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

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.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, etc. Typically done using proxies Proxy

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"

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 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 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 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 rules and/or constrains) Let user fix adaptation decisions Feedback mechanism System learns from user feedback Improve adaptation prediction for future accesses

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

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 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 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 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 adaptation method 1 community Fidelity Images Progressive JPEG compression Same device Laptop at 56Kbps Same content Same tasks

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 Proxy

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

Web Site and Tasks SiteTask Car showFind cars with license plates eStoreBuy a PDA with a camera UofT MapName 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.

eStore

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 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

For same clicks, 90% less extra data

Results For same data, 40% less extra clicks

eStore Fidelity Breakdown

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 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 2 nd & 3 rd year

Research Team Supervisor:Eyal de Lara Grad. Students:Iqbal Mohomed Alvin Chin Under. Students:Jim Cai Dennis Zhao