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CAM: Contextualized Attention Metadata Across system Boundaries

CAM: Contextualized Attention Metadata Across system Boundaries. Martin Wolpers , Jehad Najjar, Katrien Verbert & Erik Duval. Why is attention relevant?. Attention is pure money! Amazon (Opinion, Alike, Review, etc.) Google (email, blog, groups, texts, search, etc.)

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CAM: Contextualized Attention Metadata Across system Boundaries

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  1. CAM: Contextualized Attention Metadata Across system Boundaries Martin Wolpers, Jehad Najjar, Katrien Verbert & Erik Duval

  2. Why is attention relevant? Attention is pure money! Amazon (Opinion, Alike, Review, etc.) Google (email, blog, groups, texts, search, etc.) Information overflowMiss things that are important CognitionInformation filtering by human cognitive systems CAM, CAMA2007@JCDL,June 2007

  3. Outline Why? What is context from our perspective? How do we define attention? Contextualizing Attention?? How to capture CAM? Using CAM in application scenarios like learning and business process optimization? And now? CAM, CAMA2007@JCDL,June 2007

  4. What for? Application and task independent support of individualized learning and working experiences! CAM, CAMA2007@JCDL,June 2007

  5. ExampleLearning material authoring NEW CAM, CAMA2007@JCDL,June 2007

  6. ExampleSocial networking CAM, CAMA2007@JCDL,June 2007

  7. ExamplePersonalized Information Provision My Profile CAM, CAMA2007@JCDL,June 2007

  8. Handling Observations Classification based on predefined taxonomy or ontology  Limited to envisioned scenarios Monitoring key strokes, mouse gestures, click-streams, etc.  Vast amount of information CAM, CAMA2007@JCDL,June 2007

  9. Contextual Attention Metadata Usage Data Application Profiles Related Activities Data about theContext Schemas and Models (Domain, Navigation,…) Data about theUser Data about theContent Security and Privacy Contextualized Attention Metadata Usage Data User Profiles User-driven Processes CAM, CAMA2007@JCDL,June 2007

  10. Context All information available at the time of the user activity Examples Document writing about what while chatting with whom about what and listening to which music Whom sending a link of the newly created photo album in which way CAM, CAMA2007@JCDL,June 2007

  11. Attention Schemas Focus on Web applications Attention profiling mark-up language (APML) AttentionXML Broaden scope! Contextualized Attention Metadata schema (CAMs) Application and System overarchingly CAM, CAMA2007@JCDL,June 2007

  12. CAM schema User Document Application CAM, CAMA2007@JCDL,June 2007 Activity

  13. Challenge 1 Capture and correlate content, context, user and attention metadata Behavioural Patterns Knowledge Elicitation 4-Dim Room User Time Documents Activities CAM, CAMA2007@JCDL,June 2007

  14. Challenge 2 Task identification using contextualized attention metadata Mining of structured data Identify chains of activities Generalize to behavioural patterns Task descriptions base on chains of activities CAM, CAMA2007@JCDL,June 2007

  15. Challenge 3 Personalized and contextualized learning experiences User profiling User profile extensions Individual personalized learning Security and Privacy of data CAM, CAMA2007@JCDL,June 2007

  16. Collecting Attention Metadata Applications MSN Messenger, Skype Winamp MS Office Web Browser Etc. Web 2.0 Wakoopa (usage of applications) Root and Gesture Bank (feeds, browsing, etc.) Plazer (location) Etc. Information Systems CAM, CAMA2007@JCDL,June 2007

  17. Attention Monitor Setup CAM, CAMA2007@JCDL,June 2007

  18. Attention Monitor CAM, CAMA2007@JCDL,June 2007

  19. MACE (eContent+) CAM, CAMA2007@JCDL,June 2007

  20. MACE Usage Metadata CAM, CAMA2007@JCDL,June 2007

  21. CAM in business process execution CAM, CAMA2007@JCDL,June 2007

  22. CAMA workshops CAM, CAMA2007@JCDL,June 2007

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