The E3SAT tool suite allows researchers to collect, integrate, and data mine medical records, environmental exposures, and deployment locations. This provides an encompassing data view of soldier health in the military healthcare system and enables studies of environmental, epidemiological, and etiological factors driving the system.
In the aftermath of Desert Shield and Desert Storm, researchers have struggled with explaining the array of serious health impairing symptoms that have become collectively known as Gulf War Syndrome. Approximately 30 percent of the 700,000 U.S. servicemen and servicewomen in the first Persian Gulf War have registered in the Gulf War Illness database complaining of these symptoms. A key stumbling block in researching Gulf War Syndrome is the absence of a means for integrating relevant data—soldier medical records, environmental health and surveillance data, deployment data—and discovering and analyzing to discover patterns and correlations between deployment exposures and soldier signs, symptoms, and potential causes.
The HENIOMAP™ technology uses advanced utility-theoretic models to provide a human enemy network influence operations map that incorporates cultural, social, and other factors using a simple knowledge acquisition process. The system assists influence operations (IO) planners in the evaluation and formation of influence operations.
KBSI, in a contract funded by the Office of Naval Research, is developing the hidden enemy network influence operations map (HENIOMAP™) capability, a decision-support tool for course of action (COA) design, analysis and selection in the counterinsurgency (COIN) domain. HENIOMAP™ provides middle-ware capabilities, assisting COA planners in gap analysis using forward (from COAs to commander’s objective) and backward (from commander’s objective to COAs) reasoning methods. The HENIOMAP™ COA ontology represents COAs, phases, activities, states, outcomes, measures-of-performance (MOP), and measures-of-effectiveness (MOE).
The FEAST technology uses an innovative methodology for dynamic scenario generation and adaption in simulation-based environments for mission operations training. The technology’s knowledge discovery and automated reasoning methods let you evolve scenario design knowledge over time.
KBSI began by establishing the requirements for a dynamic scenario generation for individual and team learning in simulation-based environments. This requirements building will utilize the Air Force’s Mission Essential Competencies (MECs)-based model as a means for enabling effective transformation from mission based training to competency based training in DMO training environments.
The XFMR solutions concept allows for interactive critical chain re-sequencing, constraint violation identification, and automated critical chain plan option generation. These capabilities are transforming Air Logistics Center (ALC) operations to warrior-centric, highly adaptive, and more efficient sustainment enterprise activities.
The Transformation in Maintenance and Repair (XFMR, or “transformer”) research effort identified the critical method and tool technology voids that must be addressed in transforming Air Logistics Center (ALC) operations to warrior-centric, highly adaptive, and more efficient sustainment enterprise activities. Central to addressing these voids is a set of key technologies that support a critical chain program management (CCPM) approach based on Goldratt’s Theory of Constraints (TOC). The XFMR solution concept includes a set of Critical Chain deconfliction tools that provide critical chain re-sequencing, constraint violation identification, and automated and optimized critical chain plan option generation.
ProPlan™ 21 provides technology for the rapid definition, creation, and editing of constraint based MRO plans, enabling advanced plan analysis and plan execution for depot management decision support across all production echelons.
Key to the responsiveness of Air Logistic Centers (ALC), the hub for maintenance, repair, and overhaul of Air Force aircraft, is planning. Responsiveness is a particularly acute need given the increased pace of Air Force operations worldwide, and ALCs are under pressure to reduce the number of aircraft on station at any given time, to speed maintenance, repair, and overhaul (MRO) turnaround, and to meet on-time delivery commitments despite significant variability in work content, component part reliability, and replacement part acquisition lead-time—all while continuing to maintain consistently high standards of quality.
The RISE initiative is focusing on identifying/defining best practices for simulation modeling and analysis support for work center planning and decision making at the Oklahoma City Air Logistics Center (OC-ALC). The RISE findings will demonstrate the use of simulation modeling best practices in moving OC-ALC to a best-practice state.
This initiative, a ProPlan™ 21 modification initiative at OC-ALC, is focusing on identifying/defining best practices in the application of simulation modeling and analysis support for work center planning activities and decision making. This initiative will demonstrate, using a current OC-ALC work center planning problem, the application of the discovered simulation modeling best practices and develop a recommended set of requirements and actions that will allow OC-ALC to move towards that best-practice state.
The PLANDROID™ technology is an automated asset employment and redeployment planning framework for world state information management, asset capabilities characterization, mission and task requirements planning, plan assessment, and decision support.
The advent of advanced sensors and remote control technologies has expanded the use of robotic elements and unmanned combat vehicles for a variety of roles and mission types. The increased use of robotic elements, however, also adds to the complexities involved in the planning and decision making regarding these asset deployments.
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.