1 / 36

Aggregating T&E Data for CCoD Trust in Operational Implementation

Aggregating T&E Data for CCoD Trust in Operational Implementation. Presented to: 27 th International Symposium on Military Operational Research 1 September 2010. Suzanne M. Beers, Ph.D. The MITRE Corporation Operations Research & Systems Analysis Department (E525)

kevina
Download Presentation

Aggregating T&E Data for CCoD Trust in Operational Implementation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Aggregating T&E Data for CCoD Trust in Operational Implementation Presented to: 27th International Symposium on Military Operational Research 1 September 2010 Suzanne M. Beers, Ph.D. The MITRE Corporation Operations Research & Systems Analysis Department (E525) Colorado Springs, Colorado

  2. Background • Composable Capability on Demand (CCoD) • MITRE research focus area • Provides means to develop agile information architectures • Rapidly meet warfighter mission needs • Implementation challenge: Trust in composed capability • Fuzzy Logic • Multi-valued logic using fuzzy sets • Successfully used in control and analysis applications • Allows humanistic reasoning, gradual transition between states • Decision support system: Aggregate component T&E data

  3. Overview • What’s Composable Capability on Demand (CCoD)? • Implementation problem statement • CCoD trust taxonomy • What’s fuzzy logic? • Fuzzy logic-based CCoD Decision Support System (DSS) • Structure • Example DSS and results • Conclusion

  4. What’s CCoD? Producers Broker Consumers Loosely Coupled Services Describe & Publish Discover & Subscribe Composable Architectures for Net-centric Operations Rapidly Changeable to Meet Mission Information Needs

  5. One instantiation of CCOD: Mashups • Mashup is a web application that brings together several sources of data to form a unique new combination of information • EMML, Enterprise Mashup Markup Language • Language for creating enterprise mashups • Software applications that consume and mash data from variety of sources • Output presented in graphical user interface, widgets, gadgets

  6. …for example CCoD: Satellite tracking Mashup: Chicago crime

  7. Composed Capability Trust Problem Take the components “off the shelf” Compose a CCoD C2 capability Do I believe this info? Should I act on it? What should I DO??? Tell the user WHAT to give him confidence to use the system To know he’ll make the right moves based on the information

  8. CCoD Implementation Challenge • Lives could depend upon the answer… • How to build trust without slowing down the process? • Use existing component T&E data • Software quality metrics • Aggregate using fuzzy logic • Provide trust measure at composed capability level

  9. Software Trust Metrics • Component Quality Models (CQM) • Evaluate the quality of reusable software modules • Typically start with ISO/IEC 9126 standards • Define characteristics, sub-characteristics, attributes/metrics • CCoD Trust Taxonomy developed by MITRE • Trust: to have confidence in; depend on • Formal acquisition: requirements development then T&E process • CCoD: collect metrics during private to public state transition • Categories • CCoD environment • Component • Composition • Component Developer & Composer: proficiency • Conducted decision-maker survey – ID’ed important factors

  10. CCoD Trust Taxonomy – Environment

  11. CCoD Trust Taxonomy – Component

  12. CCoD Trust Taxonomy – Composition

  13. T&E Data – to – Composed Capability Trust

  14. CCoD Trust Taxonomy – Top Level Component Quality Characteristics Provide required services under specified conditions Protect info and data; unauthorized cannot read/modify; authorized not denied access Maintain a specified level of performance Usable by someone other than developer Provide appropriate performance relative to amount of resources used Can be modified

  15. CCoD Trust Taxonomy - Functionality

  16. CCoD Trust Taxonomy – Security

  17. CCoD Trust Taxonomy - Reliability

  18. CCoD Trust Taxonomy - Usability

  19. CCoD Trust Taxonomy - Efficiency

  20. CCoD Trust Taxonomy - Maintainability

  21. Down-sized CCoD Decision Hierarchy Provide correct results w/ needed precision Provide required services Provide appropriate set of functions Interact w/ other components Prevent information disclosure Protect info & data Only modifiable by authorized users Maintain performance Re-establish performance & recover data Understand if suitable / how used Non-creator usable

  22. Why fuzzy logic? • Multi-valued logic based upon fuzzy set theory • Each element in a fuzzy set has membership value • A(u)  [0,1] • Linguistic variables related in if – then rule bases • Ideal for representation and analysis of imprecise dependencies • Lowers the cost of products by simplifying programming • Define fuzzy sets, define rule bases relating the sets • Captures human-reasoning • Wide variety of successful control and analysis applications • Control: vacuum cleaners, video cameras, subway systems • Analysis: optimize manufacturing lines, predict insurgent network behavior, aging

  23. Fuzzification Fuzzy Input Inference Engine Fuzzy Output Defuzzification Crisp Output Fuzzy Inference Structure Crisp Input

  24. Fuzzification Turning a Crisp (Numerical) Value Into a Degree of Activation of a Fuzzy Set

  25. SMALL MED Fuzzy Inference -- Rule Base IF Height is MED AND Weight is LIGHT THEN Build is SMALL IF Height is SHORT AND Weight is MED THEN Build is MED max

  26. q yCOA =  i=1 yi C (yi) q i=1 C (yi) 2/3 1/3 11 3 4 10 1 2 5 6 7 8 9 yCOA = 1*0 + 2* 1/3 + 3* 2/3 + 4* 2/3 + 5* 2/3 + 6* 2/3 + 7* 2/3 + 8* 1/3 + 9* 1/3 + 10* 1/3 +11*0 yCOA = 5.642 0+ 1/3 + 2/3 + 2/3 + 2/3 + 2/3 + 2/3 + 1/3 + 1/3 + 1/3 +0 Center of Area Defuzzification

  27. Strategy-to-Task Operational Test Analysis

  28. Example Fuzzy-Logic CCoD Decision Hierarchy Provide correct results w/ needed precision Provide required services Provide appropriate set of functions Interact w/ other components Prevent information disclosure Protect info & data Only modifiable by authorized users Maintain performance Re-establish performance & recover data Understand if suitable / how used Non-creator usable

  29. Fuzzy Logic DSS – Test Data to Component Trust: Sample Fuzzy Sets

  30. Fuzzy Logic DSS – Test Data to Component Trust: Sample Rule Base (Functionality)

  31. Fuzzy Logic DSS – Test Data to Component Trust: Sample Results

  32. Fuzzy Logic DSS – Test Data to Component Trust: Sample Results

  33. Fuzzy Logic DSS – Component Trust to Composition Trust: Sample Fuzzy Sets

  34. Fuzzy Logic DSS – Component Trust to Composition Trust: Sample Rule Base

  35. Fuzzy Logic DSS – Component Trust to Composition Trust: Sample Results

  36. Conclusion • CCoD provides a means to rapidly develop agile information architectures • Warfighter needs trust in composed capability • Fuzzy logic provides easily-built decision support • Future research/work focus areas • Incorporate real-time decision-maker risk preferences • Refine metrics, definitize data sources, construct automated data collection/cataloging • Develop Web 2.0-like tool to operationalize

More Related