1. Chair for Communication Technology (ComTec), Faculty of Electrical Engineering / Computer Science Prediction of Context Time Series Stephan Sigg, Sandra Haseloff, Klaus David University of Kassel, Germany WAWC’07, August 16, 2007 Lappeenranta, Finland

2. © ComTec 2007 Dr. Sandra Haseloff 2 Contents Introduction to Context Prediction Context Abstraction Levels Context Prediction Architecture Context Prediction Algorithm Simulation Results Conclusion

3. © ComTec 2007 Dr. Sandra Haseloff 3 Context Awareness Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion In current systems, mostly present and/or past context considered Adaptation to anticipated future contexts ➜ Context Prediction Definition by [Dey]: A system is context-aware if it uses context to provide relevant information and/or services to the user, where relevancy depends on the user’s task.

4. © ComTec 2007 Dr. Sandra Haseloff 4 Context Prediction Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion What is Context prediction? Given: Time series of observed contexts Task: Infer information about future contexts

5. © ComTec 2007 Dr. Sandra Haseloff 5 Context Prediction (2) Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion

6. © ComTec 2007 Dr. Sandra Haseloff 6 Context Prediction (3) Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion

7. © ComTec 2007 Dr. Sandra Haseloff 7 Context Prediction (4) Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion

8. © ComTec 2007 Dr. Sandra Haseloff 8 Context Prediction (5) Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion

9. © ComTec 2007 Dr. Sandra Haseloff 9 Context Prediction (6) Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion

10. © ComTec 2007 Dr. Sandra Haseloff 10 Context Prediction – Definition Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion Let T be a context time series Given a probabilistic process p(t) that describes the behaviour of the user in time Context prediction is to learn and apply a prediction function that approximates p(t)

11. © ComTec 2007 Dr. Sandra Haseloff 11 High-Level and Low-Level Context Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion

12. © ComTec 2007 Dr. Sandra Haseloff 12 High-Level and Low-Level Context (2) Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion

13. © ComTec 2007 Dr. Sandra Haseloff 13 High-Level and Low-Level Context (3) Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion

14. © ComTec 2007 Dr. Sandra Haseloff 14 High-Level and Low-Level Context (4) Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion Should prediction be based on high-level contexts or on low-level contexts? Context prediction in the literature is based on high-level contexts Prediction based on low-level contexts is beneficial in some cases No architectures for low-level context prediction available

15. © ComTec 2007 Dr. Sandra Haseloff 15 Context Prediction Architecture Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion Integrated into FOXTROT (Framework for Context-Aware Computing) based on FAME 2 middleware

16. © ComTec 2007 Dr. Sandra Haseloff 16 Context Prediction Architecture (2) Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion

17. © ComTec 2007 Dr. Sandra Haseloff 17 Context Prediction Architecture (3) Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion Context History Observed past contexts Rulebase Typical context patterns Learning Component Creation and update of rulebase Prediction Component Actual context prediction Usage of alignment algorithm

18. © ComTec 2007 Dr. Sandra Haseloff 18 Context Prediction Algorithm Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion Alignment algorithm inspired from bioinformatics

19. © ComTec 2007 Dr. Sandra Haseloff 19 Context Prediction Algorithm (2) Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion

20. © ComTec 2007 Dr. Sandra Haseloff 20 Simulation Results Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion Simulations on synthetic and concrete data have been performed that confirm the results Simulations conducted so far: –Several simulations on synthetic data –Prediction of windpower –Prediction of GPS trajectories Comparison of prediction accuracy for high-level vs. low-level prediction and for different prediction algorithms

21. © ComTec 2007 Dr. Sandra Haseloff 21 Simulation Results (2) Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion Windpower prediction results –ARMA algorithm best suited –Alignment algorithms performs well –Reasons: Periodic patterns in numerical data, small prediction horizon Location prediction results –Alignment algorithm best suited –Reasons: Typical behaviour patterns, longer prediction horizon, high sampling intervals

22. © ComTec 2007 Dr. Sandra Haseloff 22 Conclusion Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion Context prediction is a promising extension for context-aware applications Context prediction based on low-level contexts can have benefits to prediction based on high-level contexts Architecture for context prediction based on low-level contexts Novel, powerful algorithm for context prediction