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Community-Driven Adaptation Iqbal Mohomed Department of Computer Science University of Toronto Wallach / de Community-Driven Adaptation Iqbal Mohomed Department of Computer Science University of Toronto Wallach / de Lara

Mobility and Adaptation • Content/applications target the desktop • Resource rich environment • Stable 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

Manual/Static Adaptation • Publishers make available content for several classes of devices • e. 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

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

Existing Approaches • Rule-based adaptation • Convert images larger than 10 KB to JPEGs Existing Approaches • Rule-based adaptation • Convert images larger than 10 KB 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"

Limitations • Cannot have rules/constrains per-object per-device • Hard to define correlation between 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

Problem • User does not care equally about all objects • The fidelity at 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%

Problem • User does not care equally about all objects • The fidelity at 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%

Observations • Computers have a really hard time judging if adapted content is good 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!

Community-Driven Adaptation • System makes initial prediction as to how to adapt content (use 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

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

Draw Backs • User is integral part of adaptation loop • Significant burden on 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!

Hypothesis • User can be grouped into communities • Community members share adaptation requirements 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

Research Questions • How good are CDA predictions? • What are good heuristics for 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

Performance Evaluation • Goal: Quantify extend to which CDA predictions meet users’ adaptation requirements 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

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

Prototype Proxy • Adaptation proxy • Transcode Web images into PJPEG • Split PJPEG Prototype Proxy • 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 56 Kbps

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

Web Site and Tasks Site Car show Task Find cars with license plates e. Web Site and Tasks Site Car show Task Find cars with license plates e. Store Buy a PDA with a camera Uof. T 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.

e. Store e. Store

e. Store e. Store

Trace Characteristics • 77 different images • All tasks can be performed with images 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

Metrics • Extra data • Measure of overshoot • Extra data sent beyond what 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

Results Results

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

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

e. Store Fidelity Breakdown e. Store Fidelity Breakdown

Summary • CDA adapt data tacking into account the content’s relationship to the user 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

Future Work • Comprehensive CDA evaluation • More bandwidths • More devices Next 7 Future Work • Comprehensive CDA evaluation • More bandwidths • More devices Next 7 months • 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 2 nd & 3 rd year

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