Complex Environment Evolution

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Complex Environment Evolution

  1. 1. Complex Environment Evolution Challenges with Semantic Service Infrastructures- Andrej Eisfeld- Achim P. Karduck- David McMeekin IEEE DEST: 18 - 20 June 2012
  2. 2. Structure Background Semantic Agents Evaluation Conclusion 22 Complex Environment Evolution
  3. 3. Background Semantic Agents Use Case ConclusionSmart Camp Aim: Reduce energy consumption in camps Example: Energy costs: 2.000.000 AUD / year 25% savings potential Main Smart Camp System components: Smart Home Controller (SHC) Smart Camp Management Unit (SCMU) 33 Complex Environment Evolution
  4. 4. Background Semantic Agents Use Case ConclusionProblem I Continuing Change “E-type systems must be continually adapted or they become progressively less satisfactory” Continuing Growth “The functional content of E-type systems must be continually increased to maintain user satisfaction over their lifetime” 44 Complex Environment Evolution
  5. 5. Background Semantic Agents Use Case ConclusionProblem II Multiple software systems in service infrastructure Evolution more difficult due to dependencies 55 Complex Environment Evolution
  6. 6. Background Semantic Agents Use Case ConclusionSemantic Service Approaches Approach Loose Coupling WSDL2.0 + SAWSDL x HTML + SA-REST HTML + hRESTs + MicroWSMO EXPRESS ReLL JSON-LD Comparison of multiple Semantic Service aproaches 66 Complex Environment Evolution
  7. 7. Background Semantic Agents Use Case ConclusionLinked Data II JSON-LD is resource orientated Linked Resources Graph (LRG): 77 Complex Environment Evolution
  8. 8. Background Semantic Agents Use Case ConclusionIdea I : LRG Ontology Resource Discovery Resource Composition Resource Invocation 88 Complex Environment Evolution
  9. 9. Background Semantic Agents Use Case ConclusionIdea II : Ontology Paths Permitted Ontology Path (POP) Not Permitted Ontology Path (NPOP) POP + NPOP → Restrictions for LRG traversal 99 Complex Environment Evolution
  10. 10. Background Semantic Agents Use Case ConclusionSemantic Handler Semantic Request Handler Resorce Discovery + Composition + Invocation Semantic Response Handler Data Discovery + Dynamic Code Reuse 1010 Complex Environment Evolution
  11. 11. Background Semantic Agents Use Case ConclusionAgent Communication 1) Define Goal 2) Traverse LRG 3) Retrieve Response 4) Process Response 1111 Complex Environment Evolution
  12. 12. Background Semantic Agents Use Case ConclusionA Semantic Camp SCMU and SHCs as Semantic Agents Flexibility for Resources location and content Functionality enrichment without recompilation 1212 Complex Environment Evolution
  13. 13. Background Semantic Agents Use Case ConclusionSetting Smart Camp Ontology Linked Resources Graph 1313 Complex Environment Evolution
  14. 14. Background Semantic Agents Use Case ConclusionResource Discovery Smart Camp Ontology Linked Resources Graph 1414 Complex Environment Evolution
  15. 15. Background Semantic Agents Use Case ConclusionRepresentations{ { "@context":{ "@context":{ "onto":"http://www.smartcamp.org/onto" "onto":"http://www.smartcamp.org/onto" "door":"onto#DoorSensor" "motion":"onto#MotionSensor" "value":"onto#sensorValue" "value":"onto#sensorValue" }, }, "@type":"door", "@type":"motion", "value":true "valueZ":false} } 15 15 Complex Environment Evolution
  16. 16. Background Semantic Agents Use Case ConclusionComposed Representation { "@context":{ "motion":"http://www.smartcamp.org/ontology#MotionSensor", "door":"http://www.smartcamp.org/ontology#DoorSensor", "value":"http://www.smartcamp.org/ontology#sensorValue" }, "@type":"http://www.smartcamp.org/ontology#Sensor", "motion":{ "value":false }, "door":{ "value":true } } 1616 Complex Environment Evolution
  17. 17. Background Semantic Agents Use Case ConclusionWhat if ... ● Requirements change → new sensors ● Requirements change → obsolete sensors 1717 Complex Environment Evolution
  18. 18. Background Semantic Agents Use Case ConclusionSummary Chosen technologies: JSON-LD + OWL Model of a Semantic Agent Higher evolvability in evolution scenario Ontology Evolution may reduce assessed evolvability 1818 Complex Environment Evolution
  19. 19. Background Semantic Agents Use Case ConclusionOutlook Implementation Research Ontology Evolution & Versioning Service Discovery in a Smart City 1919 Complex Environment Evolution
  20. 20. References ● M. Lehman. On understanding laws, evolution, and conservation in the large- program life cycle. Journal of Systems and Software, 1:213–221, 1980 ● H. P. Breivold, I. Crnkovic, R. Land, and S. Larsson. Using dependency model to support software architecture evolution. In Automated Software Engineering - Workshops, 2008. ASE Workshops 2008. 23rd IEEE/ACM International Conference on, pages 82–91, 2008. ● P.V.D. Laar and T. Punter. Views on Evolvability of Embedded Systems. Springer, 2010. ● Ora Lassila, Tim Berners-Lee, James A. Hendler. The semantic web. Scientific American, 284(5):34–43, 2001. ● http://www.cs.helsinki.fi/research/roosa/images/serious-logo-final.jpg ● http://applicanttracking.files.wordpress.com/2010/06/evolution.jpg ● http://informatique.umons.ac.be/genlog/images/wordle.jpg ● http://www.johnbendever.com/wp-content/uploads/question.jpg 2121 Complex Environment Evolution
  21. 21. DNS Service Discovery Different types of resource records PTR: Defines references to other domains SRV: Defines a service location TXT: Used to add meta-data ------------------------------------------------------------------ General usage: serviceType PTR serviceInstance serviceInstance SRV serviceLocation TXT serviceMetaData 2222 Complex Environment Evolution

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