Interaction and visual analytics 1 interaction and visual analytics Wolfgang Aigner [email protected]http://ieg.ifs.tuwien.ac.at/~aigner/ [email protected]http://ike.donau-uni.ac.at/~aigner/ Version 1.1 11.12.2008 Frederic Eyl and Gunnar Green, Aperture, University of the Arts, Berlin, 2004-05, http://www.fredericeyl.de/aperture/
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WOLFGANG AiGNER Interaction and visual analytics 1
WOLFGANG AiGNER Interaction and visual analytics 4
InfoVis & Interaction
Two main components:Visual representation
Interaction
Main focus of current research: finding novelvisual representations
BUT: Increasing interest in interactionRelated fields: Human-Computer Interaction
(HCI), Interaction Design
WOLFGANG AiGNER Interaction and visual analytics 5
InfoVis Reference Model[Card et al., 1999]
User interaction can feed back into any level
WOLFGANG AiGNER Interaction and visual analytics 6
Why interaction?
„Interaction between human and computer is at the heart ofmodern information visualization and for a single overridingreason: the enormous benefit that can accrue from being able tochange one's view of a corpus of data. Usually that corpus is solarge that no single all-inclusive view is likely to lead to insight.Those who wish to acquire insight must explore, interactively,subsets of that corpus to find their way towards the view thattriggers an 'a ha!' experience.“
[Spence, 2007]
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WOLFGANG AiGNER Interaction and visual analytics 8
I hear and I forget.I see and I remember.I do and I understand.
Confucius
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Interaction facilitates activediscourse with the data
see think
modify
[Card et al., 1983]
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Response Time
.1 secAnimation, visual continuity, sliders
1 secSystem response, conversation break
10 secCognitive response
[Stasko, 2006, Lecture Slides]
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Interaction levels
Conceptual levelWhat to be done?e.g. scrolling / navigating--> Task
Control levelHow can it be carried out by the user?e.g. move scrollbar--> User interface
Physical levelHow does the user physically interact?e.g. mouse wheel, touch screen--> Interaction devices
WOLFGANG AiGNER Interaction and visual analytics 12
WOLFGANG AiGNER Interaction and visual analytics 25
Dynamic Queries
InteractiveSearch
[Shneiderman, 1994 ff]
WOLFGANG AiGNER Interaction and visual analytics 26
Dynamic Queries (cont.)[Shneiderman, 1994 ff]
Details on Demand
WOLFGANG AiGNER Interaction and visual analytics 27
RangeSlider[Shneiderman, 1994 ff]
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Used to rapidly scan through and select from lists ofalphanumeric data
Small-sized widget to search sorted lists
Letter index visualizing the distribution of initial letters -jump to a position in the slider
Locating an items out of a list of 10,000 items ~ 28s fornovice users
AlphaSlider[Ahlberg and Shneiderman, 1994]
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Data Visualization Sliders
Data distribution is shown within control
[Eick, 1994]
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Dynamic HomeFinder[Shneiderman, 1994 ff]
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SpotfireChristopher Ahlberg
1991: Visiting student from Sweden at the HCIL University of Maryland
1996: Founder of Spotfire
2007: Spotfire was sold for 195 Mio. $
WOLFGANG AiGNER Interaction and visual analytics 32
Online examples
Immobilien Suchehttp://immo.search.ch/
Diamond Searchhttp://www.bluenile.com
Amazon.com search via Treemap (Hive Group)http://www.hivegroup.com/gallery/galleryapps_amazon
.html
Spotfire Holiday Gift Finderhttp://spotfire.tibco.com/testdrive/holidays/
WOLFGANG AiGNER Interaction and visual analytics 33
Dynamic Queries Summary
Users can rapidly, safely playfully explore a data space - nofalse input possible
Users can rapidly generate new queries based on incidental learningVisual representation of data supports data explorationAnalysis by continuously developing and testing hypotheses (detect clusters,
outliers, trends in multivariate data)Provides straightforward undo and reversing of actions
Potential problemsLimit of query complexity - filters are always conjunctivePerformance is limited for very large data sets and client / server applicationsControls require valuable display spaceControls must be fixed in advanceInformation is prunedOnly single range queries and single selection in the AlphasliderOperations are global in scope
[Büring LVA, 2007]
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Magic Lenses, Movable Filters
Arbitrarily shaped area of an object and to manipulate this area with specificoperators
cover only a part of the object
Can be overlaid and combined
Combination with Dynamic Queries [Fishkin & Stone 1995]
[Bier et al., 1993, Stone et al, 1994]
WOLFGANG AiGNER Interaction and visual analytics 35
Revisiting the InfoVisReference Model [Card et al., 1999]
User interaction can feed back into any level
WOLFGANG AiGNER Interaction and visual analytics 36
Interaction devicesKeyboard devices
Pointing devicesDirect control devices
easy to learn and use, but hand may obscure display
e.g. Lightpen; Touchscreen; Stylus
Indirect control devices
takes time to learn
e.g. Mouse; Trackball; Joystick;Touchpad; Graphics tablet
Novel devices and strategies
special purposes
e.g. Foot controls; Eye tracking; 3D trackers; DataGloves; Boom Chameleon; Haptic
feedback; Tangible user interfaces; Digital paper
Speech and auditory interfaces
Displays
Printers
[Shneiderman and Plaisant, 2005]
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Connecting Time-OrientedData and Information to a Coherent
Interactive Visualization
Ragnar Bade, Stefan Schlechtweg
Silvia Miksch
The Midgaard Project
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Aims
DataHigh-Dimensional and Time-Oriented Data and
Information
Interactive Visualization TechniquesReveal the Data at Several Levels of Detail and
Abstraction, Ranging from a Broad Overview to theFine Structure
Time Visualization and Navigation TechniqueConnects Overview+Detail, Pan+Zoom, and
Focus+Context Features to one PowerfulTime-Browser
[Miksch LVA]
WOLFGANG AiGNER Interaction and visual analytics 39
Midgaard Approach
Visualizing Time-Oriented DataQualitative Scales
Qualitative/Quantitative Hybrids
Quantitative Scales
Data Points & Their Dimension
High-Frequency Data
Interacting with DataBrowsing Data
Browsing Over Time
Semantic
ZoomingSmoothly integrated
[Miksch LVA]
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Qualitative Scales
Color-Coded Timelines
Height-Coded Timelines
[Miksch LVA]
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Qualitative-QuantitativeHybridsColor-Coded Regions
Mark Regions without Colors
[Miksch LVA]
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Quantitative Scales
Read Exact Values
Include Knowldge of Qualitative Scales
[Miksch LVA]
WOLFGANG AiGNER Interaction and visual analytics 43
Points and theirDimensionsOccurrence Time & Uncertainty
Valid Time
Deviations
Trustability
[Miksch LVA]
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High-Frequency DataAbstract vs. Expressiveness
Information Mural [Jerding & Stasko, 1998]
Tukey’s Box-Plot Redesign
[Miksch LVA]
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Interacting with Data & Time[Miksch LVA]
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WOLFGANG AiGNER Interaction and visual analytics 70
Part Bvisual analytics
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Motivation: Main Problems
Data Unmanageable –Loss of Overview
Missing Integration ofVarious (Heterogeneous)
Information Sources
VariousInterdisciplinary Methods
Missing Involvement of
Users and their Tasks
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Screen Resolution: 1024 * 768 = 786.432
Measurements of Water Level in LA Every Year:1 5.256.000
Number of Cellular Phones in Austria (2005):2 8.160.000
Transmitted Emails Every Hours (World-Wide):3 35.388.000
Whole Data often not PresentableApplying Analytical Methods
(Data Reduction)
Visualization of Most Important Dataand Information
Analytical MethodsStatistics, Machine Learning & Data Mining
Analytical Methods
1 ... Amt der NÖ Landesregierung, Abt. WA5 - Hydrologie, http://www.noel.gv.at/SERVICE/WA/WA5/htm/wnd.htm, Accessed: 11.1.20072 ... CIA Factbook, https://www.cia.gov/cia/publications/factbook/, Accessed: 11.1.20073 ... How Much Information?, UC Berkeley, http://www2.sims.berkeley.edu/research/projects/how-much-info-2003/, Accessed: 11.1.2007
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Visual Analytics – What is it?James Thomas & Kristin A. Cook:
NVAC (National Visualization and Analytics Center), Seattle, USA
„Visual Analyticsis the science ofanalytical reasoningfacilitated byinteractivevisual interfaces”
[Thomas & Cook 2005]
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Visual Information Seeking Mantra
overview first, zoom and filter, then details-on-demand
overview first, zoom and filter, then details-on-demand
overview first, zoom and filter, then details-on-demand
overview first, zoom and filter, then details-on-demand
overview first, zoom and filter, then details-on-demand
overview first, zoom and filter, then details-on-demand
... 10 times ...
[Shneiderman, 1996]
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Visual Analytics Mantra
Analyze first,show the important,
zoom filter & analyze, then details-on-demand
Analyze first,show the important,
zoom filter & analyze, then details-on-demand
Analyze first,show the important, …
... 10 times ...
[Keim, 2005, presentation]
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Application AreasEconomic & Business Data
Business IntelligenceMarket Analysis
Medicine & BiotechnologyPatients’ Data ManagementEpidemiologyGenetics
Security & Risk ManagementDisaster ManagementComputer NetworksTransportationReducing Crime and Terror RateFraud Detection
Environment & Climate Research
etc.
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Stock Prices[Hochheiser, 2003]
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WireVis -Anti Money Laundering
[Chang et al., 2007]
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Useful resourcesBooks
[Shneiderman and Plaisant, 2005] Ben Shneiderman and Catherine Plaisant,Designing the User Interface, 4th Edition, Pearson Education, 2005.
[Spence, 2007] Robert Spence, Information Visualization - Design for Interaction, 2ndEdition, Pearson Education Limited, Essex, UK, 2007
[Cooper et al., 2007] Alan Cooper, Robert Reimann, and David Cronin, About Face 3- The Essentials of Interaction Design, Wiley Publishing, Indianapolis, IN, USA, 2007.
[Sharp et al., 2007] Helen Sharp, Yvonne Rogers, and Jenny Preece, InteractionDesign - beyond human-computer interaction, 2nd Edition, John Wiley & Sons, WestSussex, UK, 2007.
[Tidwell, 2006] Jenifer Tidwell, Designing Interfaces - Patterns for Effective InteractionDesign, O'Reilly Media, Sebastopol, CA, USA, 2006.
Web Lecture by John Staskohttp://weblectures.cc.gatech.edu/videolectures/7450_Interaction_files/intro.htm
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Acknowledgements
Thanks to Silvia Miksch whose slides form the basis of thispresentation.
Ideas have been taken from John Stasko’s and ThorstenBüring’s lecture slides for their visualization classes.