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Conversational Architecture, CAVE Language, Data Stewardship

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These are the slides from the presentation I gave at the Semiotics Web meetup group on Nov 1st 2014. In this talk I discussed the emergency of the ubiquitous Internet, how to discuss the design of contextual apps, and presented an approach to privacy concerns that are inherently connected.

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Conversational Architecture, CAVE Language, Data Stewardship

  1. 1. Hello. Conversational Architecture on the Internet
  2. 2. Who’s Loren? • Founder / CEO of Axilent • Makes ACE - the Adaptive Context Engine • User Profiling and Dynamic, Personalized Content Targeting • Former Director of Technology at digital agencies HUGE and Alexander Interactive • Python hacker
  3. 3. Who’s Loren? • @LorenDavie on Twitter • loren@axilent.com
  4. 4. Phase 1: Internet in a Box www
  5. 5. Tipping Point: Introduction of the iPhone 2007 “Scrolls Like Butter”
  6. 6. Phase 2: Cloud + Devices
  7. 7. Another Tipping Point ???
  8. 8. Phase 3: Ubiquitous Internet ? ♫ www
  9. 9. Adaptive, Personalized, Contextual Here’s your coffee, just the way you like it. www
  10. 10. Five Forces • Mobile Devices • Social Media • Data • Sensors • Location
  11. 11. Problems
  12. 12. Problem 1: No Language ?
  13. 13. Problem 2: Privacy Issues
  14. 14. Solving Problem #1 Enter the Metaphor
  15. 15. The Conversation • Multi-directional • Multi-modal • Multi-channel
  16. 16. From Metaphor to Design Language Conversational Architecture Visual Expression
  17. 17. Metaphor to Design Language CAVE language cavelanguage.org
  18. 18. CAVE Language • Whiteboard / Napkin / Presentation -Friendly • Methodology Neutral • Scales Up, Scales Down • Useful Across Disciplines
  19. 19. Structure of CAVE language
  20. 20. Data The Foundation of Context
  21. 21. Data Origins: Devices and Sensors
  22. 22. Data Origins: External Data Sources
  23. 23. Data Processing
  24. 24. User Input
  25. 25. Data In a Contextual App
  26. 26. User Context PAGES Analysis
  27. 27. Personas
  28. 28. Affinity
  29. 29. Goals
  30. 30. Environment
  31. 31. Sentiment
  32. 32. Inferences Converts Data to User Context
  33. 33. Inferences An Inference is made from data
  34. 34. Inferences Usually there is a condition that must be met
  35. 35. Inferences If the condition is met, the user is associated with the context element.
  36. 36. Inferences in a Contextual App
  37. 37. Application Modes Dynamic Response to User Context
  38. 38. Switch
  39. 39. Modal Switch for a Contextual App
  40. 40. Modal Switch for a Contextual App
  41. 41. cavelanguage.org
  42. 42. Solving Problem #2 • Contextual Apps require User Data • User Data is sensitive, and can be abused
  43. 43. Privacy Debate: All or Nothing Surrender all control of your personal data Completely opt out of contextual apps vs
  44. 44. Data Stewardship A Framework for Responsible Use of Personal Data
  45. 45. Most Problems Come From Third-Party Access to Data
  46. 46. Roles in the Data Ecosystem Data Data Producer Data Consumer Data Citizen Uses is the subject of Acquires or Creates
  47. 47. Data Policy The Citizen’s Rules for Their Data
  48. 48. Contents of Data Policies • A Default Rule • Rules Tied to Letter Grades • Rules About Specific Data Categories • Whitelists / Blacklists
  49. 49. How do you know data users will follow the rules?
  50. 50. telltrail.me • A kind of “Better Business Bureau” for data users • Holds repositories of citizen data policies • Provides certification marks for compliant data users (letter grades) to let citizens know they are trustworthy
  51. 51. Letter Grades • Like NYC Restaurant health letter grades • Indicates the level of compliance of the data user organization • Lets citizens know the data user organization is trustworthy
  52. 52. Letter Grades • A: Audited and Verified adherence to Data Polices for both internally created and externally sourced data. • B: Adherence to Data Policies for both internally created and externally sourced data. • C: Adherence to Data Policies for just externally sourced data.
  53. 53. TellTrail: A Data Policy Repository
  54. 54. Thanks! @LorenDavie loren@axilent.com cavelanguage.org telltrail.me www.axilent.com

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