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GVIS: a framework for graphical mashups of heterogeneous sources to support data interpretation


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This paper introduces the GVIS framework and describes one of its applications built in support of user profile awareness. This application is aimed at opening part of users' profiles to their inspection by exploiting a graphical representation of their personal data. We developed an infrastructure for presenting these high level information in a configurable and adaptable way. The framework we developed is able to retrieve data from heterogeneous sources just by writing a small adapter and allows us to mix together different streams through an XML configuration that relies on a set of operations for elicitation of the most interesting fragments. The final goal is to provide an easily readable graphical representation of the most relevant information, in order to support the human visual system, more capable to have an overview with this kind of solution than with text. As an example application we have mashed up URLs from user browsing history with tags coming from the resulting output, represented as a pie chart, shows the most relevant subjects followed by a user. Some open issues and problems, we hope to research next, are presented in the conclusion part.

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GVIS: a framework for graphical mashups of heterogeneous sources to support data interpretation

  1. 1. Luca Mazzola, Davide Eynard and Riccardo Mazza USI - University of Lugano, Switzerland Faculty of Communication Sciences Institute for Communication Technologies HSI 2010 – Rzeszów, PL - 13 May 2010 GVIS: a framework for graphical mashups of heterogeneous sources to support data interpretation
  2. 2. Agenda <ul><li>Introduction & Context
  3. 3. Aims
  4. 4. Infrastructure
  5. 5. Implemented operators pipelines
  6. 6. Advantages
  7. 7. Application 1: tag for a website
  8. 8. Application 2: “areas” of interest of the user
  9. 9. Conclusions and Next steps </li></ul>
  10. 10. Introduction & Context <ul><li>ELearning and Technology Enhanced Learning
  11. 11. Customization [1] vs. Personalization [2] </li></ul><ul><ul><li>Based on explicitly provided preferences
  12. 12. Automatic process, out of user interaction </li></ul></ul><ul><li>In [2]: perception of user about this process: </li></ul><ul><ul><li>Trustiness and correctness of the calculated profile
  13. 13. Participation of the user </li></ul></ul><ul><li>Opening learner profile to self-inspection seems to help in </li></ul><ul><ul><li>gaining trust about the model
  14. 14. Promoting spontaneous participation in model check </li></ul></ul>
  15. 15. General Aims (the GVIS tool) Support user profile awareness through A graphical representation of their personal data in a Configurable way Gaining the trust and incentive the participation
  16. 16. Specific Aims (the two experiments) Applying GVIS infrastructure to: <ul><ul><li>One or more sets of URL
  17. 17. A URL classification </li></ul></ul>Mashing up data from heterogeneous source User profile -> areas of interest [as tag frequency] Why global URL and folksonomy instead of internal resource with ontology -> general approach
  18. 18. Infrastructure 3 layers (plus its XML configur.): <ul><ul><li>Extractor
  19. 19. Aggregator (with a set of out-of-the-box operators)
  20. 20. Builder </li></ul></ul>Heterogeneous sources
  21. 21. Implemented operators pipelines Retrieve tags for an URL with more than H occurrences Retrieve tags for a user: with more than Z occurrences on every domain with more than K entry in DB
  22. 22. Advantages <ul><li>A general purpose user-related visualization infrastructure
  23. 23. Configurable through simple XML files
  24. 24. Use of heterogeneous data source
  25. 25. No need of data consolidation (fully external sources)
  26. 26. Use of folksonomy to classify </li></ul>
  27. 27. Application 1: tag for a website
  28. 28. Application 2 : “areas” of interest of the user <ul><li>finance, stock marketing
  29. 29. trekking and montains (checking meteo) </li></ul><ul><li>watch streaming video
  30. 30. using social network </li></ul>
  31. 31. Pie chart drawbacks ... <ul><li>Unreadable... </li></ul><ul><li>...even more complex and completely unreadable </li></ul>
  32. 32. Conclusion and Next steps <ul><li>Global: </li></ul><ul><ul><li>GVIS is enough flexible
  33. 33. Mix of heterogeneous sources seems to work </li></ul></ul><ul><li>Specific: </li></ul><ul><ul><li>User profile as “interest areas” seems to collect some user's relevant characteristics
  34. 34. Enough performance (with cache implemented) </li></ul></ul><ul><li>Next steps: </li></ul><ul><ul><li>Real and extensive evaluation
  35. 35. Filtering with grounding and clustering of tags
  36. 36. New graphical methaphors through new libraries
  37. 37. Bufferization of source data (already implemented) </li></ul></ul>
  38. 38. GRAPPLE project - 7 th EU FP Questions? [email_address]