
More interactivity is desireable
Generally: Visualization sparks interest for further investigation
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:66
References 1/5
[Carlis and Konstan, 1998]Carlis, J.V. and Konstan, J.A., Interactive Visualization of Serial Periodic Data,ACM Symposium on User Interface Software and Technology, 1998
[Chuah and Eick, 1997]Chuah M.C. and Eick S.G., Glyphs for Software Visualization, InternationalWorkshop on Program Comprehension, pp. 183-191, May 1997.
[Combi et al., 1999]Combi, C., Portoni, L., and Pinciroli, F. (1999). Visualizing Temporal ClinicalData on the WWW. In Horn, W., Shahar, Y., and et al., editors, Proceedings ofthe Joint European Conference on Arti cial Intelligence in Medicine andMedical Decision Making (AIMDM'99), pages 301-311, Aalborg,Denmark.Springer, Berlin.
[Dragicevic and Huot, 2002]Dragicevic, P. and Huot, S., SpiraClock: A Continuous and Non-IntrusiveDisplay for Upcoming Events, CHI 2002, Interactive Poster: Visualization,2002
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References 2/5
[Francis and Pritchard, 1997]Brian Francis and John Pritchard, Visualisationof historical events usingLexis pencils, Centre for Applied Statistics Fylde College, Lancester University,1997
[Jazayeri et al., 1999]Jazayeri, M., Riva, C. and Gall, H., Visualizing Software Release Histories: TheUse of Color and Third Dimension, Proceedings ICSM'99, Hongji Yang andLee White (Ed.), IEEE Computer Society Press, 1999.
[Kosara and Miksch, 1999]Kosara, R. and Miksch, S. (1999). Visualization Techniques for Time-Oriented,Skeletal Plans in Medical Therapy Planning. In Horn, W., Shahar, Y., Lindberg,G., Andreassen, S., and Wyatt, J., editors, Proceedings of the Joint EuropeanConference on Arti cial Intelligence in Medicine and Medical Decision Making(AIMDM'99), pages 29-300, Aalborg, Denmark. Springer Verlag.
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:68
References 3/5
[Mackinlay et al., 1991]Mackinlay, J. D., Robertson, G. G., AND Card, S. K. 1991. The Perspective Wall:Detail and context smoothly integrated, In Proceedings of CHI ’91. ACM,New York, 173– 179.
[Müller and Schumann, 2003]Müller, W. and Schumann, H., Visualization Methods for Time-DependentData - An Overview, Proceedings of the 2003 Winter Simulation Conference, S.Chick, P.J. Sanchez, D. Ferrin, and D.J. Morrice, eds., 2003
[Muniandy, 2001]
Muniandy, K., Visualizing Time-Related Events for Intrusion Detection, LateBreaking Hot Topics Proceedings, InfoVis 2001
[Plaisant et al., 1996]Plaisant, C., Milash, B., Rose, A., Wido , S., and Shneiderman, B. (1996). LifeLines:Visualizing Personal Histories. In Proceedings CHI'96 ACM Conference onHuman Factors in Computing Systems, pages 221-227, New York. ACM Press.
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WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:69
References 4/5
[Plaisant et al., 1998]Plaisant, C., Mushlin, R., Snyder, A., Li, J., Heller, D., and Shneiderman, B. (1998).LifeLines: Using Visualization to Enhance Navigation and Analysis ofPatient Records. In Proceedings of the 1998 American Medical InformaticAssociation Annual Fall Symposium, pages 76-80.
[Schumann and Müller, 2000]Heidrun Schumann and Wolfgang Müller. Visualisierung - Grundlagen undallgemeine Methoden. Springer Verlag, Heidelberg, 2000
[Schumann and Müller, 2003]Schumann, H and Müller, W, Visualization Methods for Time-DependentData - An Overview, Proceedings of the 2003 Winter Simulation Conference,2003.
[Shneiderman, 1996]Shneiderman, Ben, The Eyes Have It: A Task by Data Type Taxonomy forInformation Visualizations, Proceedings of the IEEE Symposium on VisualLanguages, IEEE Computer Society Press, pp. 336-343, 1996.
WOLFGANG AiGNERVienna University of Technology interactive visualization of temporal data 00:70
References 5/5
[Tominski et al., 2003]Tominski, Ch., Schulze-Wollgast, P. and Schumann, H., Visualisierungzeitlicher Verläufe auf geografischen Karten. Proc. GeoVis’2003,Hannover, 2003, 47-54, in German.
[Tufte, 1983]Tufte, E.R., The Visual Display of Quantitative Informtion, GraphicsPress, Cheshire, Connecticut, USA, 1983.
[Weber et al., 2001]Weber, M., Alexa, M. and Müller, W., Visualizing Time-Series onSpirals, Proc. IEEE Symposium on Information Visualization 2001(InfoVis ‘01), San Diego, USA, 7-13, 2001