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Fine-Grain Adaptation Using Context Information. Iqbal Mohomed Department of Computer Science University of Toronto Advisor: Prof. Eyal de Lara. HotMobile 2007: Doctoral Consortium. Challenge. One size does not fit all. Challenge. Adaptation can help! Challenge:
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Fine-Grain Adaptation Using Context Information Iqbal Mohomed Department of Computer Science University of Toronto Advisor: Prof. Eyal de Lara HotMobile 2007: Doctoral Consortium
Challenge One size does not fit all
Challenge • Adaptation can help! • Challenge: • How to pick appropriate adaptation? • Existing techniques based on rules/constraints do not consider relevance of content One size does not fit all
Thesis Use context information to determine relevance of content and adapt based on this information • We investigate two domains: • Web Adaptation • Remote Health Monitoring
Web Adaptation: Factors to Consider • Usage Context
Web Adaptation: Factors to Consider • Usage Context • Varying Relevance
Web Adaptation: Factors to Consider • Usage Context • Varying Relevance • Multiple Usage
Web Adaptation: Factors to Consider • Usage Context • Varying Relevance • Multiple Usage • Situational Content • E.g. Type of device, characteristics of available wireless link, user’s location
Web Adaptation: Factors to Consider • Usage Context • Varying Relevance • Multiple Usage • Situational Content • E.g. Type of device, characteristics of available wireless link, user’s location For fine-grain adaptation, content must be tailored for both usage context and situational context!
40KB Server 1 Improve Fidelity Server 2 Mobile 2 Application 10KB 20KB Prediction Taking Usage Context Into Account Mobile 1 Application Adaptation Proxy
Remote Health Monitoring Wifi, GPRS, etc. Bluetooth, ZigBee, etc.
Remote Health Monitoring • Context-Aware Filtering can significantly reduce the amount of data transmitted • Use context information to judge what sensor readings are expected • Vary fidelity of transmitted data based on whether sensor readings conform to expectations Wifi, GPRS, etc. Bluetooth, ZigBee, etc.
Next Steps • Web Adaptation • Can we reduce the amount of interaction required, while still providing fine-grain adaptation? • How well will our techniques work on a large scale in the real-world, over an extended period of time?
Next Steps • Web Adaptation • Can we reduce the amount of interaction required, while still providing fine-grain adaptation? • How well will our techniques work on a large scale in the real-world, over an extended period of time? • Remote Health Monitoring • Can we use context-information to save energy (in ways other than reducing the amount of data)?
Next Steps • Web Adaptation • Can we reduce the amount of interaction required, while still providing fine-grain adaptation? • How well will our techniques work on a large scale in the real-world, over an extended period of time? • Remote Health Monitoring • Can we use context-information to save energy (in ways other than reducing the amount of data)? • Graduate! And live happily ever after …
Conclusions • Use context information to determine relevance of data in a given situation • When resources are constrained, optimize based on relevance Examples: • When bandwidth is costly, or low link-throughput: • Perform aggressive fidelity reduction on less relevant images • Transmit averages when sensor readings conform to norms • When screen real-estate is limited: • Simplify web page by removing irrelevant images
Conclusions • Use context information to determine relevance of data in a given situation • When resources are constrained, optimize based on relevance Examples: • When bandwidth is costly, or low link-throughput: • Perform aggressive fidelity reduction on less relevant images • Transmit averages when sensor readings conform to norms • When screen real-estate is limited: • Simplify web page by removing irrelevant images Collaborators: @ UofT; Prof. Eyal de Lara, Jin Zhang, Jim Cai, Sina Chavoshi and Alvin Chin @ IBM Watson: Dr. Maria Ebling, William Jerome, Dr. Archan Misra
Conclusions Questions/Feedback! • Use context information to determine relevance of data in a given situation • When resources are constrained, optimize based on relevance Examples: • When bandwidth is costly, or low link-throughput: • Perform aggressive fidelity reduction on less relevant images • Transmit averages when sensor readings conform to norms • When screen real-estate is limited: • Simplify web page by removing irrelevant images