MMKD is a configurable, dashboard driven knowledge discovery system that allows users without data mining expertise to perform cutting edge knowledge discovery. The technology helps the DoD Medical Logistics community meet the challenges of troop deployments in ever widening combat scenarios.
The Medical Materiel Knowledge Discoverer (MMKD) is a knowledge discovery system for the Department of Defense (DoD) Medical Logistics community that goes beyond simple data mining. The changing nature of military conflicts in the world favor an emphasis on the rapid deployment of troops in an ever-widening variety of scenarios and locales. These developments raise significant logistical challenges and risks for, among other military branches, the DoD medical community.
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 Faraday Chromate intitiative analyzed Electro Impedance Spectroscopy (EIS) data to study the effectiveness and life of particular coatings on accelerated corrosion environments.
KBSI has developed technology that performs data mining of maintenance data, estimates balance life, and delivers the right information at the correct level and appropriate time to the maintenance decision maker. This technology will support maintenance depots by providing a sound basis for decisions and judgments in the corrosion arena. This will enable the Department of Defense to replace the current body of subjective, disjointed, and anecdotal information about weapon system corrosion with credible information that is based on metrics and data, leading to substantial decreases in the maintenance costs associated with detecting, repairing, and tracking corrosive areas. In collaboration with Faraday Technologies Inc., one of the leading electrochemistry experts, we have been very productive in working toward this goal.
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.”
KBSI is researching and designing technology that allows users to rapidly build text analytics-based systems that support the research and analysis of large bodies of disparate, unstructured text sources.
The Text Analytics Situational Awareness Toolkit (T-SAT) will apply an advanced object model that allows users to combine multiple text analytics algorithms to create complex research and analysis scenarios. The technology will provide a user configurable GUI in which users can rapidly construct research and analysis systems that connect data sources to analytics-based algorithms. T-SAT’s text analytic-based algorithms can be applied to a wide range of source types–unstructured text from an online social network, text from an online chat room, or digitized textual reports taken from human intelligence sources (HUMINT)–eliminating the need for tailoring algorithms according to each target source.
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.
TEAMS is a decision support tool that uses simulation to help NASA’s Kennedy Space Center (KSC) model various activities associated with spaceport project operations, improving decision making in areas like space vehicle mix impact, launch rates, asset utilization, and spaceport life cycle costs.
NASA’s Kennedy Space Center (KSC) was experiencing problems from the numerous configurations and payload possibilities with their planned spaceport, and they lacked a tool to assist in the modeling and analysis of spaceport decisions. Not to mention, spaceport management was also in need of a method for capturing knowledge pertaining to spaceport operations. This dilemma resulted in an initiative entitled Toolkit for Enabling Adaptive Modeling and Simulation (TEAMS™), and a solution was consequently developed with KBSI’s expertise in methods and project management.
The RCMC™ methods and tools support the critical knowledge discovery, cost modeling, and maintenance cost projection needed by Reliability Centered Maintenance (RCM) decision makers. The methodology considers proposed maintenance actions on aggregate level metrics like engine availability, performance, and life cycle costs.
Current aircraft engine maintenance activities performed by the Air Force must account for both scheduled and unscheduled maintenance needs. These maintenance activities, whether scheduled or unscheduled, are characterized as following an on-condition maintenance (OCM) strategy: the maintenance work is performed to repair only what is broken or has already exceeded its time-on-wing (TOW) limits.
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.