1. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 Managing Quality of Context in Pervasive Computing Authors Y.Bu, T.Gu, X.Tao, J.Li, S.Chen, and J.Lu Proceedings of 6th IEEE International Conference on Quality Software (QSIC’06)Reporter C.F.Liao ( 廖峻鋒 ) Apr 27,2007

2. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 2/30 Context-Aware Middleware for the Smart Environments OSGi Middleware for Smart Home Middleware for Smart Environments Univ. of Florida (USA) Univ. of Florida (USA)(OSCAR) Semantic Web Ontology 新加坡大學(SOCAM) Agent Oriented Georgia Tech (Context-Toolkit) Maryland Univ. (CoBra, SOUPA) HK Polytechnic (MobiPADS) Washington University (LIME) Jini Berkley(Context-Fabric) Univ. College London (CRISMA) HCI Journal IEEE Transactions on Software Engineering ACM Transactions on Software Engineering and Methodology

3. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 3/30 Outline  Introduction  Quality-based Context Management Context Quality Measurements ER-Ontology Context Model Quality-based Context Processing Context Pooling  Experiments  Conclusion (RLR and the Case Study sections are skipped in this presentation)

4. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 4/30 Using RDF as a Common Context Representation Format Sensor Context Provider Context Provider Id=John, activity=lie down, place= bed Activity Recognition Module Activity Recognition Module raw data Context (John,has posture,lie-down) (John,location,bed) RDF SOCAM RDF = Resource Description Framework

5. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 5/30 Describing Data with RDF  RDF is a W3C standard, which has the following capabilities Able to describe most kinds of data. Able to describe the structural design of data sets. Able to describe relationships between data.  Format:  Example: (bedroom, contains, light1) (light1, state, “on”) (subject, predicate, object) Actually, all resources are represented by URI, for example: http://www.foo.bar/myhome/mybedroom#light1

6. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 6/30 Representing Context with RDF Network Light Switch1 state on Literal Resource Bedroom locatedIn size 9 contains TV1

7. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 7/30 The Structure of this Paper Current Context Applications can not work well in real world Low Context Quality! What do we mean by low “Context Quality”?  Context Quality Model A Context Management mechanism to Improve Context Quality.

8. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 8/30 Motivation  Context-awareness plays a key role in a paradigm shift from traditional desktop computing to pervasive computing.  Most context-aware applications are unlikely to work well in the real world.  Two major factors: Inconsistent contexts The limited data gathering frequency

9. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 9/30 Context Repository Context Inconsistency Room 311Aisle3 (Mary,walkIn,Room311) (Mary,walkIn,Aisle3) (Room311,disjointWith,Aisle3) (Mary,walkIn,Aisle3) tt+2t+1 Conflict! It seems that we either have to check context repository constantly or some conflict- resolving techniques have to be developed.

10. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 10/30 Data Gathering Frequency t t+5t+10 10 111214 101210 12 Real World System The temperature data gathering period is 2 seconds. 10 12 10

11. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 11/30 Outline  Introduction  Quality-based Context Management Context Quality Measurements ER-Ontology Context Model Quality-based Context Processing Context Pooling  Experiments  Conclusion (RLR and the Case Study sections are skipped in this presentation)

12. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 12/30 Evaluating Context Quality  Context Quality Measurements Delay Time Context Correctness Probability Context Consistency Probability  A well-designed context-aware system should have: Low Delay Time High Context Correctness Probability High Context Consistency Probability Context Pooling RCIR / RLR

13. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 13/30 Delay Time t t+k An event happens System know what happens in the real world Sensor Data Gathering Context Processing Service Provision Delay Time The time interval between an event happens in real world and when it is recognized by the system.

14. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 14/30 Context Correctness Probability t t+5t+10 Temperature Context 10 Context Correctness Probability = 7/ 11 = 0.64 10 111214 101210 12 10 1110 12 Real World System 10 1110 The raw context gathering period is 2 seconds Error due to context conflict resolution

15. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 15/30 Outline  Introduction  Quality-based Context Management Context Quality Measurements ER-Ontology Context Model Quality-based Context Processing Context Pooling  Experiments  Conclusion (RLR and the Case Study sections are skipped in this presentation)

16. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 16/30 Context and Context Repository Room342 CSIE Building locatedIn Context Graph (Extended RDF Network) Context Repository Context

17. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 17/30  Context Graph is essentially an extended RDF Network. Context Graph Mary Room311 CSIE Building locatedIn Node Implicit EdgeMeta Edge Raw Edge What are the benefits of this extension…?

18. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 18/30 Persistent and Dynamic Edges Room342 CSIE Building locatedIn Tom CSIE Building locatedIn Persistent Edge. The relationship that is unlikely to change. Dynamic Edge. The relationship that is changing with time.

19. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 19/30 Outline  Introduction  Quality-based Context Management Context Quality Measurements ER-Ontology Context Model Quality-based Context Processing Context Pooling  Experiments  Conclusion (RLR and the Case Study sections are skipped in this presentation)

20. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 20/30 Context Processing Procedure Raw Context Gathering Inconsistency Resolution Row Level Refactoring Context Repository Rule-based Reasoning Rules Triggering Applications Updating Context Repository Ontology-based Reasoning Ontology Context Repository RCIR RLR JENA Not-addressed in this paper JENA is a Semantic Web Framework for Java, Welcome to the lecture on 5/17 at R310

21. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 21/30 Inconsistency Resolution (Definitions)  Conflict Pair  Conflict Set Mary Room311 locatedIn Mary Room311 locatedIn Conflict We use an edge to represent a context instance here.

22. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 22/30 Inconsistency Resolution by RF  Core idea When resolving conflicts, more frequent contexts have more priority than infrequent ones. RF (Relative Frequency): Using TTL (Time to live) to transform static frequency to dynamic frequency.  Term definitions Edge TTL  The time period in which a context is valid. Edge Frequency Edge Start Time

23. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 23/30 Relative Frequency ( ) Relative Frequency ( rf )  Example TTL = 2s Frequency = 1/6 ( 次 /s) tt+6t+2 t+12t+8 (for persistent edges) (for dynamic edges)

24. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 24/30 Raw Context Inconsistency Resolution (RCIR) Raw Context Sets (Mary,walkIn,Room311) (John,walkIn, A) (Mary,walkIn, Aisle3) (Tom,walkIn, A) Jena’s Conflict Detection Mechanism (edge,edge) Conflict Sets (edge,edge) Next edge type Consistent Sets (edge,edge) No more edges (walkIn,walkIn),rf=0.9 (walkIn,walkIn),rf=0.8 (walkIn,walkIn),rf=0.6 (walkIn,walkIn),rf=0.4 Preserve a pair that have highest rf value.

25. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 25/30 Context Refactoring  If a raw edge is changed, its related implicit edges should also be changed.  The RLR (Raw Level Refactoring)algorithm aims to remove edges that are dependent to in- existing raw edges.

26. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 26/30 Conflict! Context Refactoring: An Example Light Switch state on Toilet 1 locatedIn Aisle 3 contains Tom Bedroom contains (Toilet, contains,”Tom”) (Aisle 3, contains, “Tom”) (Bedroom, contains, “Tom”)

27. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 27/30 Context Pooling Context Repository Application A RDQL Context Pool Context Change Invalidate Context Manager  Pooling the unchanged context nodes in local cache to reduce network traffic overhead.

28. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 28/30 Outline  Introduction  Quality-based Context Management Context Quality Measurements ER-Ontology Context Model Quality-based Context Processing Context Pooling  Experiments  Conclusion (RLR and the Case Study sections are skipped in this presentation)

29. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 29/30 Performance Evaluation  2 Intel Xeon CPUs, 4G RAM, Linux OS  Sensor Mica / Cricket (MIT)  Platform OSGi Platform 1257 RDF triples

30. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 30/30 Conclusions  The authors proposed a Context Quality Measurements Model based on their experiences of designing context-aware applications.  Several mechanisms are proposed to increase the context quality: ER-Ontology Context Model RCIR / RLR Context Pooling

31. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 31/30 Discussions  The limitation of context resolution mechanism.  Raw context gathering period.

32. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 32/30 Limitations of Context Resolution Sensor Activity Recognition Agent OSGi Platform Applications Bio-information Agent ?? Raw Data Bill is walking Bill is sleeping Actually, I’m sleep walking

33. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 33/30 Raw Context Gathering Period  The gathering period is important to both performance and effectiveness. To short – the processing mechanism will degrade to piece by piece processing. To long – to much inconsistency, the RCIR algorithm will have low performance.

34. Intelligent Space 國立台灣大學資訊工程研究所 智慧型空間實驗室 34/30 Outline  Introduction Introduction  Quality-based Context Management Context Quality Measurements ER-Ontology Context Model Quality-based Context Processing Context Pooling  Experiments Experiments  Conclusion