INTELLIGENT INTERACTIVE SYSTEMS - Ambient intelligence + Mobile technology

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The Fly See Buy app focuses on an interactive media system where a mobile device delivers the input for an ambient system called Points. The system navigates users at the Airport, keeping in mind the user might want to go shopping, use public services, or meet other people during their often short stay at the airport. Fly See Buy aims at travelers having their departure and or transit at the airport. The system informs (e.g. navigates) travelers using the sensors on the user’s mobile device (e.g. smartphone or tablet) in combination with existing navigation software systems already implemented at the airport. One important condition is that the users have the Fly See Buy application installed and running on their mobile system and have access to the internet.

Transcript of INTELLIGENT INTERACTIVE SYSTEMS - Ambient intelligence + Mobile technology

  • 1. Fly See Buy Barry Kollee Selvi Ratnasingam Sylvia van Schie Wouter Stuifmeel Robert Jan Prick INTELLIGENT INTERACTIVE SYSTEMS | OCTOBER 8TH, 2013
  • 2. Introduction: Topics - ambient intelligence - mobile technology
  • 3. Introduction: Topics ambient intelligence (en.wikipedia, 7/9/2013): ambient intelligence (AmI) refers to electronic environments that are sensitive and responsive to the presence of people
  • 4. Introduction: Topics Mobile technology(en.wikipedia, 7/9/2013): a standard mobile device has gone from being no more than a simple two-way pager to being a mobile phone, GPS navigation device, an embedded web browser and instant messaging client, and a handheld game console.
  • 5. Introduction: Vision Weiser, M. The Computer for the 21st Century, Scientific American (1991)
  • 6. Introduction: Ambient Intelligence Weiser, M. The Computer for the 21st Century, Scientific American (1991) Specialized elements of hardware and software, connected by wires, radio waves and infrared, will be so ubiquitous that no one will notice their presence
  • 7. Introduction: Mobile technology Little is more basic to human perception than physical juxtaposition, and so ubiquitous computers must know where they are. If a computer knows merely what room it is in, it can adapt its behavior in significant ways. Weiser, M. The Computer for the 21st Century, Scientific American (1991)
  • 8. Introduction: Vision Weiser, M. The Computer for the 21st Century, Scientific American (1991)
  • 9. Introduction: Goal When things disappear .. we are freed to use them without thinking and so to focus on new goals Weiser, M. The Computer for the 21st Century, Scientific American (1991)
  • 10. The Big Data Challenge Lausanne Data Collection Campaign A large-scale mobile data resource Privacy by design Image logfiles Monitor entire smartphone (N95) Laurila, J. K., Gatica-Perez, D., Aad, I., Blom, J., & Bornet, O.The mobile data challenge: Big data for mobile computing research. , http://privacybydesign.ca/
  • 11. The Big Data Challenge Laurila, J. K., et al.The mobile data challenge: Big data for mobile computing research.
  • 12. The Big Data Challenge Semantic place prediction Next place prediction Demographic attribute prediction Laurila, J. K., et al.The mobile data challenge: Big data for mobile computing research.
  • 13. Applying to Fly See Buy system Each data type corresponds to a table in which each row represents a record such as a phone call or an observation of a WLAN access point. User IDs and timestamps are the basic information for each record. Laurila, J. K., et al.The mobile data challenge: Big data for mobile computing research.
  • 14. How long? Investigate properties of learning Predicting social and individual models Altshuler, Y., (2012). Incremental learning with accuracy prediction of social and individual properties from mobile-phone data.
  • 15. Reality mining A. Pentland, in The Global Information Technology Report 2008-2009 (World Economic Forum, Geneva, 2009)
  • 16. Methodology Classifiers Personal properties (first level) Social links (life-partner?) Correlation amount of time vs. accuracy Altshuler, Y., Aharony, N., Fire, M., Elovici, Y., & Pentland, A. S. (2012). Incremental learning with accuracy prediction of social and individual properties from mobile-phone data.
  • 17. Methodology Android Application GPS Accelerometer Third Party application Cell tower IDs WIFI LAN IDs (proximity) Altshuler, Y., Aharony, N., Fire, M., Elovici, Y., & Pentland, A. S. (2012). Incremental learning with accuracy prediction of social and individual properties from mobile-phone data.
  • 18. Methodology Feature vector Location Sms-pattern Internet usage Call-pattern Phone applications Alarms Friends and family dataset (140 people) Altshuler, Y., Aharony, N., Fire, M., Elovici, Y., & Pentland, A. S. (2012). Incremental learning with accuracy prediction of social and individual properties from mobile-phone data.
  • 19. Conclusion Ethnicity 60% Is student Significant other 65 % Altshuler, Y., Aharony, N., Fire, M., Elovici, Y., & Pentland, A. S. (2012). Incremental learning with accuracy prediction of social and individual properties from mobile-phone data.
  • 20. Conclusion Modeled using gompertz function At a moment I can say with an amount of certainty who you are. Laurila, J. K., Gatica-Perez, D., Aad, I., Blom, J., & Bornet, O.The mobile data challenge: Big data for mobile computing research.
  • 21. AdNext: A Visit-Pattern-Aware Mobile Advertising System | for Urban Commercial Complexes Keywords Mobile advertising Sequential visit patterns Prediction models Wi-Fi localization User survey Kim, B., Ha, J., Lee, S., Kang, S., Lee, Y., Rhee, Y., . . . Song, J. (2011). AdNext: A visit-pattern-aware mobile advertising system for urban commercial complexes. Proceedings of the 12th Workshop on Mobile Computing Systems and Applications, Phoenix, Arizona. 7-12.
  • 22. COEX Mall Largest commercial complex in South Korea 260 stores / 100.000 visitors per day Customer targeting Spatial relevance Temporal relevance Image: Official Site of Korea Tourism http: //www.visitkorea.or. kr/enu/SI/SI_EN_3_1_1_1.jsp?cid=736121 Kim, B. et al AdNext: A visit-pattern-aware mobile advertising system for urban commercial complexes. Proceedings of the 12th Workshop on Mobile Computing Systems and Applications, Phoenix, Arizona. 7-12.
  • 23. AdNext System Architecture Kim, B. et al AdNext: A visit-pattern-aware mobile advertising system for urban commercial complexes. Proceedings of the 12th Workshop on Mobile Computing Systems and Applications, Phoenix, Arizona. 7-12.
  • 24. Collecting Place Visit History store-level localization accuracy identify users current location (using accelerometer) detect in/out time (location change validation) Kim, B. et al AdNext: A visit-pattern-aware mobile advertising system for urban commercial complexes. Proceedings of the 12th Workshop on Mobile Computing Systems and Applications, Phoenix, Arizona. 7-12.
  • 25. Next Visit Prediction Model 1/2 Bayesian Networks Probabilistic graphical model It models the joint probability P(X, Y), where X represents features and Y represents labels. Main features: visit place (P) visit time (T) visit duration (D) gender (G) - static age (A) - static Kim, B. et al AdNext: A visit-pattern-aware mobile adv