KBSI is developing an advanced component-based Data Display Markup Language (DDML), an XML-based neutral format also developed by KBSI, as the inter-lingua in translating the data display languages supported by different vendors.
Data display is a critical component for T&E environments in aircraft, space, and energy systems. Because telemetry functions associated with these systems produce too much data for a single person to comprehend, data display–customizable display objects, including strip charts, bar charts, vertical meters, round gauges, cross plots, tabular displays, orientation displays, and bit maps–is critical in presenting this information in an understandable format.
KBSI defined a neutral format for capturing, organizing, and sharing process-related knowledge that is based on the Integrated Definition family of Methods and the eXtended Markup Language (XML). Using this standard, KBSI developed an advanced authoring tool-kit for situation-based, process-centric computer-based training systems.
Because processes are central to the operation of all aspects of an organization, most decision-making applications and implementation solutions deal with the capture, specification, representation, and manipulation of process-related information. This information may in turn be used to support business process re-engineering, workflow systems development, and a wide range of training needs. Since these application domains require the same process information, process knowledge capture for one application should easily support other purposes. Yet, sharing and reusing process knowledge across applications remains an unrealized vision. The need for standards to capture, represent, share, and display process-related information is particularly important in training applications. Process-centered training provides students with the most reliable approach for understanding, internalizing, and applying new concepts.
The MAES initiative designed and developed technology that collects data from multiple sources, analyzes the data for trends and patterns, and, using handheld devices, deliver context sensitive, location based alerts and advice to users on healthcare, disease, safety, and environmental related issues.
Humanitarian relief, disaster recovery, nation building: these missions are becoming more significant for both military and non-governmental organizations every day. Health awareness and a proactive approach to health outcomes are critical to these missions, but, in many developing countries, even basic reporting of the health status of affected populations can be a daunting task. Communications infrastructures are minimal, transportation may be difficult, time is short, and the focus is often on addressing basic human survival needs. Establishing baselines for epidemiological awareness may be too time consuming and difficult to be a priority.
The IIWARS system uses text mining technologies and information fusion to mine data from multiple, distributed text sources, extract features and “indicators” of emerging threats, and improve the DoD’s terrorist threat assessment capabilities.
While analysts and strategists are well versed in tracking threats to conventional military targets, the new asymmetric threats posed by terrorist organizations, as 9-11 has made devastatingly clear, are much more difficult to anticipate. Threats to military targets have traditionally required capabilities that are both expensive and take a long time to develop–activities that satellites and other reconnaissance are more likely to notice.
The KDWizard is an adaptive framework for knowledge discovery and provides mechanisms for generating knowledge-discovery applications beginning with end-user specifications of decision objectives and sets of data sources.
The KDWizard initiative focused on designing, developing, and validating an adaptive framework for knowledge discovery. This framework provides mechanisms for generating knowledge-discovery applications beginning with end-user specifications of decision objectives and sets of data sources. This technology also provides users with a larger and more focused selection of decision alternatives and consequently aids them in realizing a more efficient and successful decision making process.
The Donor Profile Database (DPD) captures and maintains information about blood donor identity, deferrals, health and travel history, etc. and make this information widely accessible to a network of blood centers. The DPD allows blood centers to maintain the consistency and integrity of blood product data and maintain global connectivity.
The Federal Drug Administration’s (FDA) continuing efforts to ensure the safety of our nation’s blood supply has highlighted the need for a comprehensive and cost effective method for performing donor screening, archiving donor histories (in a widely accessible format), and validating blood center compliance with FDA and other government and international regulations. While blood centers are required by the FDA to perform donor screening and to keep records of donor histories, the method for performing these safeguards varies considerably from center to center, making it difficult to oversee donor and blood product safety compliance at the national level.
The BIOWARS technology is an adaptive system for discovering disease outbreaks and impending bio terrorism attacks. BIOWARS uses syndromic surveillance to find symptomatic data patterns and applies Bayesian networks in collecting and archiving these patterns.
An important challenge faced by intelligence analysts and the intelligence community in our post 9/11 world is to gather, piece together, and correctly interpret vast amounts of intelligence data–data that may signal an impending attack or that may help limit the severity of an attack. As a Defense Science Board study of transnational threats noted, the “making of connections between otherwise meaningless bits of information is at the core of (transnational) threat analysis.”
TINCOPS is a knowledge-based decision support system that manages and deploys knowledge for the U.S. military’s complex combat decision support applications and for analyzing and evaluating logistics plans.
The result of mission planning is a strategy for accomplishing the intended objectives that reflects decisions on the best methods and course of actions to follow. The mission planning process is knowledge intensive and involves a number of factors that must be considered including uncertainties in the intelligence collected, enemy response, changes in logistics needs or routes, etc. In addition, once the mission is active, changes in the battlespace can occur rapidly and commanders must receive accurate and current combat situational information in order to craft the most effective response and change to the original strategy.
HDWizard™ is a hybrid decision support toolkit that provides agent-based decision support for the automated generation of information from disparate and distributed data to support user-defined decision support goals.
Government and industry lack robust, hybridized approaches and methods for applying common sense reasoning techniques in decision support and knowledge management systems. KBSI’s Hybrid Discovery Wizard (HDWizard™) project focused on developing a generic HDWizard™ toolkit that includes data mining, fusion, and inference/reasoning methods.
ODSS is a system for creating and applying simulation modeling in depot management decision support. ODSS uses a hybrid discrete-event/rule-based simulation engine, providing support for optimizing plans, schedules, situation response, and process designs.
The goal of this initiative was to design, build, and deploy an On Demand Simulation Support (ODSS) system prototype within the depot-MRO domain. The ODSS prototype (referred to as the Virtual Planning Wizard – VPW), developed and tested using shared facility data from the paint and strip area at the Oklahoma City Air Logistics Center (OC-ALC), has demonstrated the effectiveness of the ODSS technology for the rapid creation and application of simulation modeling for depot management decision support.