The 3rd Project Meeting focused on: 1) Providing a 10-minute update on the status of Work Package 3 regarding user profiling and context models. 2) Presenting the Beancounter approach for collecting data from social web applications in a standardized way. 3) Demonstrating how the Beancounter allows uploading social data from an application like BrightKite and storing user profiles for applications like the Linked Music Explorer to access.

1. 3rd Project Meeting - 16/09/2009 @ Amsterdam
The Beancounter:
collecting data from the
Social Web
a ten-minutes long update on the WP3 status
Davide Palmisano, Michele Minno and Michele Mostarda

2. User profiling and context models
a (very) short ToC
Where we are
user data gathering in the Social Web
the NoTube Beancounter: a general approach
a simple demonstration
Where we are going
Linked Music Explorer and the Beancounter

3. User profiling and context models
collecting data in
the Social Web

4. User profiling and context models
collecting data in
the Social Web
extremely high heterogeneity:

5. User profiling and context models
collecting data in
the Social Web
extremely high heterogeneity:
different data models

6. User profiling and context models
collecting data in
the Social Web
extremely high heterogeneity:
different data models
syndications

7. User profiling and context models
collecting data in
the Social Web
extremely high heterogeneity:
different data models
syndications
auth technologies

8. User profiling and context models
the Beancounter
approach
a possible dev process:
choose a “social” application:

9. User profiling and context models
the Beancounter
approach
a possible dev process:
choose a “social” application:
write code to:

10. User profiling and context models
the Beancounter
approach
a possible dev process:
choose a “social” application:
write code to:
implement the auth policy

11. User profiling and context models
the Beancounter
approach
a possible dev process:
choose a “social” application:
write code to:
implement the auth policy
parse the response

12. User profiling and context models
the Beancounter
approach
a possible dev process:
choose a “social” application:
write code to:
implement the auth policy
parse the response
translate it in RDF and store it
repeat for all the stuff in the Social Web

13. User profiling and context models
the Beancounter
approach
a possible dev process:
choose a “social” application:
write code to:
implement the auth policy
parse the response
translate it in RDF and store it
repeat for all the stuff in the Social Web

14. User profiling and context models
the Beancounter
approach
a possible dev process:
choose a “social” application:
write code to:
implement the auth policy
parse the response
translate it in RDF and store it
repeat for all the stuff in the Social Web

15. User profiling and context models
the Beancounter
approach
a possible dev process:
choose a “social” application:
write code to:
implement the auth policy
parse the response
translate it in RDF and store it
repeat for all the stuff in the Social Web

16. User profiling and context models
the Beancounter
approach
a possible dev process:
choose a “social” application:
write code to:
implement the auth policy
parse the response
translate it in RDF and store it
repeat for all the stuff in the Social Web

17. User profiling and context models
the Beancounter
approach
a possible dev process:
choose a “social” application:
write code to:
implement the auth policy
parse the response
translate it in RDF and store it
repeat for all the stuff in the Social Web

18. a bit boring, isn’t it?

19. User profiling and context models
the Beancounter
approach
instead, what I really want is:
a framework that allows me to reduce at
minimum the development effort
a general architecture that
embraces the heterogeneity
allowing a decoupled and third
party development

20. User profiling and context models
the Beancounter
approach

21. User profiling and context models

22. User profiling and context models
the Beancounter
architecture
The NoTube Beancounter principles:
a general architecture with hot-
pluggable components (tubelets and
modelets)
an engine that allows to extract and
aggregate users social data
representing the data with RDF and
storing them in a preferred triple store
fully accessible with a set of REST APIs

23. User profiling and context models
the Beancounter
architecture

24. User profiling and context models
the Beancounter
architecture

25. User profiling and context models
the Beancounter
architecture

26. User profiling and context models
the Beancounter
architecture

27. User profiling and context models
the Beancounter
architecture

28. User profiling and context models
the Beancounter
architecture

29. User profiling and context models
What you are
going to see
a quick demo around the following scenario:
an instance of the Beancounter is running
an administrator wrote a Tubelet for
BrightKite and want to upload it to the
Beancounter
Davide wants to let the Beancounter
storing his data from his Brightkite account

30. User profiling and context models
Beancounter
interactions
How will Linked Music Explorer interact
with an instance of the Beancounter?

31. User profiling and context models
Beancounter
interactions
How will Linked Music Explorer interact
with an instance of the Beancounter?

32. User profiling and context models
Beancounter
interactions
How will Linked Music Explorer interact
with an instance of the Beancounter?

33. User profiling and context models
Further details
architecture
how the Beanconter interacts with other
components?
what kind of APIs?
recommendation
how to use the “beans” to provide
content recommendation?

34. backup

35. Architecture
sketch