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.

1. Hello.
Conversational Architecture on the Internet

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. Who’s Loren?
• @LorenDavie on Twitter
• loren@axilent.com

4. Phase 1: Internet in a Box
www

5. Tipping Point: Introduction of
the iPhone
2007
“Scrolls Like
Butter”

6. Phase 2: Cloud + Devices

7. Another Tipping Point
???

8. Phase 3: Ubiquitous Internet
? ♫
www

9. Adaptive, Personalized,
Contextual
Here’s your coffee,
just the way you like it.
www

10. Five Forces
• Mobile Devices
• Social Media
• Data
• Sensors
• Location

11. Problems

12. Problem 1: No Language
?

13. Problem 2: Privacy Issues

14. Solving Problem #1
Enter
the
Metaphor

15. The Conversation
• Multi-directional
• Multi-modal
• Multi-channel

16. From Metaphor
to Design Language
Conversational
Architecture
Visual
Expression

17. Metaphor to Design
Language
CAVE language
cavelanguage.org

18. CAVE Language
• Whiteboard / Napkin / Presentation -Friendly
• Methodology Neutral
• Scales Up, Scales Down
• Useful Across Disciplines

19. Structure of CAVE language

20. Data
The Foundation of Context

21. Data Origins:
Devices and Sensors

22. Data Origins:
External Data Sources

23. Data Processing

24. User Input

25. Data In a Contextual App

26. User Context
PAGES Analysis

27. Personas

28. Affinity

29. Goals

30. Environment

31. Sentiment

32. Inferences
Converts Data to User Context

33. Inferences
An Inference is made from data

34. Inferences
Usually there is a condition that must be met

35. Inferences
If the condition is met, the user is
associated with the context element.

36. Inferences in a
Contextual App

37. Application Modes
Dynamic Response to User Context

38. Switch

39. Modal Switch for a
Contextual App

40. Modal Switch for a
Contextual App

41. cavelanguage.org

42. Solving Problem #2
• Contextual Apps require User Data
• User Data is sensitive, and can be abused

43. Privacy Debate:
All or Nothing
Surrender all
control of your
personal data
Completely opt
out of contextual
apps vs

44. Data Stewardship
A Framework for Responsible Use of Personal Data

45. Most Problems Come From
Third-Party Access to Data

46. Roles in the Data Ecosystem
Data
Data Producer Data Consumer
Data Citizen
Uses
is the
subject of
Acquires or
Creates

47. Data Policy
The Citizen’s Rules for Their Data

48. Contents of Data Policies
• A Default Rule
• Rules Tied to Letter Grades
• Rules About Specific Data Categories
• Whitelists / Blacklists

49. How do you know data
users will follow the rules?

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. 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. 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. TellTrail: A Data Policy
Repository

54. Thanks!
@LorenDavie
loren@axilent.com
cavelanguage.org
telltrail.me
www.axilent.com