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Slide 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
Slide 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
Slide 3: User profiling and context models collecting data in the Social Web
Slide 4: User profiling and context models collecting data in the Social Web extremely high heterogeneity:
Slide 5: User profiling and context models collecting data in the Social Web extremely high heterogeneity: different data models
Slide 6: User profiling and context models collecting data in the Social Web extremely high heterogeneity: different data models syndications
Slide 7: User profiling and context models collecting data in the Social Web extremely high heterogeneity: different data models syndications auth technologies
Slide 8: User profiling and context models a possible dev process: choose a “social” application: the Beancounter approach
Slide 9: User profiling and context models a possible dev process: choose a “social” application: write code to: the Beancounter approach
Slide 10: User profiling and context models a possible dev process: the Beancounter approach choose a “social” application: write code to: implement the auth policy
Slide 11: User profiling and context models a possible dev process: the Beancounter approach choose a “social” application: write code to: implement the auth policy parse the response
Slide 12: User profiling and context models a possible dev process: the Beancounter approach 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
Slide 13: User profiling and context models a possible dev process: the Beancounter approach 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
Slide 14: User profiling and context models a possible dev process: the Beancounter approach 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
Slide 15: User profiling and context models a possible dev process: the Beancounter approach 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
Slide 16: User profiling and context models a possible dev process: the Beancounter approach 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
Slide 17: User profiling and context models a possible dev process: the Beancounter approach 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
Slide 18: a bit boring, isn’t it?
Slide 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
Slide 20: User profiling and context models the Beancounter approach
Slide 21: User profiling and context models
Slide 22: User profiling and context models the Beancounter architecture The NoTube Beancounter principles: a general architecture with hotpluggable 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
Slide 23: User profiling and context models the Beancounter architecture
Slide 24: User profiling and context models the Beancounter architecture
Slide 25: User profiling and context models the Beancounter architecture
Slide 26: User profiling and context models the Beancounter architecture
Slide 27: User profiling and context models the Beancounter architecture
Slide 28: User profiling and context models the Beancounter architecture
Slide 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
Slide 30: User profiling and context models Beancounter interactions How will Linked Music Explorer interact with an instance of the Beancounter?
Slide 31: User profiling and context models Beancounter interactions How will Linked Music Explorer interact with an instance of the Beancounter?
Slide 32: User profiling and context models Beancounter interactions How will Linked Music Explorer interact with an instance of the Beancounter?
Slide 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?
Slide 34: backup
Slide 35: Architecture sketch