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Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies
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Fusion 2010 - PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies

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Presentation given by Rommel Carvalho at the 13th International Conference on Information Fusion in 27 July 2010.

Presentation given by Rommel Carvalho at the 13th International Conference on Information Fusion in 27 July 2010.

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  • 1. PROGNOS: Predictive Situational Awareness with Probabilistic Ontologies Rommel Carvalho, Paulo Costa, Kathryn Laskey, and KC Chang George Mason University Paper - 13th International Conference on Information Fusion Fusion 2010 Thursday, July 15, 2010
  • 2. Agenda 2 Thursday, July 15, 2010
  • 3. Agenda Objective 2 Thursday, July 15, 2010
  • 4. Agenda Objective Methodology 2 Thursday, July 15, 2010
  • 5. Agenda Objective Methodology Modeling the PO for MDA Requirements Analysis & Design Implementation 2 Thursday, July 15, 2010
  • 6. Agenda Objective Methodology Modeling the PO for MDA Requirements Analysis & Design Implementation Reasoning 2 Thursday, July 15, 2010
  • 7. Agenda Objective Methodology Modeling the PO for MDA Requirements Analysis & Design Implementation Reasoning Testing the PO for MDA 2 Thursday, July 15, 2010
  • 8. Agenda Objective Methodology Modeling the PO for MDA Requirements Analysis & Design Implementation Reasoning Testing the PO for MDA Conclusion 2 Thursday, July 15, 2010
  • 9. Objective Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 3 Thursday, July 15, 2010
  • 10. Objective Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 4 Thursday, July 15, 2010
  • 11. Objective Develop a probabilistic ontology capable of reasoning with masses of evidence from different domains in order to provide situation awareness on maritime domain. Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 4 Thursday, July 15, 2010
  • 12. Objective Develop a probabilistic ontology capable of reasoning with masses of evidence from different domains in order to provide situation awareness on maritime domain. Part of PROGNOS PRobabilistic OntoloGies for Net-centric Operation Systems Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 4 Thursday, July 15, 2010
  • 13. Objective Develop a probabilistic ontology capable of reasoning with masses of evidence from different domains in order to provide situation awareness on maritime domain. Part of PROGNOS PRobabilistic OntoloGies for Net-centric Operation Systems Use PR-OWL MEBN High-Level Fusion UnBBayes Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 4 Thursday, July 15, 2010
  • 14. Methodology Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 5 Thursday, July 15, 2010
  • 15. UMP-SW Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 6 Thursday, July 15, 2010
  • 16. POMC Requirements Analysis & Design Implementation Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 7 Thursday, July 15, 2010
  • 17. Modeling Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 8 Thursday, July 15, 2010
  • 18. Requirements Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 9 Thursday, July 15, 2010
  • 19. Requirements In our domain we have the following set of goal/queries/ evidence: Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 9 Thursday, July 15, 2010
  • 20. Requirements In our domain we have the following set of goal/queries/ evidence: Does the ship have a terrorist crewmember? Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 9 Thursday, July 15, 2010
  • 21. Requirements In our domain we have the following set of goal/queries/ evidence: Does the ship have a terrorist crewmember? Verify if a crewmember is related to any terrorist; Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 9 Thursday, July 15, 2010
  • 22. Requirements In our domain we have the following set of goal/queries/ evidence: Does the ship have a terrorist crewmember? Verify if a crewmember is related to any terrorist; Verify if a crewmember is associated with any terrorist organization. Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 9 Thursday, July 15, 2010
  • 23. Requirements In our domain we have the following set of goal/queries/ evidence: Does the ship have a terrorist crewmember? Verify if a crewmember is related to any terrorist; Verify if a crewmember is associated with any terrorist organization. Is the ship using an unusual route? Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 9 Thursday, July 15, 2010
  • 24. Requirements In our domain we have the following set of goal/queries/ evidence: Does the ship have a terrorist crewmember? Verify if a crewmember is related to any terrorist; Verify if a crewmember is associated with any terrorist organization. Is the ship using an unusual route? Verify if there is a direct report that the ship is using an unusual route; Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 9 Thursday, July 15, 2010
  • 25. Requirements In our domain we have the following set of goal/queries/ evidence: Does the ship have a terrorist crewmember? Verify if a crewmember is related to any terrorist; Verify if a crewmember is associated with any terrorist organization. Is the ship using an unusual route? Verify if there is a direct report that the ship is using an unusual route; Verify if there is a report that the ship is meeting some other ship for no apparent reason. Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 9 Thursday, July 15, 2010
  • 26. Requirements In our domain we have the following set of goal/queries/ evidence: Does the ship have a terrorist crewmember? Verify if a crewmember is related to any terrorist; Verify if a crewmember is associated with any terrorist organization. Is the ship using an unusual route? Verify if there is a direct report that the ship is using an unusual route; Verify if there is a report that the ship is meeting some other ship for no apparent reason. Does the ship seem to exhibit evasive behavior? Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 9 Thursday, July 15, 2010
  • 27. Requirements In our domain we have the following set of goal/queries/ evidence: Does the ship have a terrorist crewmember? Verify if a crewmember is related to any terrorist; Verify if a crewmember is associated with any terrorist organization. Is the ship using an unusual route? Verify if there is a direct report that the ship is using an unusual route; Verify if there is a report that the ship is meeting some other ship for no apparent reason. Does the ship seem to exhibit evasive behavior? Verify if an electronic countermeasure (ECM) was identified by a navy ship; Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 9 Thursday, July 15, 2010
  • 28. Requirements In our domain we have the following set of goal/queries/ evidence: Does the ship have a terrorist crewmember? Verify if a crewmember is related to any terrorist; Verify if a crewmember is associated with any terrorist organization. Is the ship using an unusual route? Verify if there is a direct report that the ship is using an unusual route; Verify if there is a report that the ship is meeting some other ship for no apparent reason. Does the ship seem to exhibit evasive behavior? Verify if an electronic countermeasure (ECM) was identified by a navy ship; Verify if the ship has a responsive radar and automatic identification system (AIS). Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 9 Thursday, July 15, 2010
  • 29. Analysis & Design I Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 10 Thursday, July 15, 2010
  • 30. Analysis & Design II Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 11 Thursday, July 15, 2010
  • 31. Analysis & Design II The probabilistic rules for our model include: Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 11 Thursday, July 15, 2010
  • 32. Analysis & Design II The probabilistic rules for our model include: A ship is of interest if and only if it has a terrorist crewmember; Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 11 Thursday, July 15, 2010
  • 33. Analysis & Design II The probabilistic rules for our model include: A ship is of interest if and only if it has a terrorist crewmember; If a crewmember is related to a terrorist, then it is more likely that he is also a terrorist; Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 11 Thursday, July 15, 2010
  • 34. Analysis & Design II The probabilistic rules for our model include: A ship is of interest if and only if it has a terrorist crewmember; If a crewmember is related to a terrorist, then it is more likely that he is also a terrorist; If a crewmember is a member of a terrorist organization, then it is more likely that he is a terrorist; Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 11 Thursday, July 15, 2010
  • 35. Analysis & Design II The probabilistic rules for our model include: A ship is of interest if and only if it has a terrorist crewmember; If a crewmember is related to a terrorist, then it is more likely that he is also a terrorist; If a crewmember is a member of a terrorist organization, then it is more likely that he is a terrorist; If an organization has a terrorist member, it is more likely that it is a terrorist organization; Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 11 Thursday, July 15, 2010
  • 36. Analysis & Design II The probabilistic rules for our model include: A ship is of interest if and only if it has a terrorist crewmember; If a crewmember is related to a terrorist, then it is more likely that he is also a terrorist; If a crewmember is a member of a terrorist organization, then it is more likely that he is a terrorist; If an organization has a terrorist member, it is more likely that it is a terrorist organization; A ship of interest is more likely to have an unusual route; Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 11 Thursday, July 15, 2010
  • 37. Analysis & Design II The probabilistic rules for our model include: A ship is of interest if and only if it has a terrorist crewmember; If a crewmember is related to a terrorist, then it is more likely that he is also a terrorist; If a crewmember is a member of a terrorist organization, then it is more likely that he is a terrorist; If an organization has a terrorist member, it is more likely that it is a terrorist organization; A ship of interest is more likely to have an unusual route; A ship of interest is more likely to meet other ships for trading illicit cargo; Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 11 Thursday, July 15, 2010
  • 38. Analysis & Design III Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 12 Thursday, July 15, 2010
  • 39. Analysis & Design III The probabilistic rules for our model include: Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 12 Thursday, July 15, 2010
  • 40. Analysis & Design III The probabilistic rules for our model include: A ship that meets other ships to trade illicit cargo is more likely to have an unusual route; Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 12 Thursday, July 15, 2010
  • 41. Analysis & Design III The probabilistic rules for our model include: A ship that meets other ships to trade illicit cargo is more likely to have an unusual route; A ship of interest is more likely to have an evasive behavior; Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 12 Thursday, July 15, 2010
  • 42. Analysis & Design III The probabilistic rules for our model include: A ship that meets other ships to trade illicit cargo is more likely to have an unusual route; A ship of interest is more likely to have an evasive behavior; A ship with evasive behavior is more likely to have non responsive electronic equipment; Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 12 Thursday, July 15, 2010
  • 43. Analysis & Design III The probabilistic rules for our model include: A ship that meets other ships to trade illicit cargo is more likely to have an unusual route; A ship of interest is more likely to have an evasive behavior; A ship with evasive behavior is more likely to have non responsive electronic equipment; A ship with evasive behavior is more likely to deploy an ECM; Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 12 Thursday, July 15, 2010
  • 44. Analysis & Design III The probabilistic rules for our model include: A ship that meets other ships to trade illicit cargo is more likely to have an unusual route; A ship of interest is more likely to have an evasive behavior; A ship with evasive behavior is more likely to have non responsive electronic equipment; A ship with evasive behavior is more likely to deploy an ECM; A ship might have non responsive electronic equipment due to working problems; Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 12 Thursday, July 15, 2010
  • 45. Analysis & Design III The probabilistic rules for our model include: A ship that meets other ships to trade illicit cargo is more likely to have an unusual route; A ship of interest is more likely to have an evasive behavior; A ship with evasive behavior is more likely to have non responsive electronic equipment; A ship with evasive behavior is more likely to deploy an ECM; A ship might have non responsive electronic equipment due to working problems; A ship that is within radar range of a ship that deployed an ECM might be able to detect the ECM, but not who deployed it. Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 12 Thursday, July 15, 2010
  • 46. Implementation I Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 13 Thursday, July 15, 2010
  • 47. Implementation II Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 14 Thursday, July 15, 2010
  • 48. Implementation III Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 15 Thursday, July 15, 2010
  • 49. Implementation IV Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 16 Thursday, July 15, 2010
  • 50. Reasoning Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 17 Thursday, July 15, 2010
  • 51. SSBN Construction Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 18 Thursday, July 15, 2010
  • 52. Scalability I '!" &#" &!" %#" !"#$%&!'()&*+,& %!" $#" $!" #" !" !" #" $!" $#" %!" %#" &!" &#" '!" '#" -.(/)0&"1&-"2)+& *Linear time compared to number of nodes, but... Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 19 Thursday, July 15, 2010
  • 53. Scalability II *...exponential number of nodes compared to KB size Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 20 Thursday, July 15, 2010
  • 54. Scalability III *SSMSBN to explore local computation Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 21 Thursday, July 15, 2010
  • 55. Scalability IV *Approximation algorithms to improve computation speed Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 22 Thursday, July 15, 2010
  • 56. Testing Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 23 Thursday, July 15, 2010
  • 57. Simulate Ground Truth Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 24 Thursday, July 15, 2010
  • 58. Create Agents Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 25 Thursday, July 15, 2010
  • 59. Sample Reports Ground Truth *Some GT information will never be sampled - e.g. ship of interest **Different KBs might have different types of information - e.g. social network vs ship location Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 26 Thursday, July 15, 2010
  • 60. Sample Reports Ground Truth CIA *Some GT information will never be sampled - e.g. ship of interest **Different KBs might have different types of information - e.g. social network vs ship location Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 26 Thursday, July 15, 2010
  • 61. Sample Reports Ground Truth CIA FBI *Some GT information will never be sampled - e.g. ship of interest **Different KBs might have different types of information - e.g. social network vs ship location Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 26 Thursday, July 15, 2010
  • 62. Sample Reports Ground Truth CIA FBI Navy *Some GT information will never be sampled - e.g. ship of interest **Different KBs might have different types of information - e.g. social network vs ship location Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 26 Thursday, July 15, 2010
  • 63. Sample Reports Ground Truth CIA ... FBI Navy *Some GT information will never be sampled - e.g. ship of interest **Different KBs might have different types of information - e.g. social network vs ship location Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 26 Thursday, July 15, 2010
  • 64. Connect the Dots Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 27 Thursday, July 15, 2010
  • 65. Compare to Ground Truth Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 28 Thursday, July 15, 2010
  • 66. Compare to Ground Truth Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 28 Thursday, July 15, 2010
  • 67. Compare to Ground Truth Ground Truth Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 28 Thursday, July 15, 2010
  • 68. Compare to Ground Truth ? = Ground Truth Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 28 Thursday, July 15, 2010
  • 69. PCC Evaluation ! Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 29 Thursday, July 15, 2010
  • 70. Conclusion Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 30 Thursday, July 15, 2010
  • 71. Conclusion Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 31 Thursday, July 15, 2010
  • 72. Conclusion Showed how use the UMP-SW to create a PO for MDA Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 31 Thursday, July 15, 2010
  • 73. Conclusion Showed how use the UMP-SW to create a PO for MDA Implemented different solutions to scalability problems SSMSBN Approximation algorithms Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 31 Thursday, July 15, 2010
  • 74. Conclusion Showed how use the UMP-SW to create a PO for MDA Implemented different solutions to scalability problems SSMSBN Approximation algorithms Implemented a solid framework for testing the models Simulation Comparing results to ground truth PCC Evaluation Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 31 Thursday, July 15, 2010
  • 75. Future Work Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 32 Thursday, July 15, 2010
  • 76. Future Work Improve the PO for MDA Include new rationales based on statistical data Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 32 Thursday, July 15, 2010
  • 77. Future Work Improve the PO for MDA Include new rationales based on statistical data Improve scalability Implement hypothesis management Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 32 Thursday, July 15, 2010
  • 78. Future Work Improve the PO for MDA Include new rationales based on statistical data Improve scalability Implement hypothesis management Improve communication Gather information from different sources using OWL-S Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 32 Thursday, July 15, 2010
  • 79. Future Work Improve the PO for MDA Include new rationales based on statistical data Improve scalability Implement hypothesis management Improve communication Gather information from different sources using OWL-S Generate statistical results from different simulations Compare the results to the ground truth Compute PCC Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 32 Thursday, July 15, 2010
  • 80. Obrigado! 33 Thursday, July 15, 2010

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