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  1. 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. HCI Journal IEEE Transactions on Software Engineering Univ. of Florida (USA) (OSCAR) ACM Transactions on Software Engineering and Methodology Context-Aware Middleware for the Smart Environments Georgia Tech (Context-Toolkit) Middleware for Smart Environments Univ. College London (CRISMA) Berkley (Context-Fabric) Agent Oriented Middleware for Smart Home HK Polytechnic (MobiPADS) Semantic Web Ontology OSGi 新加坡大學 (SOCAM) Jini Maryland Univ. (CoBra, SOUPA) Washington University (LIME)

  3. 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. RDF = Resource Description Framework SOCAM (John,has posture,lie-down) Context raw data RDF (John,location,bed) Activity Recognition Module Context Provider Id=John, activity=lie down, place= bed Using RDF as a Common Context Representation Format Sensor

  5. Actually, all resources are represented by URI, for example: http://www.foo.bar/myhome/mybedroom#light1 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)

  6. Literal Resource locatedIn TV1 Bedroom contains size 9 Representing Context with RDF Network Light Switch1 on state

  7. Low Context Quality! Current Context Applications can not work well in real world What do we mean by low “Context Quality”? Context Quality Model A Context Management mechanism to Improve Context Quality. The Structure of this Paper

  8. 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. (Room311,disjointWith,Aisle3) It seems that we either have to check context repository constantly or some conflict-resolving techniques have to be developed. (Mary,walkIn,Room311) Conflict! (Mary,walkIn,Aisle3) Context Inconsistency Context Repository t t+1 t+2 (Mary,walkIn,Room311) (Mary,walkIn,Aisle3) Room 311 Aisle3

  10. 10 10 10 10 12 12 10 10 10 10 Data Gathering Frequency 10 10 10 11 12 14 10 12 10 10 12 Real World System t t+5 t+10 12 The temperature data gathering period is 2 seconds.

  11. 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. Context Pooling RCIR / RLR 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

  13. Delay Time Service Provision Sensor Data Gathering Context Processing Delay Time t t+k An event happens System know what happens in the real world The time interval between an event happens in real world and when it is recognized by the system.

  14. Error due to context conflict resolution Context Correctness Probability Context Correctness Probability = 7/ 11 = 0.64 10 10 10 11 12 14 10 12 10 10 12 Real World System t t+5 t+10 10 10 10 10 11 11 10 10 10 10 12 Temperature Context The raw context gathering period is 2 seconds

  15. 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. CSIE Building Room342 locatedIn Context and Context Repository Context Context Graph (Extended RDF Network) Context Repository

  17. Node Implicit Edge Meta Edge locatedIn Raw Edge Context Graph • Context Graph is essentially an extended RDF Network. What are the benefits of this extension…? Mary CSIE Building locatedIn locatedIn Room311

  18. Persistent and Dynamic Edges CSIE Building Persistent Edge. The relationship that is unlikely to change. Room342 locatedIn Dynamic Edge. The relationship that is changing with time. CSIE Building Tom locatedIn

  19. 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. Context Processing Procedure Context Repository Rules Row Level Refactoring Raw Context Gathering Inconsistency Resolution RCIR RLR Rule-based Reasoning Updating Context Repository Ontology-based Reasoning Triggering Applications Context Repository Ontology JENA JENA is a Semantic Web Framework for Java, Welcome to the lecture on 5/17 at R310 Not-addressed in this paper

  21. Inconsistency Resolution (Definitions) • Conflict Pair • Conflict Set Mary Room311 locatedIn Conflict Mary Room311 locatedIn We use an edge to represent a context instance here.

  22. 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. Relative Frequency ( rf ) • Example • TTL = 2s • Frequency = 1/6 (次/s) (for dynamic edges) (for persistent edges) t+8 t+12 t t+2 t+6

  24. (Mary,walkIn,Room311) (Mary,walkIn, Aisle3) Raw Context Sets (John,walkIn, A) (Tom,walkIn, A) Jena’s Conflict Detection Mechanism (edge,edge) (edge,edge) Conflict Sets (edge,edge) (edge,edge) (edge,edge) Preserve a pair that have highest rf value. (walkIn,walkIn),rf=0.9 (walkIn,walkIn),rf=0.8 (walkIn,walkIn),rf=0.6 Next edge type (walkIn,walkIn),rf=0.4 No more edges Consistent Sets (edge,edge) (edge,edge) Raw Context Inconsistency Resolution (RCIR)

  25. 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. Conflict! contains contains Context Refactoring: An Example (Bedroom, contains, “Tom”) (Toilet, contains,”Tom”) (Aisle 3, contains, “Tom”) Light Switch on state locatedIn Tom contains Bedroom Toilet 1 contains Aisle 3

  27. Invalidate Context Change Context Pooling • Pooling the unchanged context nodes in local cache to reduce network traffic overhead. Application A RDQL Context Manager Context Pool Context Repository

  28. 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. Performance Evaluation • 2 Intel Xeon CPUs, 4G RAM, Linux OS • Sensor • Mica / Cricket (MIT) • Platform • OSGi Platform • 1257 RDF triples

  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. Discussions • The limitation of context resolution mechanism. • Raw context gathering period.

  32. Raw Data Bill is walking Bill is sleeping Limitations of Context Resolution Sensor Activity Recognition Agent ?? Bio-information Agent OSGi Platform Applications Actually, I’m sleep walking

  33. 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. Outline • Introduction • Quality-based Context Management • Context Quality Measurements • ER-Ontology Context Model • Quality-based Context Processing • Context Pooling • Experiments • Conclusion