The publish/subscribe paradigm of Message Oriented Middleware provides a loosely coupled communication model between distributed applications. Traditional publish/subscribe middleware uses keywords to match advertisements and subscriptions and does not support deep semantic matching. To this end, we designed and implemented a Semantic Message Oriented Middleware system to provide such capabilities for semantic description and matching. We adopted the DARPA Agent Markup Language and Ontology Inference Layer, a formal knowledge representation language for expressing sophisticated classifications and enabling automated inference, as the topic description language in our middleware system. A simple description logic inference system was implemented to handle the matching process between the subscriptions of subscribers and the advertisements of publishers. Moreover our middleware system also has a security architecture to support secure communication and user privilege control.
Process Query Systems (PQS) are a new kind of information retrieval technology in which user queries are expressed as process descriptions. The goal of a PQS is to detect the processes using a datastream or database of events that are correlated with the processes' states. This is in contrast with most traditional database query processing, information retrieval systems and web search engines in which user queries are typically formulated as Boolean expressions. In this paper, we outline the main features of Process Query Systems and the technical challenges that process detection entails. Furthermore, we describe several importance application areas that can benefit from PQS technology. Our working prototype of a PQS, called TRAFEN (for TRAcking and Fusion ENgine) is described as well.
Proc. SPIE. 5403, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense III
KEYWORDS: Mathematical modeling, Defense and security, Homeland security, Detection and tracking algorithms, Data modeling, Sensors, Data processing, Environmental sensing, Process modeling, Fuzzy logic
Many defense and security applications involve the detection of a dynamic process. A process model describes the state transitions of an object, which evolves in time according to specific know laws. Given a process model, the process detection problem is to identify the existence of such a process in large amount of observation data. While Hidden Markov Models (HMMs) are widely used to characterize dynamic processes, it's usually hard to estimate those state transition and emission probabilities precisely in practice, especially if we don't have sufficient training data. An inaccurate process model could lead to high false alarm and misdetection rates and the inference result could be misleading in the decision-making process. To this end, we propose nonparametric weak models derived from HMMs to characterize dynamic processes. A weak model doesn't need the strong requirement for probability specification as in HMMs. In this paper, we analyze the properties of such weak models and propose recursive algorithms to compute the hypotheses of the hidden state sequence and the size of the hypothesis set. Further we analyze how to control the size of the hypothesis set by increasing the number of sensors to tune the structure of the emission matrix.
Recent advances in wireless communication and microelectronics have enabled the development of low-cost sensor devices leading to interest in large-scale sensor networks for military applications. Sensor networks consist of large numbers of networked sensors that can be dynamically deployed and used for tactical situational awareness. One critical challenge is how to dynamically integrate these sensor networks with information fusion processes to support real-time sensing, exploitation and decision-making in a rich tactical environment. In this paper, we describe our work on an extensible prototype to address the challenge. The prototype and its constituent technologies provide a proof-of-concept that demonstrates several fundamental new approaches for implementing next generation battlefield information systems. Many cutting-edge technologies are used to implement this system, including semantic web, web services, peer-to-peer network and content-based routing. This prototype system is able to dynamically integrate various distributed sensors and multi-level information fusion services into new applications and run them across a distributed network to support different mission goals. Agent technology plays a role in two fundamental ways: resources are described, located and tasked using semantic descriptions based on ontologies and semantic services; tracking, fusion and decision-making logic is implemented using agent objects and semantic descriptions as well.
National-scale critical infrastructure protection depends on many processes: intelligence gathering, analysis, interdiction, detection, response and recovery, to name a few. These processes are typically carried out by different individuals, agencies and industry sectors. Many new threats to national infrastructure are arising from the complex couplings that exist between advanced information technologies (telecommunications and internet), physical components (utilities), human services (health, law enforcement, emergency management) and commerce (financial services, logistics). Those threats arise and evolve at a rate governed by human intelligence and innovation, on `internet time' so to speak. The processes for infrastructure protection must operate on the same time scale to be effective. To achieve this, a new approach to integrating, coordinating and managing infrastructure protection must be deployed. To this end, we have designed an underlying web-like architecture that will serve as a platform for the decentralized monitoring and management of national critical infrastructures.