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Fine-Grain Adaptation Using Context Information

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

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  1. Fine-Grain Adaptation Using Context Information Iqbal Mohomed Department of Computer Science University of Toronto Advisor: Prof. Eyal de Lara HotMobile 2007: Doctoral Consortium

  2. Challenge One size does not fit all

  3. 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

  4. Thesis Use context information to determine relevance of content and adapt based on this information • We investigate two domains: • Web Adaptation • Remote Health Monitoring

  5. Web Adaptation: Factors to Consider • Usage Context

  6. Web Adaptation: Factors to Consider • Usage Context • Varying Relevance

  7. Web Adaptation: Factors to Consider • Usage Context • Varying Relevance • Multiple Usage

  8. 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

  9. 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!

  10. 40KB Server 1 Improve Fidelity Server 2 Mobile 2 Application 10KB 20KB Prediction Taking Usage Context Into Account Mobile 1 Application Adaptation Proxy

  11. Tailoring Content to Situational Context Content

  12. Tailoring Content to Situational Context Content

  13. Remote Health Monitoring Wifi, GPRS, etc. Bluetooth, ZigBee, etc.

  14. 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.

  15. 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?

  16. 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)?

  17. 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 …

  18. 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

  19. 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

  20. 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

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