Community-Driven Adaptation

1. Community-Driven Adaptation Iqbal Mohomed Department of Computer Science University of Toronto

2. Mobility and Adaptation • Content/applications target the desktop • Resource rich environment • Stable • Mobile clients • Limited resource (nw, power, screen size) • Variable resources (Mbps to Kbps) • Adapt application/data to bridge gap

3. Manual/Static Adaptation • Publishers make available content for several classes of devices • e.g., HTML and WAP versions of Web page • Disadvantages: • High cost • Several copies • Maintaining consistency and coherence • Continuous effort to support new types of devices • You can never cover all possible versions! • In practice: • Only done for few high-traffic sites • Limited number of devices

4. Proxy Automatic/Dynamic Adaptation • Adapt content on-the-fly • Optimize for device type, user preferences, context, etc. • Typically done using proxies

5. Existing Approaches • Rule-based adaptation • Convert images larger than 10KB to JPEGs at 25% resolution • Constraint-based adaptation • Functions that relate "user happiness" to metrics (resolution, color depth, frame rate, latency) • Find point that meets all constrains and maximizes "happiness"

6. Limitations • Cannot have rules/constrains per-object per-device • Hard to define correlation between "user happiness" and metrics • In practice, rely on small sets rules/constrains • Based on broad generalizations • e.g., "typical image is viewable at resolution X" • Content agnostic

7. Problem • User does not care equally about all objects • The fidelity at which an object is useful depends a lot on the task and the object's content (semantics) 10% 10%

8. Problem • User does not care equally about all objects • The fidelity at which an object is useful depends a lot on the task and the object's content (semantics) 10% 50%

9. Observations • Computers have a really hard time judging if adapted content is good enough for a task • People can do this easily! Have the users decide how to adapt content!

10. Community-Driven Adaptation • System makes initial prediction as to how to adapt content (use rules and/or constrains) • Let user fix adaptation decisions • Feedback mechanism • System learns from user feedback • Improve adaptation prediction for future accesses

11. Server 1 Correct Server 2 Prediction How it Works Mobile Application Proxy

12. Draw Backs • User is integral part of adaptation loop • Significant burden on user • Iterative process is slow and frustrating • No way people are going to accept this for every access!

13. Hypothesis • User can be grouped into communities • Community members share adaptation requirements • Adapted content that is good for one member is likely to be good for other community members • By tracking a few users we can learn how to adapt content for the community as a whole

14. Research Questions • How good are CDA predictions? • What are good heuristics for learning how to adapt? • At what granularity should user accesses be tracked? (e.g. object, page, site, etc.) • How do we classify users into communities? • Does this classification change over time? • Types of adaptations supported by this technique • Fidelity, page layout, modality (text to voice, video to image) • UI • Good UI for working with adapted data • Effects of UI on quality of adaptation prediction

15. Performance Evaluation • Goal: Quantify extend to which CDA predictions meet users’ adaptation requirements • Approach: • Step 1: User study • Create trace that captures levels of adaptation that users consider appropriate for a given task/content • Step 2: Simulation • Compare rule-based and CDA predictions to values in trace

16. Simples Meaningful Scenario • 1 kind of adaptation • 1 data type • 1 adaptation method • 1 community • Fidelity • Images • Progressive JPEG compression • Same device • Laptop at 56Kbps • Same content • Same tasks

17. Proxy Prototype • Adaptation proxy • Transcode Web images into PJPEG • Split PJPEG into 10 slices • Client • Microsoft Internet Explorer 6.0 • IE plugging enables users to request fidelity refinements • Network between client and proxy • Simulated at 56Kbps

18. Proxy Prototype Operation • When loading page, provide just 1st slice • When user clicks on image • Provide additional slice • Reload image in IE • Add request to trace

19. Web Site and Tasks SiteTask Car show Find cars with license plates eStore Buy a PDA with a camera UofT Map Name of all buildings between two BA and Queen Subway Goal: finish task as fast as possible (minimize clicks) Traces capture minimum fidelity level that users’ consider to be sufficient for the task at hand.

20. eStore

21. eStore

22. Trace Characteristics • 77 different images • All tasks can be performed with images available at Fidelity 4 (3 clicks) • Average data loaded by users for all 3 tasks • 790 KB • 32 images are never clicked by any user

23. Metrics • Extra data • Measure of overshoot • Extra data sent beyond what was selected by user • Extra clicks • Measure of undershoot • Number of time users will have to click to raise fidelity level from prediction to what they required in trace

24. Results

25. Results For same clicks, 90% less extra data

26. Results For same data, 40% less extra clicks

27. eStore Fidelity Breakdown

28. Summary • CDA adapt data tacking into account the content’s relationship to the user task • CDA outperforms rule-based adaptation • 90% less bandwidth wastage • 40% less extra clicks

29. Future Work • Comprehensive CDA evaluation • More bandwidths • More devices • Automatic classification of users into communities • Other data types • Stored video, audio • Other types of adaptation • Page layout, modality • UI • Good UI for working with adapted data • Effects of UI on quality of adaptation prediction Next 7 months 2nd & 3rd year

30. Research Team • Supervisor: Eyal de Lara • Grad. Students: Iqbal Mohomed Alvin Chin • Under. Students: Jim Cai Dennis Zhao iq@cs.toronto.edu www.cs.toronto.edu/~iq