Contents

1. Prediction of Context Time SeriesStephan Sigg, Sandra Haseloff, Klaus DavidUniversity of Kassel, GermanyWAWC’07, August 16, 2007Lappeenranta, Finland

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

3. Context AwarenessIntroduction – 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 iscontext-aware if it uses context to provide relevant information and/or services to the user, where relevancy depends on the user’s task. Dr. Sandra Haseloff

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

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

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

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

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

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

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

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

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

15. Context Prediction Architecture Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion Integrated into FOXTROT (Framework for Context-Aware Computing) based on FAME2 middleware Dr. Sandra Haseloff

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

17. Context Prediction Architecture (3) Introduction – Abstraction Levels – Architecture – Algorithm – Simulation – Conclusion • Context HistoryObserved past contexts • RulebaseTypical context patterns • Learning ComponentCreation and update of rulebase • Prediction ComponentActual context predictionUsage of alignment algorithm Dr. Sandra Haseloff

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

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