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MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Hybrid Context Inconsistency Resolution for Context-aware Services.

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Presentation on theme: "MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Hybrid Context Inconsistency Resolution for Context-aware Services."— Presentation transcript:

1 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Hybrid Context Inconsistency Resolution for Context-aware Services Chenhua Chen 1, Chunyang Ye 2, 3 and Hans-Arno Jacobsen 2 1 Department of Computer Science, University of Saarland 2 Middleware Systems Research Group, University of Toronto 3 Institute of Software, Chinese Academy of Sciences

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

3 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Context-awareness An important feature of pervasive applications Context-awareness Sense environment automatically Remember history Adapt to changing situations Contexts locations, time etc. Implicit input/output Seamless integrated 3Chen, Ye and Jacobsen, PerCom'11, Seattle

4 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Supply Chain Scenario 4Chen, Ye and Jacobsen, PerCom'11, Seattle Reading RFID tags Update warehouse database

5 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG 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 5Chen, Ye and Jacobsen, PerCom'11, Seattle

6 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Context Inconsistency Resolution 6Chen, Ye and Jacobsen, PerCom'11, Seattle Context queue Consistency constraints Validate consistency constraints Inconsistent contexts Inconsistency resolution 1) Remove latest 2) Remove oldest 3) Remove all 4) User preference, heuristics etc.

7 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG 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 7Chen, Ye and Jacobsen, PerCom'11, Seattle

8 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Our Proposal: Hybrid Solution 8Chen, Ye and Jacobsen, PerCom'11, Seattle

9 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Example of Our Proposal 9 1. Two readers report inconsistent readings 2. Postpone inconsistency resolution 3. Warehouse check in, collect weight info 4. Update profile of goods 5. Resolve inconsistent readings based on weight and profile Chen, Ye and Jacobsen, PerCom'11, Seattle

10 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Challenges 10 When to resolve? Close to T2: unacceptable recovery cost Close to T0: Semantic information is of limited usefulness How to make use of the application semantics in resolution? Chen, Ye and Jacobsen, PerCom'11, Seattle

11 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Example of Application Semantics 11 warehouse Previous Location: (2, 3) Current Location: (4, 5) Inconsistency found! The probability of each context being inaccurate is 50% Continue move one step New Location: (4, 4) (2, 3) is more likely to be inaccurate, since it is impossible to move from (2, 3) to (4, 4) in two steps. Chen, Ye and Jacobsen, PerCom'11, Seattle

12 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Context-correlation Model 12 C1C1 C2C2 C3C3 C4C4 C5C5 C7C7 C6C6 C1C1 C2C2 C7C7 C4C4 C5C5 C8C8 C9C9 f e (c 3, a) Current contexts Contexts after invoking action a Chen, Ye and Jacobsen, PerCom'11, Seattle f e (CL, a): | NL – CL|≤ 1 CL NL C3C3 C8C8 At least one of C 3 and C 8 is inaccurate!

13 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG 13 C1C1 C2C2 C3C3 … C7C7 C8C8 C9C9 … CiCi CjCj CkCk … … Context C i1 Contexts C i2 Contexts C in C1C1 C2C2 C3C3 p1p1 p2p2 p3p3 C3C3 C1C1 C2C2 p1p1 p2p2 p 1 ≥ 1- p 2 * p 3 Context-correlation Model

14 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Application Error Recovery 14 Inconsistency resolution s0s0 s2s2 a1a1 a2a2 s1s1 s3s3 a3a3 s4s4 a4a4 Sensing c Inconsistency detection a2a2 s2’s2’ s3’s3’ a3a3 b4b4 s2”s2” b3b3 b2b2 Backward recovery Forward recovery Chen, Ye and Jacobsen, PerCom'11, Seattle

15 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Example of Error Recovery Backward recovery – Backtrack the movement Forward recovery – Select a different path 15 warehouse Chen, Ye and Jacobsen, PerCom'11, Seattle

16 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG 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 16Chen, Ye and Jacobsen, PerCom'11, Seattle

17 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Resolution Algorithm 17Chen, Ye and Jacobsen, PerCom'11, Seattle Inconsistency detected Postpone resolution Application continues Collect application semantics Build correlation graph Calculate probability Compute error recovery cost Resolve inconsistency Error recovery

18 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG 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 warehouse Chen, Ye and Jacobsen, PerCom'11, Seattle

19 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG 19 Results L-RL: Remove latest L-RO: Remove oldest M-H: Hybrid solution Higher error rate Chen, Ye and Jacobsen, PerCom'11, Seattle

20 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG 20 Higher threshold Location-aware Results L-RL: Remove latest L-RO: Remove oldest M-H: Hybrid solution Chen, Ye and Jacobsen, PerCom'11, Seattle

21 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG 21 Higher error rate 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 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG 22 Higher threshold Location-aware 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 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Scalability 23 Randomly generate correlation graph Calculate probability of each context being inaccurate Record the time needed Chen, Ye and Jacobsen, PerCom'11, Seattle

24 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG 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 24Chen, Ye and Jacobsen, PerCom'11, Seattle

25 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG 25Chen, Ye and Jacobsen, PerCom'11, Seattle

26 MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG 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 26Chen, Ye and Jacobsen, PerCom'11, Seattle


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