C3E talk on Navigating Cyberspace, January 2014

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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 …

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)

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  • This was a 15 minute talk at the C3E workshop on navigating cyberspace. I give a brief overview of what works and what doesn’t for visualization. I also talk a bit at the end about ways of scaling things up (in particular, collaboration, crowdsourcing, and machine learning). Some slides in this talk borrowed from Chris Harrison and Jeff Heer
  • Qmeeinfographic on amount of information
  • Yeah, we’re more like this
  • Wonderful book, with a wonderful title that really summarizes the essence of infoviz: using vision to think
  • Here’s an example of infoviz. Can have text instructions. Can also have a map. Note that this map is good in that it shows relationships, distances, etc. However, this map also has a lot of clutter, in terms of too many unimportant streets, text running into each other, and color makes it hard to differentiate between what’s important and what’s not.
  • Compare to Google Maps, they de-emphasize certain roads, emphasize others more, and are better at layout of text labels.
  • Another case study. If you squint, entire map looks red.
  • Compare to this one, shows that America is actually more purple than red. Same data, different representation.
  • Divide things up by county. Can immediately see missing data, as well as distribution of who votes for whom.
  • Distortion view, shows state sizes based on electoral votes.
  • This is by population size, can see that major population areas tend to vote blue.
  • All visualizations have biases. Need fast alternatives to help understand things (so you don’t fool yourself), and you need to realize this when dealing with data.
  • One of the most beautiful visualizations. Note that it’s roughly geographic, but also relational, showing stops relative to each other. Note that the river Thames does not turn at 90 degrees, and it doesn’t show exact distances. The task of a person in the Tube is not about distances, but just relative distances and relative spaces.
  • Some data sets don’t have a natural visualization though. This is an art piece by Ben Fry of processing fame, and while it’s very cool, note that it doesn’t really use “vision to think”, things don’t pop out here.
  • And if there’s too much data, sometimes all you get is a big fat blob
  • This notion of navigating cyberspace probably won’t be successful, because it doesn’t have the same characteristics of a space that we normally think of
  • But just because there might not be a good natural metaphor doesn’t preclude us from trying to build good conceptual models. If you physically open a computer, you won’t find icons, folders, windows, etc, but it’s still a fantastic conceptual model for helping us make use of the power of a computer. (Despite the fact that it’s 40 years old)
  • Slide from Jeff Heer
  • Slide from Jeff Heer
  • Slide from Jeff HeerData is messy (missing in this case), a common problem
  • Slide also from Jeff HeerAlso see Licklider’s quote in Man-Machine Symbiosis, he says something similar
  • http://groups.csail.mit.edu/medg/people/psz/Licklider.html
  • Slide also from Jeff HeerToolchain of work (sort of similar to Clang and LLVM toolchain)
  • Maybe crowdsourcing can help too, this is based on our work on analyzing smartphone apps, to find unusual behaviors
  • Polo Chau, Christos Faloutsos, Jason Hong, NikiKitturA bottom-up approach for understanding graphs with hundreds of thousands of nodes and edgesUses a bottom-up approach, where you start with exemplars, and then uses machine learning algorithms to expand and cluster the graph

Transcript

  • 1. Computational Cybersecurity in Compromised Environments (C3E ) Jan 14, 2014 Computer Human Interaction: Mobility Privacy Security ©2009 Carnegie Mellon University : 1 Making Sense of Navigating Cyberspace Jason Hong
  • 2. • Bandwidth Time ©2014 Carnegie Mellon University : 2 Computing Trends
  • 3. Bandwidth • Storage • Time ©2014 Carnegie Mellon University : 3 Computing Trends
  • 4. Bandwidth • Storage • Computing Power • Time ©2014 Carnegie Mellon University : 4 Computing Trends
  • 5. Bandwidth • Storage • Computing Power • Information • Time ©2014 Carnegie Mellon University : 5 Computing Trends
  • 6. ©2014 Carnegie Mellon University : 6
  • 7. Cognitive Processing • Time ©2014 Carnegie Mellon University : 7 Human Capabilities
  • 8. Cognitive Processing • Visual acuity • Time ©2014 Carnegie Mellon University : 8 Human Capabilities
  • 9. Cognitive Processing • Visual acuity • Human bandwidth … • Time ©2014 Carnegie Mellon University : 9 Human Capabilities
  • 10. 7 2 ©2014 Carnegie Mellon University : 10
  • 11. Evidence suggests it’s more like 4 ©2014 Carnegie Mellon University : 11
  • 12. ©2014 Carnegie Mellon University : 12
  • 13. 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. ©2014 Carnegie Mellon University : 13 The Power of Visualization
  • 14. ©2014 Carnegie Mellon University : 14
  • 15. ©2014 Carnegie Mellon University : 15 The Power of Visualization
  • 16. 1. Aesthetics and color really matter 2. Study what people are trying to do 3. InfoViz is also what you don’t show ©2014 Carnegie Mellon University : 16 Some Lessons
  • 17. ©2014 Carnegie Mellon University : 17 US Election 2004
  • 18. ©2014 Carnegie Mellon University : 18 InfoViz’s Can Show and Hide Info
  • 19. ©2014 Carnegie Mellon University : 19 All Viz’s Show and Hide Info
  • 20. ©2014 Carnegie Mellon University : 20 InfoViz’s Can Show and Hide Info
  • 21. ©2014 Carnegie Mellon University : 21 All Viz’s Show and Hide Info
  • 22. 1. 2. 3. 4. Aesthetics and color really matter Study what people are trying to do Infoviz is also what you don’t show All visualizations have inherent biases ©2014 Carnegie Mellon University : 22 Some Lessons
  • 23. ©2014 Carnegie Mellon University : 23 London Underground Map 1990s
  • 24. ©2014 Carnegie Mellon University : 24 Visualization of DNA by Ben Fry
  • 25. ©2014 Carnegie Mellon University : 25 Visualization of the Internet
  • 26. ©2014 Carnegie Mellon University : 26 Earlier Conceptions of the Net
  • 27. ©2014 Carnegie Mellon University : 27
  • 28. 1. 2. 3. 4. 5. Aesthetics and color really matter Study what people are trying to do Infoviz is also what you don’t show All visualizations have inherent biases May not have natural representations, but can have good conceptual models ©2014 Carnegie Mellon University : 28 Some Lessons
  • 29. Example from Jeff Heer ©2014 Carnegie Mellon University : 29
  • 30. ©2014 Carnegie Mellon University : 30
  • 31. ©2014 Carnegie Mellon University : 31
  • 32. Work by Jeff Heer ©2014 Carnegie Mellon University : 32
  • 33. ©2014 Carnegie Mellon University : 33 About 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
  • 34. ©2014 Carnegie Mellon University : 34
  • 35. 1. 2. 3. 4. 5. Aesthetics and color really matter Study what people are trying to do Infoviz is also what you don’t show All visualizations have inherent biases May not have natural representations, but can have good conceptual models 6. Viz just one part of toolchain ©2014 Carnegie Mellon University : 35 Some Lessons
  • 36. • Many Eyes (by IBM) ©2014 Carnegie Mellon University : 36 Collaborative Analysis?
  • 37. ©2014 Carnegie Mellon University : 37
  • 38. ©2014 Carnegie Mellon University : 38
  • 39. • Pay Mturkers to help find potential problems with smartphone apps ©2014 Carnegie Mellon University : 39 CrowdScanner 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. 90% users were surprised this app sent their precise location to mobile ads providers. 0% users were surprised this app can control camera flashlight.
  • 40. User can specify exemplars of a group Belief Propagation to find more nodes ©2014 Carnegie Mellon University : 40 Combine Data Mining + Viz
  • 41. • Mixed-initiative: Human + Machine I feel like I have a “partnership with the machine” • Builds a highly personalized landscape (unlike automatic methods) ©2014 Carnegie Mellon University : 41 Apolo’s Key Contributions
  • 42. • Considering visualizations 1. 2. 3. 4. 5. Aesthetics and color really matter Study what people are trying to do Infoviz is also what you don’t show All visualizations have inherent biases May not have natural representations, but can have good conceptual models 6. Viz just one part of toolchain • Ongoing research – Collaborative analysis – Machine learning + infoviz ©2014 Carnegie Mellon University : 42 Summary