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Hybrid Context Inconsistency Resolution for Context-aware Services

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Hybrid Context Inconsistency Resolution for Context-aware Services

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    1. Hybrid Context Inconsistency Resolution for Context-aware Services Chenhua Chen1, Chunyang Ye2, 3 and Hans-Arno Jacobsen2 1Department of Computer Science, University of Saarland 2Middleware Systems Research Group, University of Toronto 3 Institute of Software, Chinese Academy of Sciences

    2. Outline Background Context-awareness Research Problem Context Inconsistency Resolution Hybrid Solution Context Correlation Model Application Recovery Model Experimental Results 2 Chen, Ye and Jacobsen, PerCom'11, Seattle

    3. Context-awareness An important feature of pervasive applications 3 Chen, Ye and Jacobsen, PerCom'11, Seattle

    4. Supply Chain Scenario 4 Chen, Ye and Jacobsen, PerCom'11, Seattle

    5. Context Inconsistency Reasons Environmental noise Examples RFID reader report wrong readings Register incorrect number in warehouse GPS or GSM devices report inaccurate location Pick wrong route 5 Chen, Ye and Jacobsen, PerCom'11, Seattle

    6. Context Inconsistency Resolution 6 Chen, Ye and Jacobsen, PerCom'11, Seattle

    7. Limitations Difficult to identify problematic contexts E.g., remove the latest, oldest, least frequently used etc. Counter example to remove the latest Two RFID readers, the first one is inaccurate, the second one is accurate Resolution approaches rely heavily on constraints Accuracy and completeness of constraints are crucial Counter example Constraint: Two RFID readers report identical readings Reported readings are the same but inaccurate 7 Chen, Ye and Jacobsen, PerCom'11, Seattle

    8. Our Proposal: Hybrid Solution 8 Chen, Ye and Jacobsen, PerCom'11, Seattle

    9. Example of Our Proposal 9 Chen, Ye and Jacobsen, PerCom'11, Seattle

    10. Challenges 10 Chen, Ye and Jacobsen, PerCom'11, Seattle

    11. Example of Application Semantics 11 Chen, Ye and Jacobsen, PerCom'11, Seattle

    12. Context-correlation Model 12 Chen, Ye and Jacobsen, PerCom'11, Seattle

    13. 13 Context-correlation Model

    14. Application Error Recovery 14 Chen, Ye and Jacobsen, PerCom'11, Seattle

    15. Example of Error Recovery Backward recovery Backtrack the movement Forward recovery Select a different path 15 Chen, Ye and Jacobsen, PerCom'11, Seattle

    16. Cost Model Compensation cost (cpc) For backward recovery Cost of compensating a task Execution cost (ecc) For forward recovery Cost of executing a task Total cost for an error recovery plan 16 Chen, Ye and Jacobsen, PerCom'11, Seattle

    17. Resolution Algorithm 17 Chen, Ye and Jacobsen, PerCom'11, Seattle

    18. Experiment Setup 16 X 16 Map cpc = ecc = 1 Search the target in a heuristic way Random placement of goods Metrics: Accuracy of resolution Cost of error recovery 18 Chen, Ye and Jacobsen, PerCom'11, Seattle

    19. 19 Results L-RL: Remove latest L-RO: Remove oldest M-H: Hybrid solution Chen, Ye and Jacobsen, PerCom'11, Seattle

    20. 20 Results L-RL: Remove latest L-RO: Remove oldest M-H: Hybrid solution Chen, Ye and Jacobsen, PerCom'11, Seattle

    21. 21 Results L-RL: Remove latest L-RO: Remove oldest M-H: Hybrid solution H-ER: Error recovery only Chen, Ye and Jacobsen, PerCom'11, Seattle

    22. 22 Results L-RL: Remove latest L-RO: Remove oldest M-H: Hybrid solution H-ER: Error recovery only Chen, Ye and Jacobsen, PerCom'11, Seattle

    23. Scalability 23 Chen, Ye and Jacobsen, PerCom'11, Seattle

    24. Conclusions A novel approach to resolve context inconsistency Combine low-level inconsistency resolution with high-level error recovery Correlation model to reason about inaccurate contexts Cost model to calculate recovery cost Algorithm to trade off accuracy against recovery cost Future work More real-life experiments Extend the correlation model to support confidence 24 Chen, Ye and Jacobsen, PerCom'11, Seattle

    25. 25 Chen, Ye and Jacobsen, PerCom'11, Seattle

    26. Related Work Existing resolution strategies [Heckmann, IJCAI-MRC’05] Remove the latest, the oldest, the least frequently used [Bu et al. QSIC’06] Remove all [Park et al. Compsac’05] User preference [Capra et al. TSE’03] Auction [Xu et al. ICDCS’08] Heuristics 26 Chen, Ye and Jacobsen, PerCom'11, Seattle

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