The FAMOS OSAI Method™ technology is a system for developing and executing distributed military simulation applications. The technology reduces the time and effort needed to develop and deploy distributed simulation applications and helps establish knowledge sharing and semantic integration among modeling and simulation applications.
In this initiative, KBSI collaborated with the Air Force to build a system for the development and execution of distributed military simulation applications. The Framework for Adaptive Modeling and Ontology-driven Simulation (FAMOS OSAI Method™) reduces the time and effort required for developing and deploying distributed simulation applications and lays the groundwork for knowledge sharing, communication, and semantic integration among modeling and simulation applications.
The Military Health Data Mining Algorithms Library (M-HDML) applies data mining technologies and techniques to the storage and retrieval of patient data, helping doctors in the DoD’s vast medical health system (MHS) to more accurately diagnose their patients.
In our increasingly data-centric world, data mining technologies are being enlisted for a wide variety of uses: from retail sales, to video gaming, to, most recently, combating terrorism. The staggering amount of data has improved the stock of intelligent data mining systems and knowledge discovery techniques that help users extract meaningful information from enormous data sets. In the industrial arena, more and more organizations are investing in data mining techniques (software and hardware) as a means for gaining profitable business insights from their huge central transactional databases. The Gartner group estimates that the use of data mining applications will increase from less than 5% currently to 80% over the next decade.*
KBSI developed a library of models for post deployment software maintenance (PDSM) workload estimation along with a comprehensive methodology for PDSM concept analysis: i.e., how to think about supporting software-intensive systems, and how to formulate and analyze trade-offs in the design space.
To meet the National Defense Strategy of global engagement, rapid mobility, a small logistics footprint, and inherent reliability, the U.S. Army’s weapon systems program office wanted a means for more effective post-deployment software maintenance. Software is now recognized as the highest risk system component in virtually every major defense acquisition, and the role of software in the overall weapon systems acquisition enterprise continues to grow. For example, when the C-17 development program began in 1985, the government planned for the development of four subsystems with about 164,000 lines of code. By 1990, this number had increased to 56 subsystems and about 1,356,000 lines of code, including approximately 643,000 newly developed lines-of-code.
Superiority Through Better Decision Making (SUPERCISION™) uses data mining, Bayesian nets, situation theory, and commercial gaming technologies to support the assessment and improvement of decision making skills in training systems. SUPERCISION™ methods and tools improve scenario-based training and reduce the time and effort needed to create tailored training scenarios.
A frequently quoted maxim from the Army’s 100-5 Operations Field Manual states: “Effective decision making combines judgment with information as an element of combat power: it requires knowing if to decide, when to decide, and what to decide.”
Aviation maintenance, repair, and overhaul (MRO) work involves more than simply turning wrenches or stripping and repainting. Before MRO work can begin on an aircraft, large numbers of MRO assets both big and small, have to be in the right place, at the right time and must be moved in coordination with the aircraft they’ll be servicing. For large-scale MRO facilities like the Air Force’s Air Logistics Centers, the inability to track the location of aircraft and ground support equipment (GSE) makes it difficult to quickly locate, stage, deploy, and coordinate aircraft, GSE, and critical resource movements among the busy ramps and hangars. This inability causes persistent schedule delays, increased MRO flow times, and mounting aviation repair costs at a time when most military organizations are looking for better resource efficiency and utilizations. Working with the Aircraft Maintenance Operations Control Center (AMOCC) at Tinker AFB, KBSI customized its generic asset visualization technology, providing a knowledge based visualization and management application used to track aircraft and GSE across the air logistics center.
The ISMF technology includes a robust method and set of support tools for automating the generation and management of training scenarios tailored to training goals and student performance. Student performance is identified using Bayesian models and evaluation scripts, the visualization and annotation of simulation trace data, and data analysis and data mining techniques.
In the NAVAIR-sponsored Intelligent Scenario Management Framework (ISMF) small-business innovation research (SBIR) initiative, KBSI developed a robust method and a set of support tools for automating the generation and management of training scenarios in response to training goals and student performance. Specifically, student deficiencies are identified through the use of Bayesian models and evaluation scripts, the visualization and annotation of simulation trace data, and through data analysis and data mining techniques.