visualization techniques for analyzing and sharing ... · • a system for exploring large...
TRANSCRIPT
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Watson HealthCognitive Visualization Lab
Cody [email protected]
ibm.biz/cogvislab
November 9, 2015
Visualization Techniques for Analyzing and
Sharing Relational Data
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Visualization Techniques …. Relational DataAgenda
Introduction VoroGraph GraphTrail StoryFacetsNode-Link Discussion
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Cody Dunne, PhD – Cognitive Visualization RSMWeb: ibm.biz/codydunne Email: [email protected]
epidemiology/dynamic networks
layout readability exploration provenance network type overviews group/set visualization
aggregation techniques literature exploration news term occurrence computer network traffic
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The Data Problem
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Why Visualization?Anscombe’s quartet – Table
I II III IV
x y x y x y x y
10.00 8.04 10.00 9.14 10.00 7.46 8.00 6.58
8.00 6.95 8.00 8.14 8.00 6.77 8.00 5.76
13.00 7.58 13.00 8.74 13.00 12.74 8.00 7.71
9.00 8.81 9.00 8.77 9.00 7.11 8.00 8.84
11.00 8.33 11.00 9.26 11.00 7.81 8.00 8.47
14.00 9.96 14.00 8.10 14.00 8.84 8.00 7.04
6.00 7.24 6.00 6.13 6.00 6.08 8.00 5.25
4.00 4.26 4.00 3.10 4.00 5.39 19.00 12.50
12.00 10.84 12.00 9.13 12.00 8.15 8.00 5.56
7.00 4.82 7.00 7.26 7.00 6.42 8.00 7.91
5.00 5.68 5.00 4.74 5.00 5.73 8.00 6.89
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Why Visualization?Anscombe’s quartet – Statistics & Visualization
Property in Each Case
Value Equality
Mean of x 9 Exact
Variance of x 11 Exact
Mean of y 7.502 decimal places
Variance of y4.122 or 4.127
3 decimal places
Correlation between x & y
0.8163 decimal places
Linear regression line
y = 3.00 +0.500x
2 & 3 decimalplaces
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No catalogue of techniques can convey a willingness to look for what can be seen, whether or not anticipated. Yet this is at the heart of exploratory data analysis. ... the picture-examining eye is the best finder we have of the wholly unanticipated.
– Tukey, 1980
Why Visualization?Tukey
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Design ChoicesCharm vs. clarity
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Design ChoicesCharm vs. clarity
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Data Analysis Process
Kandel et al., 2012 Heer, 2013 (unpublished)
Discovery Acquisition
WranglingCleaning
Integration
Profiling Visualization
Modeling Modeling
ReportingPresentation
Dissemination
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Data Analysis Process
Acquisition
Cleaning
Integration
Visualization
Modeling
Presentation
Dissemination
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Node-Link Network Visualizations
Introduction VoroGraph GraphTrail StoryFacetsNode-Link Discussion
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Node 1 Node 2
Alice Bob
Alice Cathy
Cathy Alice
Node-Link Graph VisualizationGeneral
Graph ≈ NetworkNode ≈ Vertex ≈ EntityEdge ≈ Link ≈ Relationship ≈ Tie
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# User 1 User 2 # User 1 User 2
1 20andlife barrywellman 15 danevans87 informor
2 20andlife BrianDavidson 16 danevans87 NetSciWestPoint
3 barrywellman elizabethmdaly 17 danielequercia BrianDavidson
4 barrywellman informor 18 danielequercia drewconway
5 BrianDavidson hcraygliangjie 19 danielequercia ipeirotis
6 BrianDavidson informor 20 danielequercia johnflurry
7 BrianDavidson NetSciWestPoint 21 danielequercia loyan
8 byaber barrywellman 22 danielequercia loyan
9 byaber danielequercia 23 danielequercia mcscharf
10 byaber mcscharf 24 danielequercia NetSciWestPoint
11 chrisnordyke RebeccaBadger … … …
12 danevans87 barrywellman 106 sechrest Japportreport
13 danevans87 BrianDavidson 107 sechrest loyan
14 danevans87 drewconway 108 sechrest RebeccaBadger
Node-Link Network VisualizationTweets of the #Win09 Workshop
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Node-Link Network VisualizationTweets of the #Win09 Workshop
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Sociology Scientometrics Politics Urban Planning
Biology Archaeology WWW
Node-Link Network VisualizationWho uses network analysis?
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Network Visualization in IBMServices exposing network data (many more coming)
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Alternate Network Visualizations
Dunne et al., 2012 Gove et al., 2011 Blue et al., 2008
Henry & Fekete, 2006 Freire et al., 2010 Wattenberg, 2006
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VoroGraph
Introduction VoroGraph GraphTrail StoryFacetsNode-Link Discussion
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GLEAM Epidemic ModelPopulation basins, local commuting, and global flights
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Choropleth MapsGastner and Newman 2012 Election (State)
http://www-personal.umich.edu/~mejn/election/2012/
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Area MorphingGastner and Newman 2012 Election (State)
http://www-personal.umich.edu/~mejn/election/2012/
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Area MorphingGastner and Newman 2012 Election (County)
http://www-personal.umich.edu/~mejn/election/2012/
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Contiguous Density Equalizing CartogramsGastner and Newman 2012 Election (County)
http://www-personal.umich.edu/~mejn/election/2012/
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Voronoi TessellationWestern Europe
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Centroidal Voronoi Tessellation – Animated!Western Europe
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VoroGraphAnimated Transitions
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VoroGraphInterface Demo
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VoroGraphContiguous Edge Coding
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VoroGraphData Squares
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VoroGraphNon-Contiguous Edge Coding
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VoroGraphNon-Contiguous Edge Coding
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VoroGraphForce-Directed Group-in-a-Box
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• Equal-population hexagons discretize the space for countability
• Easier attribute comparison with color/size coding
• Hexagons make clear it is an artificial representation
• Enforces a degree of generalization
• Contiguous relationship display
Dunne C, Muller M, Perra N, and Martino M. (2015) “VoroGraph: Visualization Tools for Epidemic Analysis”, In CHI '15 Interactivity. DOI:10.1145/2702613.2725459
VoroGraphDiscussion
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GraphTrail
Introduction VoroGraph GraphTrail StoryFacetsNode-Link Discussion
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Networks can be• Large &
complex• Multivariate• Heterogeneous
Analysis can take• Many sessions• Many users
Node-Link Visualization is Hard!
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• Aggregation
• Drag-and-drop interactions
• Integrated exploration history
GraphTrailOverview
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GraphTrailAggregating charts
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GraphTrailDrag and drop interaction
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Municipality = Écija OR Marchena
GraphTrailProvenance/history visualization
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Tag CloudDig Sites
Grouped by Ceramic
Component
Municipality = Écija OR
Marchena
GraphTrailProvenance & chart parameterization
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part ofauthor
cites
P1A1
A2 A3
P2
Author• Name• Affiliation• Paper.Topic*
Paper• Topic• Year
Proceeding• Country• Date
GraphTrailPivoting & Derived Attributes
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• GraphTrail can make the same findings as other tools
– And more!
• New users can make findings
• New users understand the exploration history
– And usually motivation!
GraphTrailLab study
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Brughmans, T., et al., 2011
“How were Iron-Age communities integrated into the political and economic structure of the Roman Empire?”
“How were urban social hierarchies within the Roman provinces structured and articulated?”
Field Study With Archaeologists
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. . .
GraphTrailField study – current practice
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GraphTrailField study – analyses
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1. Number of nodes, edges, types
2. Number of charts20 – 30 per session
Nodes Types Edges Types
CHI 10K 3 20K 3+
Archaeology 13K 24 20K 35
GraphTrailScalability
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• A system for exploring large multivariate, heterogeneous networksusing aggregation by node and edge attributes,
• A method for capturing a user’s exploration history and integrating it directly into the workspace, and
• A longitudinal field study and a qualitative lab study that prove the utility of these approaches.
Dunne C, Riche NH, Lee B, Metoyer RA and Robertson GG (2012), "GraphTrail: Analyzing large multivariate, heterogeneous networks while supporting exploration history", In CHI '12. pp. 1663-1672. DOI:10.1145/2207676.2208293
Riche N, Lee B and Dunne C (2011), "Interactive visualization for exploring multi-modal, multi-relational, and multivariate graph data". U.S. Patent Application No. (13/041474).
GraphTrailDiscussion
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StoryFacets
Introduction VoroGraph GraphTrail StoryFacetsNode-Link Discussion
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StoryFacetsIndividual exploration
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StoryFacetsReal-world exploratory analysis
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StoryFacetsPirolli and Card sensemaking loop
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I am your database.
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StoryFacetsStar Wars data
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StoryFacetsOverview
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StoryFacetsTrail facet – Star Wars trilogy ships & pilots
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StoryFacetsDashboard facet – Star Wars male vs. female
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StoryFacetsInfographic facet – Star Wars Jabba the Hutt
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StoryFacetsStory (slideshow) facet – Star Wars character height
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StoryFacetsLinked back to trail facet
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• 19 casual participants in a public setting
– Novice users were able to select different communication format based on the target audience and nature of the content
• Three visualization professional expert reviewers
StoryFacetsEvaluation
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• Exploratory data analysis is much more than the initial exploration session
• Unified platform to support exploration, collaboration, discussion, and presentation
• Study with casual participants and expert reviewers reveal future directions
StoryFacetsDiscussion
Park DG, Dunne C, Ragan E, Elmqvist N (2015) “StoryFacets: Generating Multiple Representations of Exploratory Data Analysis for Communication”, Under submission.
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Discussion
Introduction VoroGraph GraphTrail StoryFacetsNode-Link Discussion
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Graph Drawing 2017Hosted in September by IBM
Cambridge, MA, USA
Cody Dunne, IBM [email protected]
T. Alan Keahey, IBM Watson
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• Additional visualizations
• Context aware comments
• Exploration hints for new paths
• Streaming/temporal data, intelligent updates, and resurgent relevancy
• Linked chart parameterization for comparisons with auto chain layout and compression
• Advanced modeling and analytics (IBM Catalyst, IBM Watson)
• User management & security
GraphTrail & StoryFacetsBlocks to build on