c3e talk on navigating cyberspace, january 2014
DESCRIPTION
A brief 15 minute overview of what does and doesn't work in information visualization, plus a brief discussion of how to address issues of scale (collaborative analysis, crowdsourcing, machine learning)TRANSCRIPT
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Navigating Cyberspace
Computational Cybersecurity in Compromised Environments (C3E )Jan 14, 2014
Jason Hong
ComputerHumanInteraction:MobilityPrivacySecurity
Making Sense of
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• Bandwidth
Time
Computing Trends
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• Bandwidth• Storage
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Computing Trends
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Time
Computing Trends
• Bandwidth• Storage• Computing Power
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Time
Computing Trends
• Bandwidth• Storage• Computing Power• Information
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• Cognitive Processing
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Human Capabilities
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• Cognitive Processing• Visual acuity
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Human Capabilities
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• Cognitive Processing• Visual acuity• Human bandwidth
…
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Human Capabilities
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7 2
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Evidence suggests it’s more like 4
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The Power of Visualization
1. Start out going Southwest on ELLSWORTH AVE Towards BROADWAY by turning right. 2: Turn RIGHT onto BROADWAY. 3. Turn RIGHT onto QUINCY ST. 4. Turn LEFT onto CAMBRIDGE ST. 5. Turn SLIGHT RIGHT onto MASSACHUSETTS AVE. 6. Turn RIGHT onto RUSSELL ST.
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The Power of Visualization
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Some Lessons
1. Aesthetics and color really matter2. Study what people are trying to do3. InfoViz is also what you don’t show
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US Election 2004
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InfoViz’s Can Show and Hide Info
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All Viz’s Show and Hide Info
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InfoViz’s Can Show and Hide Info
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All Viz’s Show and Hide Info
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Some Lessons
1. Aesthetics and color really matter2. Study what people are trying to do3. Infoviz is also what you don’t show4. All visualizations have inherent biases
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London Underground Map 1990s
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Visualization of DNA
by Ben Fry
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Visualization of the Internet
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Earlier Conceptions of the Net
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Some Lessons
1. Aesthetics and color really matter2. Study what people are trying to do3. Infoviz is also what you don’t show4. All visualizations have inherent biases5. May not have natural representations,
but can have good conceptual models
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Example from Jeff Heer
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Work by Jeff Heer
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33About 85 per cent of my
"thinking" time was spent getting into a position to think, to make a decision…Much more time went into finding or obtaining information than into digesting it… When the graphs were finished, the relations were obvious at once, but the plotting had to be done in order to make them so.
- J.C.R. Licklider, 1960
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Some Lessons
1. Aesthetics and color really matter2. Study what people are trying to do3. Infoviz is also what you don’t show4. All visualizations have inherent biases5. May not have natural representations,
but can have good conceptual models6. Viz just one part of toolchain
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Collaborative Analysis?
• Many Eyes (by IBM)
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CrowdScanner
• Pay Mturkers to help find potential problems with smartphone apps
90% users were surprised this app sent their precise location to mobile ads providers.
95% users were surprised this app sent their approximate location to mobile ads providers.
95% users were surprised this app sent their phone’s unique ID to mobile ads providers.
See all
0% users were surprised this app can control camera flashlight.
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Combine Data Mining + Viz
User can specify exemplars of a groupBelief Propagation to find more nodes
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Apolo’s Key Contributions
• Mixed-initiative: Human + Machine
• Builds a highly personalized landscape (unlike automatic methods)
I feel like I have a “partnership with the
machine”
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Summary
• Considering visualizations1. Aesthetics and color really matter2. Study what people are trying to do3. Infoviz is also what you don’t show4. All visualizations have inherent biases5. May not have natural representations,
but can have good conceptual models6. Viz just one part of toolchain
• Ongoing research– Collaborative analysis– Machine learning + infoviz