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PMA: A Mobile Context-Aware Personal Messaging Assistant

PMA: A Mobile Context-Aware Personal Messaging Assistant. Senaka Buthpitiya Deepthi Madamanchi Sumalatha Kommaraju Martin Griss CyLab Mobility Research Center. Mobility Research Center Carnegie Mellon Silicon Valley. Agenda. Introduction to Email Sorting Related Work

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PMA: A Mobile Context-Aware Personal Messaging Assistant

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  1. PMA: A Mobile Context-Aware Personal Messaging Assistant Senaka Buthpitiya Deepthi Madamanchi Sumalatha Kommaraju Martin Griss CyLab Mobility Research Center Mobility Research Center Carnegie Mellon Silicon Valley

  2. Agenda • Introduction to Email Sorting • Related Work • PMA – Design and Architecture • Experiments & Results • Conclusion • Future Work

  3. What is a “Mobile Context-Aware Personal Messaging Assistant”? • An advanced rule-based email management system which uses the mobile user’s context and email content to • classify emails • prioritize emails • selectively deliver key messages to mobile phone • Uses real-time context information from: • hard sensors (GPS, accelerometer, etc.) on Mobile phone • soft sensors (calendar, …)

  4. Email Flooding in the Real World • Busy professionals receive in excess of 50 emails per day, • 23% require immediate attention • 13% require attention later • 64% are unimportant • Problem is even worse for mobile • professionals • Difficult to sort through emails on mobile devices • Wastes precious bandwidth and battery life • End Result: • Wastes time sorting through unwanted emails • Drastic reduction in productivity!

  5. Problems • Most email sorting/classification programs take only email-content into account • Depending on users’ contexts, the emails thatthey wish to see vary • Depending on the users’ contexts the number of emails they can scan through varies • Email sorting/classification programs consider importance only • Importance and urgency are • orthogonal yet affects • email sorting equally

  6. PMA Architecture PMA separately rates emails according importance and urgency using context information and email content e.g. – email from the user’s boss about present meeting is important and very urgent PMA decides on what-to deliver, how-to-deliver and where-to-deliveraccording to user’s context e.g. – deliver as SMS, text-to-voice SMS, forward to co-worker Uses a rule-based system for decision making

  7. Context Information • Gathered from hard sensors on a Nokia N95 (which also doubles as a delivery point for selected emails) • Gathered from soft sensors such as Google Calendar • Context includes all information • related to user including, • Static context such as name and • family details • Dynamic context such as meeting • topic, driving speed • User preferences

  8. Experiment - 1 • AIM – Test effectiveness of PMA’s urgency and importance classifiers • For various user contexts, • PMA classifies a test set of emails separately for importance and urgency • compared against ratings for the same emails by user Number of type X emails correctly classified by PMA Recall = Total number of emails selected by users as type X Number of type X emails correctly classified by PMA Precision = Number of emails classified by PMA as X

  9. Results Summary of precision and recall of importance classification Summary of precision and recall of urgency classification

  10. Experiment - 2 • AIM – Test effectiveness of PMA’s delivery agent and overall system • For various user contexts, • PMA decides on what action to perform with a given email • SMS to user • Send to users as text-to-voice SMS • Folder for later viewing • Take no action • compared against user’s expected action on each email

  11. Results

  12. Conclusions • PMA sorts and delivers messages that are relevant to the user in his current context, effectively • Uses emails content and user’s context information for decision making • PMA uses separate scales to measure urgency and importance of an email • PMA is scalable for all inbox sizes • PMA is easily personalized to suit the requirements of any user for better accuracy

  13. Future Work • Performance of PMA • Machine learning schemes to automate the learning from user feedback • Improve run-time • Generalization of PMA • Support for various email accounts Yahoo! mail, Hotmail, etc. • Support for additional message types (SMS, IM, RSS, etc.) • Personalization of PMA • User interface to create/edit custom rules • Mobile device interface for feedback and usability

  14. Thank You

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