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.
AARDIS researched advanced electronic warfare (EW) training methods and tools that reduce the cognitive workload of instructors, improve training effectiveness, accelerate improvements in student performance, and reduce training costs.
Pilot training at the U.S. Air Force (USAF) faces two fundamental challenges: rising training costs and restrictions and the changing nature of military conflicts around the globe. According to a recent RAND study, the costs of USAF initial skills training (IST) have risen to approximately $750 million per year. In addition, the opportunities for live training exercises, because of limits on training airspace, rising fuel costs and heightened security, have been diminishing.
KBSI is researching the application of advanced semantic technologies to layered sensing networks (LSN) and the use of data mining technologies for discovering and extracting information from large data stores. The research also investigates the automatic generation of ontologies from unprocessed data sources.
The increased use of networked sensor platforms to perform various command and control, surveillance, and reconnaissance missions has resulted in a corresponding increase in the quantity (and variety) of data that must be organized and analyzed as a precursor to extracting useful information. The purpose of this initiative with Ball Aerospace and Technologies Corporation (BATC) and Air Force Research Laboratories, Anti Tamper and Software Protection (AFRL/RYT) is to research various methods and technologies for extracting valuable information from vast quantities of data.
ONSITE uses an innovative approach to natural language processing (NLP) in enabling state of the art natural language processing rates in support of military tactical operations. The initiative also investigated ontologies for higher processing throughput and the improved semantic resolution of extracted information.
KBSI has been awarded funding from the Defense Advanced Research Projects Agency (DARPA) to research, design, and demonstrate enabling technology for Open-Source Information Tactical Exploitation (ONSITE). The ONSITE initiative applies an innovative approach to natural language processing (NLP) that aims at achieving state of the art processing rates for the understanding of natural language in support of military tactical operations. DARPA’s goal is to improve natural language processing speed and efficiency despite constrained computational resources, accelerated operational timelines, and specific intelligence objectives. Improving the speed and efficiency of NLP allows war fighters to more quickly process data in the bid for tactical advantage.
The ETHOS™ method is used for securing trust based on the information exchanged between the different nodes of a sensor network. The initiative investigated various trust metrics and algorithms to define a set of trust metrics that use information entropy as the basis for calculating the reputation of particular nodes.
In this U.S. Air Force Research Laboratory (AFRL) – Wright-Patterson AFB funded effort KBSI developed an Entropy-Trust-Homology Operational Security (ETHOS™) method for securing trust that is based on the information exchanged between the different nodes of a sensor network. Trust entropy metrics are created based on both the patterns of information entropy flow between nodes and on usage behavior. Usage behavior includes both user behavior (user monitoring) as well as CPU behavior (process monitoring). Each node in the system creates a set of trust metrics that corresponds to the set of directly observable neighboring nodes. A trust metric that reflects the reputation of a particular node could then be calculated by the direct and indirect querying of nodes in the network.
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.”