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


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?


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


…travel in timevirtually.

InteractivevisualizationGives us the ability to…


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


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


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


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]


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


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]


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]


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]


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]


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]


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


GANTT charts 1/2

Project management, project planning

Tasks and their temporal attributes (location, duration)

Milestones

Past + present + future

Hierarchical decomposition


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)


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.


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]


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


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


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]


Temporal Objects 2/2

Pros:Simple representation for complex time

annotationsTemporal uncertaintyEasy to comprehend

Cons:Only presentation, no interactionNo direct manipulation


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


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]


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


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]


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]


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:


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]


TimeWheel / Zeitrad 2/2

User interaction:Rotation of variable axes(moving axes of interest into a position parallel to the time axis)


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]


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]



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


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)


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


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Interval dataOnly duration of element

Periodicity unknownAnimation



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


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]


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.


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]


Time-wheel 2/3

Increasing trend Tapering trend

„Prickly fruit“ „Hairy fruit“


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]


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]


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


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


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


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.


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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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.


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

overview - visual analyticsieg.ifs.tuwien.ac.at/~aigner/presentations/tempdatavis03.pdforganization...

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