Behavioral Model (Rules and Rule Editor)\n The discussion above describes how SMEs can define and compile an information model, provide persistence through a database or XML flat files, and then generate instances of the model through automated GUIs. Instances come to the framework web service in XML messages and are then parsed, validated, persisted, and now ready for intelligent processing. So how do SMEs define what they want to have happen when instances of the model that they define are transmitted to the system? They define a Behavioral Model with executable rules that define the behavior of the system.\n Rules are defined in 2 parts: the conditional statement (when some condition occurs) and the Action that should happen if the condition is true. If the condition is true, the rule is said to “Fire”. The KAI tool suite provides a Rule Editor that allow SMEs to define rules that are evaluated when events occur within the system (a new “Glucose Observation” is received). There are 3 components of rule processing and execution:
Analytics Engine\n The KAI tool suite provides analytical tools that can be used by users (analysts) via the tool suite or the Rule Engine. For instance, the analyst might want to know the median or mean blood glucose reading taken in the morning over the past month and the variance that could be associated with an A1c. Rules can be defined to use statistical package plugin augmentations to the Analytics Engine, such as when a rule might use an expected value or risk probability in its condition.
The KAI Framework is comprised of system security, enterprise messaging, framework services, and other capabilities that are combined into an Enterprise Service Bus (ESB) where messages and events are received or generated, validated, persisted, and intelligently acted upon in a way that satisfies our user’s needs. Events enter the framework as web service transmissions or internal timer events and are acted upon as defined by rules and implemented in the rule engine. Events contain either model formatted messages or externally defined messages (such as HL7 messages or any other type of message). External messages are transmitted to a framework adaptor that translates the message into an internal model define message and places it on the bus. The framework is composed of Adaptors, Plugins, and Services and combined in a way that provides horizontal scalability, fault tolerance, and extensibility.\n Background: Technologies and principals used in the KAI tools and framework can be traced back to our seminal work on “data fusion” and “targeting” systems for the DoD and intelligence community. These systems were designed to detect “signals” from diverse data sources in the presence of uncertainty and noise. A foundational effort along these lines was performed by the development of a web-based surveillance and targeting system for the intelligence community which was designed to use real-time multi-source intelligence to rapidly identify battlefield targets and other possible threats. These threats and targets were then combined with ontological reasoning and rules to help accelerate the decision-making process performed by human intelligence analysts. We developed and deployed complex algorithms that were designed to perform multi-modal data interpretation processes. The reasoning and analysis tasks were directed at integrating or fusing “imperfect cues” contained in dissimilar ISR sensor data (ELINT, MSI, SAR) so as to establish, with a degree of probabilistic certainty, the presence, identity, and current/future position of a “target”. In a manner analogous to human sensory integration models, automated processes were designed that follow a natural data abstraction and association/correlation hierarchy to find “hidden” patterns or associations in large intelligence data stores. These tasks required the concurrent integration of numerous complex tools/algorithms (e.g. Max Likelihood/MHT, Bayesian accrual, Dempster-Shafer, Neural Networks, etc.) stemming from distinct technical disciplines: probability/estimation theory, image processing/pattern recognition, sensor-specific digital signal processing, vehicle dynamics and artificial intelligence.\n Another related effort was our performance on an application of similar technologies in support of mobile warfighters. In this instance, semantics and rules were used to evaluate sources of intelligence information. Ontologies and semantic reasoners were employed to deduce threat associations and distill meaningful reports from large volumes of raw data (e.g. tactical imagery, SIGINT reports) and transmit compressed warnings/advisories (“actionable intelligence”) to mobile war zone users via handheld communications and computing devices.\n Accomplishments: The KAI Tool Suite, Framework, and Technologies are continually refined and enhanced with new capabilities being added frequently. However, advanced capabilities are currently functional:\n 1. Model and ontological specification (Model Editor)
2. Model Translation and Compilation in Java is currently available